WorldWideScience

Sample records for eeg brain mapping

  1. Modeling epileptic brain states using EEG spectral analysis and topographic mapping.

    Science.gov (United States)

    Direito, Bruno; Teixeira, César; Ribeiro, Bernardete; Castelo-Branco, Miguel; Sales, Francisco; Dourado, António

    2012-09-30

    Changes in the spatio-temporal behavior of the brain electrical activity are believed to be associated to epileptic brain states. We propose a novel methodology to identify the different states of the epileptic brain, based on the topographic mapping of the time varying relative power of delta, theta, alpha, beta and gamma frequency sub-bands, estimated from EEG. Using normalized-cuts segmentation algorithm, points of interest are identified in the topographic mappings and their trajectories over time are used for finding out relations with epileptogenic propagations in the brain. These trajectories are used to train a Hidden Markov Model (HMM), which models the different epileptic brain states and the transition among them. Applied to 10 patients suffering from focal seizures, with a total of 30 seizures over 497.3h of data, the methodology shows good results (an average point-by-point accuracy of 89.31%) for the identification of the four brain states--interictal, preictal, ictal and postictal. The results suggest that the spatio-temporal dynamics captured by the proposed methodology are related to the epileptic brain states and transitions involved in focal seizures. Copyright © 2012 Elsevier B.V. All rights reserved.

  2. An Intracranial Electroencephalography (iEEG Brain Function Mapping Tool with an Application to Epilepsy Surgery Evaluation

    Directory of Open Access Journals (Sweden)

    Yinghua eWang

    2016-04-01

    Full Text Available Object: Before epilepsy surgeries, intracranial electroencephalography (iEEG is often employed in function mapping and epileptogenic foci localization. Although the implanted electrodes provide crucial information for epileptogenic zone resection, a convenient clinical tool for electrode position registration and brain function mapping visualization is still lacking. In this study, we developed a Brain Function Mapping (BFM Tool, which facilitates electrode position registration and brain function mapping visualization, with an application to epilepsy surgeries.Methods: The BFM Tool mainly utilizes electrode location registration and function mapping based on pre-defined brain models from other software. In addition, the electrode node and mapping properties, such as the node size/color, edge color / thickness, mapping method, can be adjusted easily using the setting panel. Moreover, users may manually import / export location and connectivity data to generate figures for further application. The role of this software is demonstrated by a clinical study of language area localization.Results: The BFM Tool helps clinical doctors and researchers visualize implanted electrodes and brain functions in an easy, quick and flexible manner.Conclusions: Our tool provides convenient electrode registration, easy brain function visualization, and has good performance. It is clinical-oriented and is easy to deploy and use. The BFM tool is suitable for epilepsy and other clinical iEEG applications.

  3. STUDI AWAL: PENGARUH GAME KEKERASAN TERHADAP AKTIVITAS OTAK ANAK MELALUI PEMETAAN SINYAL OTAK (BRAIN MAPPING MENGGUNAKAN WIRELESS EEG

    Directory of Open Access Journals (Sweden)

    Nita Handayani

    2017-06-01

    Full Text Available Brain mapping adalah pemetaan aktivitas kelistrikan otak untuk mempelajari fungsional otak manusia. Pada studi ini, brain mapping digunakan untuk mempelajari pengaruh game kekerasan terhadap aktivitas fungsional otak anak dengan menggunakan wireless EEG (electroencephalography berupa Emotiv Epoc 14-channel. Subjek penelitian ini adalah anak-anak pecandu game kekerasan (10 anak dengan rentang usia antara 12-15 tahun. Aktivitas otak pada saat bermain game akan dibandingkan dengan kondisi rileks. Waktu perekaman EEG selama 42 menit untuk setiap subjek. Dari hasil analisis spektral daya menggunakan periodogram Welch menunjukkan bahwa pada saat bermain game, frekuensi gelombang delta dan theta meningkat terutama pada area frontal (F7, F3, FC5, FC6, F4, F8, dan AF4. Spektral daya gelombang alpha mengalami penurunan sedangkan gelombang beta mengalami peningkatan pada saat bermain game. Hal ini mengindikasikan bahwa anak mengalami beban mental dan berada pada kondisi stres pada saat bermain game kekerasan.

  4. Abnormal pain processing in chronic tension-type headache: a high-density EEG brain mapping study

    DEFF Research Database (Denmark)

    Buchgreitz, L.; Egsgaard, L.L.; Jensen, R.

    2008-01-01

    Central sensitization caused by prolonged nociceptive input from muscles is considered to play an important role for chronification of tension-type headache. In the present study we used a new high-density EEG brain mapping technique to investigate spatiotemporal aspects of brain activity...... in response to muscle pain in 19 patients with chronic tension-type headache (CTTH) and 19 healthy, age- and sex-matched controls. Intramuscular electrical stimuli (single and train of five pulses delivered at 2 Hz) were applied to the trapezius muscle and somatosensory evoked potentials were recorded...... with 128-channel EEG both in- and outside a condition with induced tonic neck/shoulder muscle pain (glutamate injection into the trapezius muscle). Significant reduction in magnitude during and after induced tonic muscle pain was found in controls at the P200 dipole in response to both the first (baseline...

  5. Comparative neuroimaging in children with cerebral palsy using fMRI and a novel EEG-based brain mapping during a motor task--a preliminary investigation.

    Science.gov (United States)

    Lee, Jae Jin; Lee, Dong Ryul; Shin, Yoon Kyum; Lee, Nam Gi; Han, Bong S; You, Sung Joshua Hyun

    2013-01-01

    The purpose of this study was to compare topographical maps using a novel EEG-based brain mapping system with fMRI in normal and children with cerebral palsy (CP) during a grasping motor task. A normal child (mean ± SD = 13 ± 0 yrs) and four children with CP (mean ± SD = 10.25 ± 2.86 yrs) were recruited from a local community school and medical center. A novel EEG-based brain mapping system with 30 scalp sites (an extension of the 10-20 system) and a 3T MR scanner were used to observe cortical activation patterns during a grasping motor task. Descriptive analysis. In the EEG brain mapping data, the sensorimotor cortex (SMC) and inferior parietal cortex (IPC) were activated in all of the children. The children with CP showed additional activation areas in the premotor cortex (PMC), superior parietal cortex (SPC), and prefrontal cortex (PFC). In the fMRI brain mapping data, SMC activation was observed in all of the children, and the children with CP showed additional activation areas in the PMC and primary somatosensory cortex (PSC). The EEG-based topographical maps were equivalent to the maps obtained from fMRI during the grasping motor task. The results indicate that our novel EEG-based brain mapping system is useful for probing cortical activation patterns in normal children and children with CP.

  6. New perspectives in EEG/MEG brain mapping and PET/fMRI neuroimaging of human pain.

    Science.gov (United States)

    Chen, A C

    2001-10-01

    With the maturation of EEG/MEG brain mapping and PET/fMRI neuroimaging in the 1990s, greater understanding of pain processing in the brain now elucidates and may even challenge the classical theory of pain mechanisms. This review scans across the cultural diversity of pain expression and modulation in man. It outlines the difficulties in defining and studying human pain. It then focuses on methods of studying the brain in experimental and clinical pain, the cohesive results of brain mapping and neuroimaging of noxious perception, the implication of pain research in understanding human consciousness and the relevance to clinical care as well as to the basic science of human psychophysiology. Non-invasive brain studies in man start to unveil the age-old puzzles of pain-illusion, hypnosis and placebo in pain modulation. The neurophysiological and neurohemodynamic brain measures of experimental pain can now largely satisfy the psychophysiologist's dream, unimaginable only a few years ago, of modelling the body-brain, brain-mind, mind-matter duality in an inter-linking 3-P triad: physics (stimulus energy); physiology (brain activities); and psyche (perception). For neuropsychophysiology greater challenges lie ahead: (a) how to integrate a cohesive theory of human pain in the brain; (b) what levels of analyses are necessary and sufficient; (c) what constitutes the structural organisation of the pain matrix; (d) what are the modes of processing among and across the sites of these structures; and (e) how can neural computation of these processes in the brain be carried out? We may envision that modular identification and delineation of the arousal-attention, emotion-motivation and perception-cognition neural networks of pain processing in the brain will also lead to deeper understanding of the human mind. Two foreseeable impacts on clinical sciences and basic theories from brain mapping/neuroimaging are the plausible central origin in persistent pain and integration of

  7. Synergetic fMRI-EEG brain mapping in alpha-rhythm voluntary control mode.

    Science.gov (United States)

    Shtark, M B; Verevkin, E G; Kozlova, L I; Mazhirina, K G; Pokrovskii, M A; Petrovskii, E D; Savelov, A A; Starostin, A S; Yarosh, S V

    2015-03-01

    For the first time in neurobiology-related issues, the synergistic spatial dynamics of EEG and fMRI (BOLD phenomenon) was studied during cognitive alpha biofeedback training in the operant conditioning mode (acoustic reinforcement of alpha-rhythm development and stability). Significant changes in alpha-rhythm intensity were found in T6 electrode area (Brodmann area 37). Brodmann areas related to solving alpha-training tasks and maximally involved in the formation of new neuronal network were middle and superior temporal gyri (areas 21, 22, and 37), fusiform gyrus, inferior frontal gyrus (areas 4, 6, and 46), anterior cingulate gyrus (areas 23 and 24), cuneus, and precuneus (area 7). Wide involvement of Brodmann areas is determined by psychological architecture of alpha-rhythm generating system control that includes complex cognitive activities: decision making, retrieval of long-term memory, evaluation of the reward and control efficiency during alpha-EEG biofeedback.

  8. EpiTools, A software suite for presurgical brain mapping in epilepsy: Intracerebral EEG.

    Science.gov (United States)

    Medina Villalon, S; Paz, R; Roehri, N; Lagarde, S; Pizzo, F; Colombet, B; Bartolomei, F; Carron, R; Bénar, C-G

    2018-03-29

    In pharmacoresistant epilepsy, exploration with depth electrodes can be needed to precisely define the epileptogenic zone. Accurate location of these electrodes is thus essential for the interpretation of Stereotaxic EEG (SEEG) signals. As SEEG analysis increasingly relies on signal processing, it is crucial to make a link between these results and patient's anatomy. Our aims were thus to develop a suite of software tools, called "EpiTools", able to i) precisely and automatically localize the position of each SEEG contact and ii) display the results of signal analysis in each patient's anatomy. The first tool, GARDEL (GUI for Automatic Registration and Depth Electrode Localization), is able to automatically localize SEEG contacts and to label each contact according to a pre-specified nomenclature (for instance that of FreeSurfer or MarsAtlas). The second tool, 3Dviewer, enables to visualize in the 3D anatomy of the patient the origin of signal processing results such as rate of biomarkers, connectivity graphs or Epileptogenicity Index. GARDEL was validated in 30 patients by clinicians and proved to be highly reliable to determine within the patient's individual anatomy the actual location of contacts. GARDEL is a fully automatic electrode localization tool needing limited user interaction (only for electrode naming or contact correction). The 3Dviewer is able to read signal processing results and to display them in link with patient's anatomy. EpiTools can help speeding up the interpretation of SEEG data and improving its precision. Copyright © 2018 Elsevier B.V. All rights reserved.

  9. Deep Neural Architectures for Mapping Scalp to Intracranial EEG.

    Science.gov (United States)

    Antoniades, Andreas; Spyrou, Loukianos; Martin-Lopez, David; Valentin, Antonio; Alarcon, Gonzalo; Sanei, Saeid; Took, Clive Cheong

    2018-03-19

    Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly mapping scalp to iEEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp-intracranial recording to establish a novel methodology for detecting the epileptic discharges from the sEEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real-world clinical application, these pseudo-iEEGs are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of iEEG and the limitations of sEEG, we have achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping.

  10. Topographic Brain Mapping: A Window on Brain Function?

    Science.gov (United States)

    Karniski, Walt M.

    1989-01-01

    The article reviews the method of topographic mapping of the brain's electrical activity. Multiple electroencephalogram (EEG) electrodes and computerized analysis of the EEG signal are used to generate maps of frequency and voltage (evoked potential). This relatively new technique holds promise in the evaluation of children with behavioral and…

  11. Computerized EEG and brain imaging studies in untreated schizophrenic patients

    International Nuclear Information System (INIS)

    Miyauchi, Toshiro; Kishimoto, Hideji; Hagimoto, Hiroshi; Fujita, Haruhiro; Tanaka, Kenkichi

    1993-01-01

    We undertook routine EEG, Z-map, CT and PET scans in seven acute untreated schizophrenics. Routine EEGs showed slower activity in only one case. However, the Z-map showed slower activity in all the cases. CT demonstrated brain atrophy in three of the cases, and PET revealed hypofrontality in two, right hypoparietality in four, and both conditions in one case. There was no relation between CT and PET or the Z-map. However, a significant increase in alpha 1 activity was demonstrated on the Z-map in cases who were found to be the parietal type on PET; this was not conspicuous in the frontal type on PET. Moreover, in three of the patients, the Z-map findings were similar to the lesion indicated on PET. (author)

  12. Baby Brain Map

    Science.gov (United States)

    ... a Member Home Resources & Services Professional Resource Baby Brain Map Mar 17, 2016 The Brain Map was adapted in 2006 by ZERO TO ... supports Adobe Flash Player. To view the Baby Brain Map, please visit this page on a browser ...

  13. EEG Based Inference of Spatio-Temporal Brain Dynamics

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese

    Electroencephalography (EEG) provides a measure of brain activity and has improved our understanding of the brain immensely. However, there is still much to be learned and the full potential of EEG is yet to be realized. In this thesis we suggest to improve the information gain of EEG using three...... different approaches; 1) by recovery of the EEG sources, 2) by representing and inferring the propagation path of EEG sources, and 3) by combining EEG with functional magnetic resonance imaging (fMRI). The common goal of the methods, and thus of this thesis, is to improve the spatial dimension of EEG...... recovery ability. The forward problem describes the propagation of neuronal activity in the brain to the EEG electrodes on the scalp. The geometry and conductivity of the head layers are normally required to model this path. We propose a framework for inferring forward models which is based on the EEG...

  14. EEG

    Science.gov (United States)

    ... brain dead. EEG cannot be used to measure intelligence. Normal Results Brain electrical activity has a certain ... 2018, A.D.A.M., Inc. Duplication for commercial use must be authorized in writing by ADAM ...

  15. Wearable ear EEG for brain interfacing

    Science.gov (United States)

    Schroeder, Eric D.; Walker, Nicholas; Danko, Amanda S.

    2017-02-01

    Brain-computer interfaces (BCIs) measuring electrical activity via electroencephalogram (EEG) have evolved beyond clinical applications to become wireless consumer products. Typically marketed for meditation and neu- rotherapy, these devices are limited in scope and currently too obtrusive to be a ubiquitous wearable. Stemming from recent advancements made in hearing aid technology, wearables have been shrinking to the point that the necessary sensors, circuitry, and batteries can be fit into a small in-ear wearable device. In this work, an ear-EEG device is created with a novel system for artifact removal and signal interpretation. The small, compact, cost-effective, and discreet device is demonstrated against existing consumer electronics in this space for its signal quality, comfort, and usability. A custom mobile application is developed to process raw EEG from each device and display interpreted data to the user. Artifact removal and signal classification is accomplished via a combination of support matrix machines (SMMs) and soft thresholding of relevant statistical properties.

  16. Data-driven forward model inference for EEG brain imaging

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Hauberg, Søren; Hansen, Lars Kai

    2016-01-01

    Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain......-of-concept study, we show that, even when anatomical knowledge is unavailable, a suitable forward model can be estimated directly from the EEG. We propose a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models....... Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging...

  17. Use Case Analysis: The Ambulatory EEG in Navy Medicine for Traumatic Brain Injuries

    Science.gov (United States)

    2016-12-01

    science of binaural beats . Retrieved from http://binauralbrains.com/the-science-of- binaural - beats / Biosignal. (2016). MicroEEG. Retrieved from http...Cap. Source: Binaural Brains (n.d.). ....................................4  Figure 3.  EEG Machine. Source: Refine Medical Technology (n.d...EEG. Figures 2, 3, and 4 display images of a standard EEG cap, EEG machine, and an EEG recording. Figure 2. Standard EEG Cap. Source: Binaural Brains

  18. EEG

    African Journals Online (AJOL)

    2017-09-03

    Sep 3, 2017 ... However, very few studies have examined the use of EEG in developing countries, including Ni- ... of evoked potentials from brain neurons, referred to as .... Percentage. Gender. Male. 89. 62.7. Female. 53. 37.3. Age. 0-10. 59.

  19. Model-based analysis and optimization of the mapping of cortical sources in the spontaneous scalp EEG

    NARCIS (Netherlands)

    Sazonov, A.; Bergmans, J.W.M.; Cluitmans, P.J.M.; Griep, P.A.M.; Arends, J.B.A.M.; Boon, P.A.J.M.

    2007-01-01

    The mapping of brain sources into the scalp electroencephalogram (EEG) depends on volume conduction properties of the head and on an electrode montage involving a reference. Mathematically, this source mapping (SM) is fully determined by an observation function (OF) matrix. This paper analyses the

  20. Correlations of CT and EEG findings in brain affections

    International Nuclear Information System (INIS)

    Roth, B.; Nevsimalova, S.; Kvicala, V.

    1984-01-01

    The results were compared of electroencephalography (EEG) and computerized tomography (CT) examinations of 250 patients with different brain affections. In intracranial expansive processes the pre-operative CT findings were positive in 100% cases, the EEG findings in 89.7% of cases. In severe traumatic affections the EEG and CT findings were positive in all cases, in mild injuries and post-traumatic conditions the EEG findings were more frequently positive than the CT. In focal and diffuse vascular affections the EEG and CT findings were consistent, in transitory ischemic conditions the EEG findings were more frequently positive. In inflammatory cerebral affections and in paroxymal diseases the EEG findings were positive more frequently than the CT. The same applies for demyelinating and degenerative affections. Findings of other authors were confirmed to the effect that CT very reliably reveals morphological changes in cerebral tissue while EEG records the functional state of the central nervous system and its changes. The two methods are complementary. (author)

  1. Brain Functional Connectivity in MS: An EEG-NIRS Study

    Science.gov (United States)

    2015-10-01

    1 AWARD NUMBER: W81XWH-14-1-0582 TITLE: Brain Functional Connectivity in MS: An EEG -NIRS Study PRINCIPAL INVESTIGATOR: Heather Wishart...Functional Connectivity in MS: An EEG -NIRS Study 5b. GRANT NUMBER W81XWH-14-1-0582 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER Heather...electrical ( EEG ) and blood volume and blood oxygen-based (NIRS and fMRI) signals, and to use the results to help optimize blood oxygen level

  2. Developmental changes in brain connectivity assessed using the sleep EEG.

    OpenAIRE

    Tarokh L; Carskadon M A; Achermann P

    2010-01-01

    Adolescence represents a time of significant cortical restructuring. Current theories posit that during this period connections between frequently utilized neural networks are strengthened while underutilized synaptic connections are discarded. The aim of the present study was to examine the developmental evolution of connectivity between brain regions using the sleep EEG. All night sleep EEG recordings in two longitudinal cohorts (children and teens) followed at 1.5 3 year intervals and one ...

  3. Brain Oscillations in Sport: Toward EEG Biomarkers of Performance

    OpenAIRE

    Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

    2016-01-01

    Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical a...

  4. Brain oscillations in sport: toward EEG biomakers of performance

    OpenAIRE

    Guy eCheron; Guy eCheron; Geraldine ePetit; Julian eCheron; Axelle eLeroy; Axelle eLeroy; Ana Maria Cebolla; Carlos eCevallos; Mathieu ePetieau; David eZarka; Thomas eHoellinger; Anne-Marie eClarinval; Bernard eDan; Bernard eDan

    2016-01-01

    Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The noninvasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical ap...

  5. Mapping the brain

    International Nuclear Information System (INIS)

    Begley, S.; Wright, L.; Church, V.; Hager, M.

    1992-01-01

    With powerful new technologies such as positron tomography and superconducting quantum interference device that peer through the skull and see the brain at work, neuroscientists seek the wellsprings of thoughts and emotions, the genesis of intelligence and language. A functional map of the brain is thus obtained and its challenge is to move beyond brain structure to create a detailed diagram of which part do what. For that the brain's cartographers rely on a variety of technologies such as positron tomography and superconducting quantum interference devices. Their performances and uses are briefly reviewed. ills

  6. Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Hansen, Lars Kai

    2016-01-01

    the functional dynamics of the brain. Solving the inverse problem of EEG is however highly ill-posed as there are many more potential locations of the EEG generators than EEG measurement points. Several well-known properties of brain dynamics can be exploited to alleviate this problem. More short ranging......Electroencephalography (EEG) can capture brain dynamics in high temporal resolution. By projecting the scalp EEG signal back to its origin in the brain also high spatial resolution can be achieved. Source localized EEG therefore has potential to be a very powerful tool for understanding...

  7. Estimation of effective brain connectivity with dual Kalman filter and EEG source localization methods.

    Science.gov (United States)

    Rajabioun, Mehdi; Nasrabadi, Ali Motie; Shamsollahi, Mohammad Bagher

    2017-09-01

    Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.

  8. Brain-computer interfaces for EEG neurofeedback: peculiarities and solutions.

    Science.gov (United States)

    Huster, René J; Mokom, Zacharais N; Enriquez-Geppert, Stefanie; Herrmann, Christoph S

    2014-01-01

    Neurofeedback training procedures designed to alter a person's brain activity have been in use for nearly four decades now and represent one of the earliest applications of brain-computer interfaces (BCI). The majority of studies using neurofeedback technology relies on recordings of the electroencephalogram (EEG) and applies neurofeedback in clinical contexts, exploring its potential as treatment for psychopathological syndromes. This clinical focus significantly affects the technology behind neurofeedback BCIs. For example, in contrast to other BCI applications, neurofeedback BCIs usually rely on EEG-derived features with only a minimum of additional processing steps being employed. Here, we highlight the peculiarities of EEG-based neurofeedback BCIs and consider their relevance for software implementations. Having reviewed already existing packages for the implementation of BCIs, we introduce our own solution which specifically considers the relevance of multi-subject handling for experimental and clinical trials, for example by implementing ready-to-use solutions for pseudo-/sham-neurofeedback. © 2013.

  9. Electroencephalography(EEG)-based instinctive brain-control of a quadruped locomotion robot.

    Science.gov (United States)

    Jia, Wenchuan; Huang, Dandan; Luo, Xin; Pu, Huayan; Chen, Xuedong; Bai, Ou

    2012-01-01

    Artificial intelligence and bionic control have been applied in electroencephalography (EEG)-based robot system, to execute complex brain-control task. Nevertheless, due to technical limitations of the EEG decoding, the brain-computer interface (BCI) protocol is often complex, and the mapping between the EEG signal and the practical instructions lack of logic associated, which restrict the user's actual use. This paper presents a strategy that can be used to control a quadruped locomotion robot by user's instinctive action, based on five kinds of movement related neurophysiological signal. In actual use, the user drives or imagines the limbs/wrists action to generate EEG signal to adjust the real movement of the robot according to his/her own motor reflex of the robot locomotion. This method is easy for real use, as the user generates the brain-control signal through the instinctive reaction. By adopting the behavioral control of learning and evolution based on the proposed strategy, complex movement task may be realized by instinctive brain-control.

  10. Detection of EEG electrodes in brain volumes.

    Science.gov (United States)

    Graffigna, Juan P; Gómez, M Eugenia; Bustos, José J

    2010-01-01

    This paper presents a method to detect 128 EEG electrodes in image study and to merge with the Nuclear Magnetic Resonance volume for better diagnosis. First we propose three hypotheses to define a specific acquisition protocol in order to recognize the electrodes and to avoid distortions in the image. In the second instance we describe a method for segmenting the electrodes. Finally, registration is performed between volume of the electrodes and NMR.

  11. Prompt recognition of brain states by their EEG signals

    DEFF Research Database (Denmark)

    Peters, B.O.; Pfurtscheller, G.; Flyvbjerg, H.

    1997-01-01

    Brain states corresponding to intention of movement of left and right index finger and right foot are classified by a ''committee'' of artificial neural networks processing individual channels of 56-electrode electroencephalograms (EEGs). Correct recognition is achieved in 83% of cases...

  12. Brain oscillations in sport: toward EEG biomakers of performance

    Directory of Open Access Journals (Sweden)

    Guy eCheron

    2016-02-01

    Full Text Available Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The noninvasive nature of high-density electroencephalography (EEG recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.

  13. Brain Oscillations in Sport: Toward EEG Biomarkers of Performance

    Science.gov (United States)

    Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

    2016-01-01

    Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators. PMID:26955362

  14. Brain Oscillations in Sport: Toward EEG Biomarkers of Performance.

    Science.gov (United States)

    Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

    2016-01-01

    Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.

  15. EEG datasets for motor imagery brain-computer interface.

    Science.gov (United States)

    Cho, Hohyun; Ahn, Minkyu; Ahn, Sangtae; Kwon, Moonyoung; Jun, Sung Chan

    2017-07-01

    Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states. We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states. © The Authors 2017. Published by Oxford University Press.

  16. Progress in EEG-Based Brain Robot Interaction Systems

    Directory of Open Access Journals (Sweden)

    Xiaoqian Mao

    2017-01-01

    Full Text Available The most popular noninvasive Brain Robot Interaction (BRI technology uses the electroencephalogram- (EEG- based Brain Computer Interface (BCI, to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques.

  17. Continuous EEG-SEP monitoring in severe brain injury.

    Science.gov (United States)

    Amantini, A; Fossi, S; Grippo, A; Innocenti, P; Amadori, A; Bucciardini, L; Cossu, C; Nardini, C; Scarpelli, S; Roma, V; Pinto, F

    2009-04-01

    To monitor acute brain injury in the neurological intensive care unit (NICU), we used EEG and somatosensory evoked potentials (SEP) in combination to achieve more accuracy in detecting brain function deterioration. Sixty-eight patients (head trauma and intracranial hemorrhage; GCSSEP and intracranial pressure monitoring (ICP). Fifty-five patients were considered "stable" or improving, considering the GCS and CT scan: in this group, SEP didn't show significant changes. Thirteen patients showed neurological deteriorations and, in all patients, cortical SEP showed significant alterations (amplitude decrease>50% often till complete disappearance). SEP deterioration anticipated ICP increase in 30%, was contemporary in 38%, and followed ICP increase in 23%. Considering SEP and ICP in relation to clinical course, all patients but one with ICP less than 20 mmHg were stable, while the three patients with ICP greater than 40 mmHg all died. Among the 26 patients with ICP of 20-40 mmHg, 17 were stable, while nine showed clinical and neurophysiological deterioration. Thus, there is a range of ICP values (20-40 mmHg) were ICP is scarcely indicative of clinical deterioration, rather it is the SEP changes that identify brain function deterioration. Therefore, SEP have a twofold interest with respect to ICP: their changes can precede an ICP increase and they can constitute a complementary tool to interpret ICP trends. It has been very important to associate SEP and EEG: about 60% of our patients were deeply sedated and, because of their relative insensitivity to anesthetics, only SEP allowed us to monitor brain damage evolution when EEG was scarcely valuable. We observed 3% of nonconvulsive status epilepticus compared to 18% of neurological deterioration. If the aim of neurophysiological monitoring is to "detect and protect", it may not be limited to detecting seizures, rather it should be able to identify brain deterioration, so we propose the combined monitoring of EEG with SEP.

  18. Localizing Brain Activity from Multiple Distinct Sources via EEG

    Directory of Open Access Journals (Sweden)

    George Dassios

    2014-01-01

    Full Text Available An important question arousing in the framework of electroencephalography (EEG is the possibility to recognize, by means of a recorded surface potential, the number of activated areas in the brain. In the present paper, employing a homogeneous spherical conductor serving as an approximation of the brain, we provide a criterion which determines whether the measured surface potential is evoked by a single or multiple localized neuronal excitations. We show that the uniqueness of the inverse problem for a single dipole is closely connected with attaining certain relations connecting the measured data. Further, we present the necessary and sufficient conditions which decide whether the collected data originates from a single dipole or from numerous dipoles. In the case where the EEG data arouses from multiple parallel dipoles, an isolation of the source is, in general, not possible.

  19. Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization

    Science.gov (United States)

    Liu, Quanying; Ganzetti, Marco; Wenderoth, Nicole; Mantini, Dante

    2018-01-01

    Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis. PMID:29551969

  20. Preliminary study of Alzheimer's Disease diagnosis based on brain electrical signals using wireless EEG

    Science.gov (United States)

    Handayani, N.; Akbar, Y.; Khotimah, S. N.; Haryanto, F.; Arif, I.; Taruno, W. P.

    2016-03-01

    This research aims to study brain's electrical signals recorded using EEG as a basis for the diagnosis of patients with Alzheimer's Disease (AD). The subjects consisted of patients with AD, and normal subjects are used as the control. Brain signals are recorded for 3 minutes in a relaxed condition and with eyes closed. The data is processed using power spectral analysis, brain mapping and chaos test to observe the level of complexity of EEG's data. The results show a shift in the power spectral in the low frequency band (delta and theta) in AD patients. The increase of delta and theta occurs in lobus frontal area and lobus parietal respectively. However, there is a decrease of alpha activity in AD patients where in the case of normal subjects with relaxed condition, brain alpha wave dominates the posterior area. This is confirmed by the results of brain mapping. While the results of chaos analysis show that the average value of MMLE is lower in AD patients than in normal subjects. The level of chaos associated with neural complexity in AD patients with lower neural complexity is due to neuronal damage caused by the beta amyloid plaques and tau protein in neurons.

  1. Mapping perception to action in piano practice: a longitudinal DC-EEG study

    Directory of Open Access Journals (Sweden)

    Bangert Marc

    2003-10-01

    Full Text Available Abstract Background Performing music requires fast auditory and motor processing. Regarding professional musicians, recent brain imaging studies have demonstrated that auditory stimulation produces a co-activation of motor areas, whereas silent tapping of musical phrases evokes a co-activation in auditory regions. Whether this is obtained via a specific cerebral relay station is unclear. Furthermore, the time course of plasticity has not yet been addressed. Results Changes in cortical activation patterns (DC-EEG potentials induced by short (20 minute and long term (5 week piano learning were investigated during auditory and motoric tasks. Two beginner groups were trained. The 'map' group was allowed to learn the standard piano key-to-pitch map. For the 'no-map' group, random assignment of keys to tones prevented such a map. Auditory-sensorimotor EEG co-activity occurred within only 20 minutes. The effect was enhanced after 5-week training, contributing elements of both perception and action to the mental representation of the instrument. The 'map' group demonstrated significant additional activity of right anterior regions. Conclusion We conclude that musical training triggers instant plasticity in the cortex, and that right-hemispheric anterior areas provide an audio-motor interface for the mental representation of the keyboard.

  2. Temporal Comparison Between NIRS and EEG Signals During a Mental Arithmetic Task Evaluated with Self-Organizing Maps.

    Science.gov (United States)

    Oyama, Katsunori; Sakatani, Kaoru

    2016-01-01

    Simultaneous monitoring of brain activity with near-infrared spectroscopy and electroencephalography allows spatiotemporal reconstruction of the hemodynamic response regarding the concentration changes in oxyhemoglobin and deoxyhemoglobin that are associated with recorded brain activity such as cognitive functions. However, the accuracy of state estimation during mental arithmetic tasks is often different depending on the length of the segment for sampling of NIRS and EEG signals. This study compared the results of a self-organizing map and ANOVA, which were both used to assess the accuracy of state estimation. We conducted an experiment with a mental arithmetic task performed by 10 participants. The lengths of the segment in each time frame for observation of NIRS and EEG signals were compared with the 30-s, 1-min, and 2-min segment lengths. The optimal segment lengths were different for NIRS and EEG signals in the case of classification of feature vectors into the states of performing a mental arithmetic task and being at rest.

  3. Principal tools for exploring the brain and mapping its activity

    International Nuclear Information System (INIS)

    Mazoyer, B.; Mashaal, M.

    1996-01-01

    The electro-encephalography (EEG), magneto-encephalography (MEG), scanner, positron computed tomography, single photon emission computed tomography (SPECT) and NMR imaging are the main methods used to explore human brain and to do a mapping of its activity. These methods are described into details (principle, visualization, uses, advantages, disadvantages). They can be useful to detect the possible anomalies of the human brain. (O.M.)

  4. Estimating Brain Load from the EEG

    Directory of Open Access Journals (Sweden)

    Anu Holm

    2009-01-01

    Full Text Available Modern work requires cognitively demanding multitasking and the need for sustained vigilance, which may result in work-related stress and may increase the possibility of human error. Objective methods for estimating cognitive overload and mental fatigue of the brain on-line, during work performance, are needed. We present a two-channel electroencephalography (EEG–based index, theta Fz/alpha Pz ratio, potentially implementable into a compact wearable device. The index reacts to both acute external and cumulative internal load. The index increased with the number of tasks to be performed concurrently (p = 0.004 and with increased time awake, both after normal sleep (p = 0.002 and sleep restriction (p = 0.004. Moreover, the increase of the index was more pronounced in the afternoon after sleep restriction (p = 0.006. As a measure of brain state and its dynamics, the index can be considered equivalent to the heartbeat, an indicator of the cardiovascular state, thus inspiring the name "brainbeat".

  5. Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Michael H. Thaut

    2005-11-01

    Full Text Available Most EEG-based BCI systems make use of well-studied patterns of brain activity. However, those systems involve tasks that indirectly map to simple binary commands such as “yes” or “no” or require many weeks of biofeedback training. We hypothesized that signal processing and machine learning methods can be used to discriminate EEG in a direct “yes”/“no” BCI from a single session. Blind source separation (BSS and spectral transformations of the EEG produced a 180-dimensional feature space. We used a modified genetic algorithm (GA wrapped around a support vector machine (SVM classifier to search the space of feature subsets. The GA-based search found feature subsets that outperform full feature sets and random feature subsets. Also, BSS transformations of the EEG outperformed the original time series, particularly in conjunction with a subset search of both spaces. The results suggest that BSS and feature selection can be used to improve the performance of even a “direct,” single-session BCI.

  6. Brain Network Analysis from High-Resolution EEG Signals

    Science.gov (United States)

    de Vico Fallani, Fabrizio; Babiloni, Fabio

    Over the last decade, there has been a growing interest in the detection of the functional connectivity in the brain from different neuroelectromagnetic and hemodynamic signals recorded by several neuro-imaging devices such as the functional Magnetic Resonance Imaging (fMRI) scanner, electroencephalography (EEG) and magnetoencephalography (MEG) apparatus. Many methods have been proposed and discussed in the literature with the aim of estimating the functional relationships among different cerebral structures. However, the necessity of an objective comprehension of the network composed by the functional links of different brain regions is assuming an essential role in the Neuroscience. Consequently, there is a wide interest in the development and validation of mathematical tools that are appropriate to spot significant features that could describe concisely the structure of the estimated cerebral networks. The extraction of salient characteristics from brain connectivity patterns is an open challenging topic, since often the estimated cerebral networks have a relative large size and complex structure. Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach seems relevant and useful as firstly demonstrated on a set of anatomical brain networks. In those studies, the authors have employed two characteristic measures, the average shortest path L and the clustering index C, to extract respectively the global and local properties of the network structure. They have found that anatomical brain networks exhibit many local connections (i.e. a high C) and few random long distance connections (i.e. a low L). These values identify a particular model that interpolate between a regular

  7. EEG Topographic Mapping of Visual and Kinesthetic Imagery in Swimmers.

    Science.gov (United States)

    Wilson, V E; Dikman, Z; Bird, E I; Williams, J M; Harmison, R; Shaw-Thornton, L; Schwartz, G E

    2016-03-01

    This study investigated differences in QEEG measures between kinesthetic and visual imagery of a 100-m swim in 36 elite competitive swimmers. Background information and post-trial checks controlled for the modality of imagery, swimming skill level, preferred imagery style, intensity of image and task equality. Measures of EEG relative magnitude in theta, low (7-9 Hz) and high alpha (8-10 Hz), and low and high beta were taken from 19 scalp sites during baseline, visual, and kinesthetic imagery. QEEG magnitudes in the low alpha band during the visual and kinesthetic conditions were attenuated from baseline in low band alpha but no changes were seen in any other bands. Swimmers produced more low alpha EEG magnitude during visual versus kinesthetic imagery. This was interpreted as the swimmers having a greater efficiency at producing visual imagery. Participants who reported a strong intensity versus a weaker feeling of the image (kinesthetic) had less low alpha magnitude, i.e., there was use of more cortical resources, but not for the visual condition. These data suggest that low band (7-9 Hz) alpha distinguishes imagery modalities from baseline, visual imagery requires less cortical resources than kinesthetic imagery, and that intense feelings of swimming requires more brain activity than less intense feelings.

  8. David Ferrier: brain drawings and brain maps.

    Science.gov (United States)

    Lazar, J Wayne

    2013-01-01

    This chapter has two emphases, one is about the men who influenced the visual representations that David Ferrier (1843-1928) used to illustrate his work on localization of brain functions during the years 1873-1875, namely, Alexander Ecker, John C. Galton, and Ernest Waterlow, and the other is about the nature of medical representations and of Ferrier's illustrations in particular. Medical illustrations are characterized either as pictures, line drawings, or brain maps. Ferrier's illustrations will be shown to be increasingly sophisticated brain maps that contrast with early nineteenth-century standards of medical illustrations, as exemplified by John Bell (1763-1829). © 2013 Elsevier B.V. All rights reserved.

  9. Optimal spatiotemporal representation of multichannel EEG for recognition of brain states associated with distinct visual stimulus

    Science.gov (United States)

    Hramov, Alexander; Musatov, Vyacheslav Yu.; Runnova, Anastasija E.; Efremova, Tatiana Yu.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

    2018-04-01

    In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.

  10. Demonstration of brain noise on human EEG signals in perception of bistable images

    Science.gov (United States)

    Grubov, Vadim V.; Runnova, Anastasiya E.; Kurovskaya, Maria K.; Pavlov, Alexey N.; Koronovskii, Alexey A.; Hramov, Alexander E.

    2016-03-01

    In this report we studied human brain activity in the case of bistable visual perception. We proposed a new approach for quantitative characterization of this activity based on analysis of EEG oscillatory patterns and evoked potentials. Accordingly to theoretical background, obtained experimental EEG data and results of its analysis we studied a characteristics of brain activity during decision-making. Also we have shown that decisionmaking process has the special patterns on the EEG data.

  11. Rhythmic EEG patterns in extremely preterm infants : Classification and association with brain injury and outcome

    NARCIS (Netherlands)

    Weeke, Lauren C; van Ooijen, Inge M; Groenendaal, Floris; van Huffelen, Alexander C.; van Haastert, Ingrid C; van Stam, Carolien; Benders, Manon J; Toet, Mona C; Hellström-Westas, Lena; de Vries, Linda S

    2017-01-01

    OBJECTIVE: Classify rhythmic EEG patterns in extremely preterm infants and relate these to brain injury and outcome. METHODS: Retrospective analysis of 77 infants born <28 weeks gestational age (GA) who had a 2-channel EEG during the first 72 h after birth. Patterns detected by the BrainZ seizure

  12. A New Generation of Brain-Computer Interfaces Driven by Discovery of Latent EEG-fMRI Linkages Using Tensor Decomposition

    Directory of Open Access Journals (Sweden)

    Gopikrishna Deshpande

    2017-06-01

    Full Text Available A Brain-Computer Interface (BCI is a setup permitting the control of external devices by decoding brain activity. Electroencephalography (EEG has been extensively used for decoding brain activity since it is non-invasive, cheap, portable, and has high temporal resolution to allow real-time operation. Due to its poor spatial specificity, BCIs based on EEG can require extensive training and multiple trials to decode brain activity (consequently slowing down the operation of the BCI. On the other hand, BCIs based on functional magnetic resonance imaging (fMRI are more accurate owing to its superior spatial resolution and sensitivity to underlying neuronal processes which are functionally localized. However, due to its relatively low temporal resolution, high cost, and lack of portability, fMRI is unlikely to be used for routine BCI. We propose a new approach for transferring the capabilities of fMRI to EEG, which includes simultaneous EEG/fMRI sessions for finding a mapping from EEG to fMRI, followed by a BCI run from only EEG data, but driven by fMRI-like features obtained from the mapping identified previously. Our novel data-driven method is likely to discover latent linkages between electrical and hemodynamic signatures of neural activity hitherto unexplored using model-driven methods, and is likely to serve as a template for a novel multi-modal strategy wherein cross-modal EEG-fMRI interactions are exploited for the operation of a unimodal EEG system, leading to a new generation of EEG-based BCIs.

  13. A New Generation of Brain-Computer Interfaces Driven by Discovery of Latent EEG-fMRI Linkages Using Tensor Decomposition.

    Science.gov (United States)

    Deshpande, Gopikrishna; Rangaprakash, D; Oeding, Luke; Cichocki, Andrzej; Hu, Xiaoping P

    2017-01-01

    A Brain-Computer Interface (BCI) is a setup permitting the control of external devices by decoding brain activity. Electroencephalography (EEG) has been extensively used for decoding brain activity since it is non-invasive, cheap, portable, and has high temporal resolution to allow real-time operation. Due to its poor spatial specificity, BCIs based on EEG can require extensive training and multiple trials to decode brain activity (consequently slowing down the operation of the BCI). On the other hand, BCIs based on functional magnetic resonance imaging (fMRI) are more accurate owing to its superior spatial resolution and sensitivity to underlying neuronal processes which are functionally localized. However, due to its relatively low temporal resolution, high cost, and lack of portability, fMRI is unlikely to be used for routine BCI. We propose a new approach for transferring the capabilities of fMRI to EEG, which includes simultaneous EEG/fMRI sessions for finding a mapping from EEG to fMRI, followed by a BCI run from only EEG data, but driven by fMRI-like features obtained from the mapping identified previously. Our novel data-driven method is likely to discover latent linkages between electrical and hemodynamic signatures of neural activity hitherto unexplored using model-driven methods, and is likely to serve as a template for a novel multi-modal strategy wherein cross-modal EEG-fMRI interactions are exploited for the operation of a unimodal EEG system, leading to a new generation of EEG-based BCIs.

  14. Effect of Brain-to-Skull Conductivity Ratio on EEG Source Localization Accuracy

    OpenAIRE

    Gang Wang; Doutian Ren

    2013-01-01

    The goal of this study was to investigate the influence of the brain-to-skull conductivity ratio (BSCR) on EEG source localization accuracy. In this study, we evaluated four BSCRs: 15, 20, 25, and 80, which were mainly discussed according to the literature. The scalp EEG signals were generated by BSCR-related forward computation for each cortical dipole source. Then, for each scalp EEG measurement, the source reconstruction was performed to identify the estimated dipole sources by the actual ...

  15. EEG biofeedback

    OpenAIRE

    Dvořáček, Michael

    2010-01-01

    Vznik EEG aktivity v mozku, rozdělení EEG vln podle frekvence, způsob měření EEG, přístroje pro měření EEG. Dále popis biofeedback metody, její možnosti a návrh biofeedback her. Popis zpracování naměřených EEG signálů. EEG generation, brain rhythms, methods of recording EEG, EEG recorder. Description of biofeedback, potentialities of biofeedback, proposal of biofeedback games. Description of processing measured EEG signals. B

  16. Model-Based Analysis and Optimization of the Mapping of Cortical Sources in the Spontaneous Scalp EEG

    Directory of Open Access Journals (Sweden)

    Andrei V. Sazonov

    2007-01-01

    Full Text Available The mapping of brain sources into the scalp electroencephalogram (EEG depends on volume conduction properties of the head and on an electrode montage involving a reference. Mathematically, this source mapping (SM is fully determined by an observation function (OF matrix. This paper analyses the OF-matrix for a generation model for the desynchronized spontaneous EEG. The model involves a four-shell spherical volume conductor containing dipolar sources that are mutually uncorrelated so as to reflect the desynchronized EEG. The reference is optimized in order to minimize the impact in the SM of the sources located distant from the electrodes. The resulting reference is called the localized reference (LR. The OF-matrix is analyzed in terms of the relative power contribution of the sources and the cross-channel correlation coefficient for five existing references as well as for the LR. It is found that the Hjorth Laplacian reference is a fair approximation of the LR, and thus is close to optimum for practical intents and purposes. The other references have a significantly poorer performance. Furthermore, the OF-matrix is analyzed for limits to the spatial resolution for the EEG. These are estimated to be around 2 cm.

  17. The colorful brain: Visualization of EEG background patterns

    NARCIS (Netherlands)

    van Putten, Michel Johannes Antonius Maria

    2008-01-01

    This article presents a method to transform routine clinical EEG recordings to an alternative visual domain. The method is intended to support the classic visual interpretation of the EEG background pattern and to facilitate communication about relevant EEG characteristics. In addition, it provides

  18. Mapping brain function to brain anatomy

    International Nuclear Information System (INIS)

    Valentino, D.J.; Huang, H.K.; Mazziotta, J.C.

    1988-01-01

    In Imaging the human brain, MRI is commonly used to reveal anatomical structure, while PET is used to reveal tissue function. This paper presents a protocol for correlating data between these two imaging modalities; this correlation can provide in vivo regional measurements of brain function which are essential to our understanding of the human brain. The authors propose a general protocol to standardize the acquisition and analysis of functional image data. First, MR and PET images are collected to form three-dimensional volumes of structural and functional image data. Second, these volumes of image data are corrected for distortions inherent in each imaging modality. Third, the image volumes are correlated to provide correctly aligned structural and functional images. The functional images are then mapped onto the structural images in both two-dimensional and three-dimensional representations. Finally, morphometric techniques can be used to provide statistical measures of the structure and function of the human brain

  19. Wireless brain-machine interface using EEG and EOG: brain wave classification and robot control

    Science.gov (United States)

    Oh, Sechang; Kumar, Prashanth S.; Kwon, Hyeokjun; Varadan, Vijay K.

    2012-04-01

    A brain-machine interface (BMI) links a user's brain activity directly to an external device. It enables a person to control devices using only thought. Hence, it has gained significant interest in the design of assistive devices and systems for people with disabilities. In addition, BMI has also been proposed to replace humans with robots in the performance of dangerous tasks like explosives handling/diffusing, hazardous materials handling, fire fighting etc. There are mainly two types of BMI based on the measurement method of brain activity; invasive and non-invasive. Invasive BMI can provide pristine signals but it is expensive and surgery may lead to undesirable side effects. Recent advances in non-invasive BMI have opened the possibility of generating robust control signals from noisy brain activity signals like EEG and EOG. A practical implementation of a non-invasive BMI such as robot control requires: acquisition of brain signals with a robust wearable unit, noise filtering and signal processing, identification and extraction of relevant brain wave features and finally, an algorithm to determine control signals based on the wave features. In this work, we developed a wireless brain-machine interface with a small platform and established a BMI that can be used to control the movement of a robot by using the extracted features of the EEG and EOG signals. The system records and classifies EEG as alpha, beta, delta, and theta waves. The classified brain waves are then used to define the level of attention. The acceleration and deceleration or stopping of the robot is controlled based on the attention level of the wearer. In addition, the left and right movements of eye ball control the direction of the robot.

  20. Brain order disorder 2nd group report of f-EEG

    Science.gov (United States)

    Lalonde, Francois; Gogtay, Nitin; Giedd, Jay; Vydelingum, Nadarajen; Brown, David; Tran, Binh Q.; Hsu, Charles; Hsu, Ming-Kai; Cha, Jae; Jenkins, Jeffrey; Ma, Lien; Willey, Jefferson; Wu, Jerry; Oh, Kenneth; Landa, Joseph; Lin, C. T.; Jung, T. P.; Makeig, Scott; Morabito, Carlo Francesco; Moon, Qyu; Yamakawa, Takeshi; Lee, Soo-Young; Lee, Jong-Hwan; Szu, Harold H.; Kaur, Balvinder; Byrd, Kenneth; Dang, Karen; Krzywicki, Alan; Familoni, Babajide O.; Larson, Louis; Harkrider, Susan; Krapels, Keith A.; Dai, Liyi

    2014-05-01

    responses, which is what we seek. The averaged traditional EEG measurements and are further improved by BSS decomposition into finer stimulus-response source mixing matrix [A] having finer & faster spatial grids with rapid temporal updates. Then, the functional EEG is the second order co-variance matrix defined as the electrode-pair fluctuation correlation function C(s~, s~') of independent thermodynamic source components. (1) We define a 1-D Space filling curve as a spiral curve without origin. This pattern is historically known as the Peano-Hilbert arc length a. By taking the most significant bits of the Cartesian product a≡ O(x * y * z), it represents the arc length in the numerical size with values that map the 3-D neighborhood proximity into a 1-D neighborhood arc length representation. (2) 1-D Fourier coefficients spectrum have no spurious high frequency contents, which typically arise in lexicographical (zig-zag scanning) discontinuity [Hsu & Szu, "Peano-Hilbert curve," SPIE 2014]. A simple Fourier spectrum histogram fits nicely with the Compressive Sensing CRDT Mathematics. (3) Stationary power spectral density is a reasonable approximation of EEG responses in striate layers in resonance feedback loops capable of producing a 100, 000 neuronal collective Impulse Response Function (IRF). The striate brain layer architecture represents an ensemble functional-EEG: After taking the 1-D space-filling curve, we compute the ensemble averaged 1-D Power Spectral Density (PSD) and then make use of the inverse FFT to generate f-EEG. (ii) Goal#2 individual wellness baseline (IWB): We need novel change detection, so we derive the ubiquitous fat-tail distributions for healthy brains PSD in outdoor environments (Signal=310°C Noise=27°C: SNR=310/300; 300°K=(1/40)eV). The departure from IWB might imply stress, fever, a sports injury, an unexpected fall, or numerous midnight excursions which may signal an onset of dementia in Home Alone Senior (HAS), discovered by telemedicine

  1. BrainMap `95 workshop

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1995-12-31

    The fourth annual BrainMap workshop was held at La Mansion del Rio Hotel in San Antonio December 3--4, 1995. The conference title was ``Human Brain Mapping and Modeling.`` The meeting was attended by 137 registered participants and 30 observers from 82 institutions representing 12 countries. The meeting focused on the technical issues associated with brain mapping and modeling. A total of 23 papers were presented covering the following topics: spatial normalization and registration; functional image analysis; metanalysis and modeling; and new horizons in biological databases. The full program with abstracts was available on the Research Imaging Center`s web site. A book will be published by John Wiley and Sons prior to the end of 1998.

  2. [Daytime tiredness correlated with nocturnal respiratory and arousal variables in patients with sleep apnea: polysomnographic and EEG mapping studies].

    Science.gov (United States)

    Saletu, M; Hauer, C; Anderer, P; Saletu-Zyhlarz, G; Gruber, G; Oberndorfer, S; Mandl, M; Popovic, R; Saletu, B

    2000-03-24

    There is evidence that daytime tiredness is caused by apnea/hypopnea with oxygen desaturation and/or by sleep fragmentation due to arousals. The aim of this study was to investigate objective and subjective sleep and awakening quality and daytime vigilance--objectified by midmorning mapping of vigilance-controlled EEG (V-EEG)--in sleep apnea patients (N: 18), as compared with age- and sex-matched normal controls (N: 18) as well as to correlate nocturnal respiratory distress and arousals to daytime brain function. Statistical analyses demonstrated a deterioration in subjective and objective sleep and awakening quality in apnea patients. Midmorning V-EEG mapping in apnea patients exhibited less total power, more delta and theta, less alpha and beta activity, as well as a slower dominant frequency and centroid of the total activity compared to controls, which suggests a vigilance decrement. The Spearman rank correlation between 6 polysomnographically registered respiratory variables and 36 diurnal quantitative EEG measures demonstrated the following: the higher the apnea, apnea-hypopnea, snoring and desaturation indices and the lower the minimum and average low oxygen saturation, the more pronounced was diurnal tiredness. Eleven arousal measures based on ASDA criteria showed the following significant correlations: the higher the nocturnal arousal index and the more arousals due to hypopneas, the greater was daytime tiredness. On the other hand, the greater the average frequency change during arousals and the more spontaneous arousals, the better was daytime vigilance. Our findings show that, in contrast to the lengthy Multiple Sleep Latency (MSLT) and Maintenance of Wakefulness (MWT) tests which evaluate sleep pressure under resting conditions conducive to sleep, V-EEG mapping provides a brief objective measure of a sleep apnea patient's daytime tiredness under conditions of wakefulness more appropriate to reflect the patient's everyday life.

  3. EEG Oscillatory States: Universality, Uniqueness and Specificity across Healthy-Normal, Altered and Pathological Brain Conditions

    Science.gov (United States)

    Fingelkurts, Alexander A.; Fingelkurts, Andrew A.

    2014-01-01

    For the first time the dynamic repertoires and oscillatory types of local EEG states in 13 diverse conditions (examined over 9 studies) that covered healthy-normal, altered and pathological brain states were quantified within the same methodological and conceptual framework. EEG oscillatory states were assessed by the probability-classification analysis of short-term EEG spectral patterns. The results demonstrated that brain activity consists of a limited repertoire of local EEG states in any of the examined conditions. The size of the state repertoires was associated with changes in cognition and vigilance or neuropsychopathologic conditions. Additionally universal, optional and unique EEG states across 13 diverse conditions were observed. It was demonstrated also that EEG oscillations which constituted EEG states were characteristic for different groups of conditions in accordance to oscillations’ functional significance. The results suggested that (a) there is a limit in the number of local states available to the cortex and many ways in which these local states can rearrange themselves and still produce the same global state and (b) EEG individuality is determined by varying proportions of universal, optional and unique oscillatory states. The results enriched our understanding about dynamic microstructure of EEG-signal. PMID:24505292

  4. The brain as a distributed intelligent processing system: an EEG study.

    Science.gov (United States)

    da Rocha, Armando Freitas; Rocha, Fábio Theoto; Massad, Eduardo

    2011-03-15

    Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Wechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. The present results support these claims and the neural efficiency hypothesis.

  5. The brain as a distributed intelligent processing system: an EEG study.

    Directory of Open Access Journals (Sweden)

    Armando Freitas da Rocha

    Full Text Available BACKGROUND: Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS, first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. METHODOLOGY AND PRINCIPAL FINDINGS: In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Wechsler Adult Intelligence Scale and WISC (Wechsler Intelligence Scale for Children, and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. CONCLUSION: The present results support these claims and the neural efficiency hypothesis.

  6. The Brain as a Distributed Intelligent Processing System: An EEG Study

    Science.gov (United States)

    da Rocha, Armando Freitas; Rocha, Fábio Theoto; Massad, Eduardo

    2011-01-01

    Background Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. Methodology and Principal Findings In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. Conclusion The present results support these claims and the neural efficiency hypothesis. PMID:21423657

  7. Brain Friendly Techniques: Mind Mapping

    Science.gov (United States)

    Goldberg, Cristine

    2004-01-01

    Mind Mapping can be called the Swiss Army Knife for the brain, a total visual thinking tool or a multi-handed thought catcher. Invented by Tony Buzan in the early 1970s and used by millions around the world, it is a method that can be a part of a techniques repertoire when teaching information literacy, planning, presenting, thinking, and so…

  8. Functional community analysis of brain: a new approach for EEG-based investigation of the brain pathology.

    Science.gov (United States)

    Ahmadlou, Mehran; Adeli, Hojjat

    2011-09-15

    Analysis of structure of the brain functional connectivity (SBFC) is a fundamental issue for understanding of the brain cognition as well as the pathology of brain disorders. Analysis of communities among sub-parts of a system is increasingly used for social, ecological, and other networks. This paper presents a new methodology for investigation of the SBFC and understanding of the brain based on graph theory and community pattern analysis of functional connectivity graph of the brain obtained from encephalograms (EEGs). The methodology consists of three main parts: fuzzy synchronization likelihood (FSL), community partitioning, and decisions based on partitions. As an example application, the methodology is applied to analysis of brain of patients with attention deficit/hyperactivity disorder (ADHD) and the problem of discrimination of ADHD EEGs from healthy (non-ADHD) EEGs. Copyright © 2011. Published by Elsevier Inc.

  9. Preliminary study of Alzheimer's Disease diagnosis based on brain electrical signals using wireless EEG

    International Nuclear Information System (INIS)

    Handayani, N; Akbar, Y; Khotimah, S N; Haryanto, F; Arif, I; Taruno, W P

    2016-01-01

    This research aims to study brain's electrical signals recorded using EEG as a basis for the diagnosis of patients with Alzheimer's Disease (AD). The subjects consisted of patients with AD, and normal subjects are used as the control. Brain signals are recorded for 3 minutes in a relaxed condition and with eyes closed. The data is processed using power spectral analysis, brain mapping and chaos test to observe the level of complexity of EEG's data. The results show a shift in the power spectral in the low frequency band (delta and theta) in AD patients. The increase of delta and theta occurs in lobus frontal area and lobus parietal respectively. However, there is a decrease of alpha activity in AD patients where in the case of normal subjects with relaxed condition, brain alpha wave dominates the posterior area. This is confirmed by the results of brain mapping. While the results of chaos analysis show that the average value of MMLE is lower in AD patients than in normal subjects. The level of chaos associated with neural complexity in AD patients with lower neural complexity is due to neuronal damage caused by the beta amyloid plaques and tau protein in neurons. (paper)

  10. Brain Computer Interface: Assessment of Spinal Cord Injury Patient towards Motor Movement through EEG application

    Directory of Open Access Journals (Sweden)

    Syam Syahrull Hi-Fi

    2017-01-01

    Full Text Available Electroencephalography (EEG associated with motor task have been comprehensively investigated and it can also describe the brain activities while spinal cord injury (SCI patient with para/tetraplegia performing movement with their limbs. This paper reviews on conducted research regarding application of brain computer interface (BCI that offer alternative for neural impairments community such as spinal cord injury patient (SCI which include the experimental design, signal analysis of EEG band signal and data processing methods. The findings claim that the EEG signals of SCI patients associated with movement tasks can be stimulated through mental and motor task. Other than that EEG signal component such as alpha and beta frequency bands indicate significance for analysing the brain activity of subjects with SCI during movements.

  11. Guiding transcranial brain stimulation by EEG/MEG to interact with ongoing brain activity and associated functions

    DEFF Research Database (Denmark)

    Thut, Gregor; Bergmann, Til Ole; Fröhlich, Flavio

    2017-01-01

    of NTBS with respect to the ongoing brain activity. Temporal patterns of ongoing neuronal activity, in particular brain oscillations and their fluctuations, can be traced with electro- or magnetoencephalography (EEG/MEG), to guide the timing as well as the stimulation settings of NTBS. These novel, online...... and offline EEG/MEG-guided NTBS-approaches are tailored to specifically interact with the underlying brain activity. Online EEG/MEG has been used to guide the timing of NTBS (i.e., when to stimulate): by taking into account instantaneous phase or power of oscillatory brain activity, NTBS can be aligned......Non-invasive transcranial brain stimulation (NTBS) techniques have a wide range of applications but also suffer from a number of limitations mainly related to poor specificity of intervention and variable effect size. These limitations motivated recent efforts to focus on the temporal dimension...

  12. Could the beta rebound in the EEG be suitable to realize a "brain switch"?

    Science.gov (United States)

    Pfurtscheller, G; Solis-Escalante, T

    2009-01-01

    Performing foot motor imagery is accompanied by a peri-imagery ERD and a post-imagery beta ERS (beta rebound). Our aim was to study whether the post-imagery beta rebound is a suitable feature for a simple "brain switch". Such a brain switch is a specifically designed brain-computer interface (BCI) with the aim to detect only one predefined brain state (e.g. EEG pattern) in ongoing brain activity. One EEG (Laplacian) recorded at the vertex during cue-based brisk foot motor imagery was analysed in 5 healthy subjects. The peri-imagery ERD and the post-imagery beta rebound (ERS) were analysed in detail between 6 and 40Hz and classified with two support vector machines. The ERD was detected in ongoing EEG (simulation of asynchronous BCI) with a true positive rate (TPR) of 28.4%+/-13.5 and the beta rebound with a TPR of 59.2%+/-20.3. In single runs with 30 cues each, the TPR for beta rebound detection was 78.6%+/-12.8. The false positive rate was always kept below 10%. The findings suggest that the beta rebound at Cz during foot motor imagery is a relatively stable and reproducible phenomenon detectable in single EEG trials. Our results indicate that the beta rebound is a suitable feature to realize a "brain switch" with one single EEG (Laplacian) channel only.

  13. A dry EEG-system for scientific research and brain-computer interfaces

    Directory of Open Access Journals (Sweden)

    Thorsten Oliver Zander

    2011-05-01

    Full Text Available Although it ranks among the oldest tools in neuroscientific research, electroencephalography (EEG still forms the method of choice in a wide variety of clinical and research applications. In the context of Brain-Computer Interfacing (BCI, EEG recently has become a tool to enhance Human-Machine Interaction (HMI. EEG could be employed in a wider range of environments, especially for the use of BCI systems in a clinical context or at the homes of patients. However, the application of EEG in these contexts is impeded by the cumbersome preparation of the electrodes with conductive gel that is necessary to lower the impedance between electrodes and scalp. Dry electrodes could provide a solution to this barrier and allow for EEG applications outside the laboratory. In addition, dry electrodes may reduce the time needed for neurological exams in clinical practice. This study evaluates a prototype of a three-channel dry electrode EEG system, comparing it to state-of-the-art conventional EEG electrodes. Two experimental paradigms were used: first, Event-Related Potentials (ERP were investigated with a variant of the oddball paradigm. Second, features of the frequency domain were compared by a paradigm inducing occipital alpha. Furthermore, both paradigms were used to evaluate BCI classification accuracies of both EEG systems. Amplitude and temporal structure of ERPs as well as features in the frequency domain did not differ significantly between the EEG systems. BCI classification accuracies were equally high in both systems when the frequency domain was considered. With respect to the oddball classification accuracy, there were slight differences between the wet and dry electrode systems. We conclude that the tested dry electrodes were capable to detect EEG signals with good quality and that these signals can be used for research or BCI applications. Easy to handle electrodes may help to foster the use of EEG among a wider range of potential users.

  14. Scale-Free Brain-Wave Music from Simultaneously EEG and fMRI Recordings

    Science.gov (United States)

    Lu, Jing; Wu, Dan; Yang, Hua; Luo, Cheng; Li, Chaoyi; Yao, Dezhong

    2012-01-01

    In the past years, a few methods have been developed to translate human EEG to music. In 2009, PloS One 4 e5915, we developed a method to generate scale-free brainwave music where the amplitude of EEG was translated to music pitch according to the power law followed by both of them, the period of an EEG waveform is translated directly to the duration of a note, and the logarithm of the average power change of EEG is translated to music intensity according to the Fechner's law. In this work, we proposed to adopt simultaneously-recorded fMRI signal to control the intensity of the EEG music, thus an EEG-fMRI music is generated by combining two different and simultaneous brain signals. And most importantly, this approach further realized power law for music intensity as fMRI signal follows it. Thus the EEG-fMRI music makes a step ahead in reflecting the physiological process of the scale-free brain. PMID:23166768

  15. Scale-free brain-wave music from simultaneously EEG and fMRI recordings.

    Science.gov (United States)

    Lu, Jing; Wu, Dan; Yang, Hua; Luo, Cheng; Li, Chaoyi; Yao, Dezhong

    2012-01-01

    In the past years, a few methods have been developed to translate human EEG to music. In 2009, PloS One 4 e5915, we developed a method to generate scale-free brainwave music where the amplitude of EEG was translated to music pitch according to the power law followed by both of them, the period of an EEG waveform is translated directly to the duration of a note, and the logarithm of the average power change of EEG is translated to music intensity according to the Fechner's law. In this work, we proposed to adopt simultaneously-recorded fMRI signal to control the intensity of the EEG music, thus an EEG-fMRI music is generated by combining two different and simultaneous brain signals. And most importantly, this approach further realized power law for music intensity as fMRI signal follows it. Thus the EEG-fMRI music makes a step ahead in reflecting the physiological process of the scale-free brain.

  16. Rhythmic EEG patterns in extremely preterm infants: Classification and association with brain injury and outcome.

    Science.gov (United States)

    Weeke, Lauren C; van Ooijen, Inge M; Groenendaal, Floris; van Huffelen, Alexander C; van Haastert, Ingrid C; van Stam, Carolien; Benders, Manon J; Toet, Mona C; Hellström-Westas, Lena; de Vries, Linda S

    2017-12-01

    Classify rhythmic EEG patterns in extremely preterm infants and relate these to brain injury and outcome. Retrospective analysis of 77 infants born Rhythmic patterns were observed in 62.3% (ictal 1.3%, PEDs 44%, other waveforms 86.3%) with multiple patterns in 36.4%. Ictal discharges were only observed in one and excluded from further analyses. The EEG location of the other waveforms (pRhythmic waveforms related to head position are likely artefacts. Rhythmic EEG patterns may have a different significance in extremely preterm infants. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  17. Aberrant brain response after auditory deviance in PTSD compared to trauma controls: An EEG study

    NARCIS (Netherlands)

    Bangel, Katrin A.; van Buschbach, Susanne; Smit, Dirk J. A.; Mazaheri, Ali; Olff, Miranda

    2017-01-01

    Part of the symptomatology of post-traumatic stress disorder (PTSD) are alterations in arousal and reactivity which could be related to a maladaptive increase in the automated sensory change detection system of the brain. In the current EEG study we investigated whether the brain's response to a

  18. Brain Mapping of drug addiction in witdrawal condition based P300 Signals

    Science.gov (United States)

    Turnip, Arjon; Esti Kusumandari, Dwi; Hidayat, Teddy

    2018-04-01

    Drug abuse for a long time will slowly cause changes in brain structure and performance. These changes tend to occur in the front of the brain which is directly interfere the concentration and the decision-making process. In this study an experiment involving 10 drug users was performed. The process of recording data with EEG system is conducted during craving condition and 1 hour after taking methadone. From brain mapping results obtained that brain activity tend to occur in the upper layer of the brain during craving conditions and tend to be in the midle layer of the brain after one hour of taking methadone.

  19. Brain/MINDS: brain-mapping project in Japan

    Science.gov (United States)

    Okano, Hideyuki; Miyawaki, Atsushi; Kasai, Kiyoto

    2015-01-01

    There is an emerging interest in brain-mapping projects in countries across the world, including the USA, Europe, Australia and China. In 2014, Japan started a brain-mapping project called Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS). Brain/MINDS aims to map the structure and function of neuronal circuits to ultimately understand the vast complexity of the human brain, and takes advantage of a unique non-human primate animal model, the common marmoset (Callithrix jacchus). In Brain/MINDS, the RIKEN Brain Science Institute acts as a central institute. The objectives of Brain/MINDS can be categorized into the following three major subject areas: (i) structure and functional mapping of a non-human primate brain (the marmoset brain); (ii) development of innovative neurotechnologies for brain mapping; and (iii) human brain mapping; and clinical research. Brain/MINDS researchers are highly motivated to identify the neuronal circuits responsible for the phenotype of neurological and psychiatric disorders, and to understand the development of these devastating disorders through the integration of these three subject areas. PMID:25823872

  20. Brain/MINDS: brain-mapping project in Japan.

    Science.gov (United States)

    Okano, Hideyuki; Miyawaki, Atsushi; Kasai, Kiyoto

    2015-05-19

    There is an emerging interest in brain-mapping projects in countries across the world, including the USA, Europe, Australia and China. In 2014, Japan started a brain-mapping project called Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS). Brain/MINDS aims to map the structure and function of neuronal circuits to ultimately understand the vast complexity of the human brain, and takes advantage of a unique non-human primate animal model, the common marmoset (Callithrix jacchus). In Brain/MINDS, the RIKEN Brain Science Institute acts as a central institute. The objectives of Brain/MINDS can be categorized into the following three major subject areas: (i) structure and functional mapping of a non-human primate brain (the marmoset brain); (ii) development of innovative neurotechnologies for brain mapping; and (iii) human brain mapping; and clinical research. Brain/MINDS researchers are highly motivated to identify the neuronal circuits responsible for the phenotype of neurological and psychiatric disorders, and to understand the development of these devastating disorders through the integration of these three subject areas.

  1. Topographic brain mapping of emotion-related hemisphere asymmetries.

    Science.gov (United States)

    Roschmann, R; Wittling, W

    1992-03-01

    The study used topographic brain mapping of visual evoked potentials to investigate emotion-related hemisphere asymmetries. The stimulus material consisted of color photographs of human faces, grouped into two emotion-related categories: normal faces (neutral stimuli) and faces deformed by dermatological diseases (emotional stimuli). The pictures were presented tachistoscopically to 20 adult right-handed subjects. Brain activity was recorded by 30 EEG electrodes with linked ears as reference. The waveforms were averaged separately with respect to each of the two stimulus conditions. Statistical analysis by means of significance probability mapping revealed significant differences between stimulus conditions for two periods of time, indicating right hemisphere superiority in emotion-related processing. The results are discussed in terms of a 2-stage-model of emotional processing in the cerebral hemispheres.

  2. Extraction of features from sleep EEG for Bayesian assessment of brain development.

    Directory of Open Access Journals (Sweden)

    Vitaly Schetinin

    Full Text Available Brain development can be evaluated by experts analysing age-related patterns in sleep electroencephalograms (EEG. Natural variations in the patterns, noise, and artefacts affect the evaluation accuracy as well as experts' agreement. The knowledge of predictive posterior distribution allows experts to estimate confidence intervals within which decisions are distributed. Bayesian approach to probabilistic inference has provided accurate estimates of intervals of interest. In this paper we propose a new feature extraction technique for Bayesian assessment and estimation of predictive distribution in a case of newborn brain development assessment. The new EEG features are verified within the Bayesian framework on a large EEG data set including 1,100 recordings made from newborns in 10 age groups. The proposed features are highly correlated with brain maturation and their use increases the assessment accuracy.

  3. Optimal use of EEG recordings to target active brain areas with transcranial electrical stimulation.

    Science.gov (United States)

    Dmochowski, Jacek P; Koessler, Laurent; Norcia, Anthony M; Bikson, Marom; Parra, Lucas C

    2017-08-15

    To demonstrate causal relationships between brain and behavior, investigators would like to guide brain stimulation using measurements of neural activity. Particularly promising in this context are electroencephalography (EEG) and transcranial electrical stimulation (TES), as they are linked by a reciprocity principle which, despite being known for decades, has not led to a formalism for relating EEG recordings to optimal stimulation parameters. Here we derive a closed-form expression for the TES configuration that optimally stimulates (i.e., targets) the sources of recorded EEG, without making assumptions about source location or distribution. We also derive a duality between TES targeting and EEG source localization, and demonstrate that in cases where source localization fails, so does the proposed targeting. Numerical simulations with multiple head models confirm these theoretical predictions and quantify the achieved stimulation in terms of focality and intensity. We show that constraining the stimulation currents automatically selects optimal montages that involve only a few (4-7) electrodes, with only incremental loss in performance when targeting focal activations. The proposed technique allows brain scientists and clinicians to rationally target the sources of observed EEG and thus overcomes a major obstacle to the realization of individualized or closed-loop brain stimulation. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  4. Effects of green and black tea consumption on brain wave activities in healthy volunteers as measured by a simplified Electroencephalogram (EEG): A feasibility study.

    Science.gov (United States)

    Okello, Edward J; Abadi, Awatf M; Abadi, Saad A

    2016-06-01

    Tea has been associated with many mental benefits, such as attention enhancement, clarity of mind, and relaxation. These psychosomatic states can be measured in terms of brain activity using an electroencephalogram (EEG). Brain activity can be assessed either during a state of passive activity or when performing attention tasks and it can provide useful information about the brain's state. This study investigated the effects of green and black consumption on brain activity as measured by a simplified EEG, during passive activity. Eight healthy volunteers participated in the study. The EEG measurements were performed using a two channel EEG brain mapping instrument - HeadCoach™. Fast Fourier transform algorithm and EEGLAB toolbox using the Matlab software were used for data processing and analysis. Alpha, theta, and beta wave activities were all found to increase after 1 hour of green and black tea consumption, albeit, with very considerable inter-individual variations. Our findings provide further evidence for the putative beneficial effects of tea. The highly significant increase in theta waves (P by 'from field to shelf practices'.

  5. {sup 99m}Tc-HMPAO Brain SPECT in Seizure Disorder: Comparison Brain SPECT, MRI / CT and EEG

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Hyung In [Kyunghee University Hospital, Seoul (Korea, Republic of); Im, Ju Hyuk; Choi, Chang Woon; Lee, Dong Soo; Chung, June Key; No, Jae Kyu; Lee, Myung Chul; Koh, Chang Soon [Seoul National University Hospital, Seoul (Korea, Republic of)

    1994-03-15

    We studied 115 patients with seizure who had been performed brain SPECT brain MRI of CT and EEG. To evaluate the pattern of brain SPECT in seizure patients 28 of them had secondary epilepsies, 87 had primary epilepsies. In primary epilepsies, 42 were generalized seizure and 45 were partial seizure. The causes of secondary epilepsies were congenital malformation, cerebromalacia, cerebral infarction ultiple sclerosis, AV-malformation. granuloma and etc, in order. In 28 secondary epilepsies, 25 of them, brain SPECT lesions was concordant with MRI or CT lesions. 3 were disconcordant. The brain SPECT findings of generalized seizure were normal in 22 patients, diffuse irregular decreased perfusion in 8, decreased in frontal cortex in 4. temporal in 5 and frontotemporal in 3. In 45 partial seizure, 19 brain SPECT were concordant with EEG (42.4%).

  6. 99mTc-HMPAO Brain SPECT in Seizure Disorder: Comparison Brain SPECT, MRI / CT and EEG

    International Nuclear Information System (INIS)

    Yang, Hyung In; Im, Ju Hyuk; Choi, Chang Woon; Lee, Dong Soo; Chung, June Key; No, Jae Kyu; Lee, Myung Chul; Koh, Chang Soon

    1994-01-01

    We studied 115 patients with seizure who had been performed brain SPECT brain MRI of CT and EEG. To evaluate the pattern of brain SPECT in seizure patients 28 of them had secondary epilepsies, 87 had primary epilepsies. In primary epilepsies, 42 were generalized seizure and 45 were partial seizure. The causes of secondary epilepsies were congenital malformation, cerebromalacia, cerebral infarction ultiple sclerosis, AV-malformation. granuloma and etc, in order. In 28 secondary epilepsies, 25 of them, brain SPECT lesions was concordant with MRI or CT lesions. 3 were disconcordant. The brain SPECT findings of generalized seizure were normal in 22 patients, diffuse irregular decreased perfusion in 8, decreased in frontal cortex in 4. temporal in 5 and frontotemporal in 3. In 45 partial seizure, 19 brain SPECT were concordant with EEG (42.4%).

  7. Brain perfusion SPECT and EEG findings in Rett syndrome

    International Nuclear Information System (INIS)

    Lappalainen, R.; Liewendahl, K.; Nikkinen, P.; Sainio, K.; Riikonen, R.S.

    1997-01-01

    Thirteen patients (mean age 8.4 + 5.3 years) with Rett syndrome (RS) were studied with EEG and 99m Tc-HMPAO SPECT. Eleven patients had background abnormalities and 10 patients paroxysmal activity in EEG. Hypoperfusion of varying severity was detected in 11 patients, 7 patients having multiple lesions. Bifrontal hypoperfusion, observed in 6 patients, was the most distinctive finding. Hypoperfusion was observed also in other cortical regions, except for the occipital lobes. There was no correlation between severity of the background abnormality or presence of paroxysmal activity in EEG and grade of hypoperfusion. There was, however, an association between the severity of hypoperfusion and early manifestation of symptoms in patients with RS. Whether this early-onset group of patients represents a different disease entity or only reflects disease variability the basic pathology being the same, is a possibility that deserves further clarification. (au) 37 refs

  8. A Fast EEG Forecasting Algorithm for Phase-Locked Transcranial Electrical Stimulation of the Human Brain

    Directory of Open Access Journals (Sweden)

    Farrokh Mansouri

    2017-07-01

    Full Text Available A growing body of research suggests that non-invasive electrical brain stimulation can more effectively modulate neural activity when phase-locked to the underlying brain rhythms. Transcranial alternating current stimulation (tACS can potentially stimulate the brain in-phase to its natural oscillations as recorded by electroencephalography (EEG, but matching these oscillations is a challenging problem due to the complex and time-varying nature of the EEG signals. Here we address this challenge by developing and testing a novel approach intended to deliver tACS phase-locked to the activity of the underlying brain region in real-time. This novel approach extracts phase and frequency from a segment of EEG, then forecasts the signal to control the stimulation. A careful tuning of the EEG segment length and prediction horizon is required and has been investigated here for different EEG frequency bands. The algorithm was tested on EEG data from 5 healthy volunteers. Algorithm performance was quantified in terms of phase-locking values across a variety of EEG frequency bands. Phase-locking performance was found to be consistent across individuals and recording locations. With current parameters, the algorithm performs best when tracking oscillations in the alpha band (8–13 Hz, with a phase-locking value of 0.77 ± 0.08. Performance was maximized when the frequency band of interest had a dominant frequency that was stable over time. The algorithm performs faster, and provides better phase-locked stimulation, compared to other recently published algorithms devised for this purpose. The algorithm is suitable for use in future studies of phase-locked tACS in preclinical and clinical applications.

  9. Similar or disparate brain patterns? The intra-personal EEG variability of three women with multiple personality disorder.

    Science.gov (United States)

    Lapointe, A R; Crayton, J W; DeVito, R; Fichtner, C G; Konopka, L M

    2006-07-01

    Quantitative EEG was used to assess the intra-personal variability of brain electrical activity for 3 women diagnosed with Multiple Personality Disorder (MPD). Two separate control groups (within-subject and between-subject) were used to test the hypothesis that the intra-personal EEG variability between 2 alters would be less than the interpersonal EEG variability between 2 controls, and similar to the intra-personal EEG variability of a single personality. This hypothesis was partially supported. In general, the 2 EEG records of a MPD subject (alter 1 vs. alter 2) were more different from one another than the 2 EEG records of a single control, but less different from one another than the EEG records of 2 separate controls. Most of the EEG variability between alters involved beta activity in the frontal and temporal lobes.

  10. A method for detecting nonlinear determinism in normal and epileptic brain EEG signals.

    Science.gov (United States)

    Meghdadi, Amir H; Fazel-Rezai, Reza; Aghakhani, Yahya

    2007-01-01

    A robust method of detecting determinism for short time series is proposed and applied to both healthy and epileptic EEG signals. The method provides a robust measure of determinism through characterizing the trajectories of the signal components which are obtained through singular value decomposition. Robustness of the method is shown by calculating proposed index of determinism at different levels of white and colored noise added to a simulated chaotic signal. The method is shown to be able to detect determinism at considerably high levels of additive noise. The method is then applied to both intracranial and scalp EEG recordings collected in different data sets for healthy and epileptic brain signals. The results show that for all of the studied EEG data sets there is enough evidence of determinism. The determinism is more significant for intracranial EEG recordings particularly during seizure activity.

  11. Mapping Language Problems in the Brain

    Science.gov (United States)

    ... issue Health Capsule Mapping Language Problems in the Brain En español Send us your comments We often ... more about how language is organized in the brain, an NIH-funded research team studied people with ...

  12. Computational brain connectivity mapping: A core health and scientific challenge.

    Science.gov (United States)

    Deriche, Rachid

    2016-10-01

    One third of the burden of all the diseases in Europe is due to problems caused by diseases affecting brain. Although exceptional progress have been obtained for exploring the brain during the past decades, it is still terra-incognita and calls for specific efforts in research to better understand its architecture and functioning. To take up this great challenge of modern science and to solve the limited view of the brain provided just by one imaging modality, this article advocates the idea developed in my research group of a global approach involving new generation of models for brain connectivity mapping and strong interactions between structural and functional connectivities. Capitalizing on the strengths of integrated and complementary non invasive imaging modalities such as diffusion Magnetic Resonance Imaging (dMRI) and Electro & Magneto-Encephalography (EEG & MEG) will contribute to achieve new frontiers for identifying and characterizing structural and functional brain connectivities and to provide a detailed mapping of the brain connectivity, both in space and time. Thus leading to an added clinical value for high impact diseases with new perspectives in computational neuro-imaging and cognitive neuroscience. Copyright © 2016 Elsevier B.V. All rights reserved.

  13. Development of Brain EEG Connectivity across Early Childhood: Does Sleep Play a Role?

    Directory of Open Access Journals (Sweden)

    Monique K. LeBourgeois

    2013-11-01

    Full Text Available Sleep has beneficial effects on brain function and learning, which are reflected in plastic changes in the cortex. Early childhood is a time of rapid maturation in fundamental skills—e.g., language, cognitive control, working memory—that are predictive of future functioning. Little is currently known about the interactions between sleep and brain maturation during this developmental period. We propose coherent electroencephalogram (EEG activity during sleep may provide unique insight into maturational processes of functional brain connectivity. Longitudinal sleep EEG assessments were performed in eight healthy subjects at ages 2, 3 and 5 years. Sleep EEG coherence increased across development in a region- and frequency-specific manner. Moreover, although connectivity primarily decreased intra-hemispherically across a night of sleep, an inter-hemispheric overnight increase occurred in the frequency range of slow waves (0.8–2 Hz, theta (4.8–7.8 Hz and sleep spindles (10–14 Hz, with connectivity changes of up to 20% across a night of sleep. These findings indicate sleep EEG coherence reflects processes of brain maturation—i.e., programmed unfolding of neuronal networks—and moreover, sleep-related alterations of brain connectivity during the sensitive maturational window of early childhood.

  14. Linking EEG signals, brain functions and mental operations: Advantages of the Laplacian transformation.

    Science.gov (United States)

    Vidal, Franck; Burle, Boris; Spieser, Laure; Carbonnell, Laurence; Meckler, Cédric; Casini, Laurence; Hasbroucq, Thierry

    2015-09-01

    Electroencephalography (EEG) is a very popular technique for investigating brain functions and/or mental processes. To this aim, EEG activities must be interpreted in terms of brain and/or mental processes. EEG signals being a direct manifestation of neuronal activity it is often assumed that such interpretations are quite obvious or, at least, straightforward. However, they often rely on (explicit or even implicit) assumptions regarding the structures supposed to generate the EEG activities of interest. For these assumptions to be used appropriately, reliable links between EEG activities and the underlying brain structures must be established. Because of volume conduction effects and the mixture of activities they induce, these links are difficult to establish with scalp potential recordings. We present different examples showing how the Laplacian transformation, acting as an efficient source separation method, allowed to establish more reliable links between EEG activities and brain generators and, ultimately, with mental operations. The nature of those links depends on the depth of inferences that can vary from weak to strong. Along this continuum, we show that 1) while the effects of experimental manipulation can appear widely distributed with scalp potentials, Laplacian transformation allows to reveal several generators contributing (in different manners) to these modulations, 2) amplitude variations within the same set of generators can generate spurious differences in scalp potential topographies, often interpreted as reflecting different source configurations. In such a case, Laplacian transformation provides much more similar topographies, evidencing the same generator(s) set, and 3) using the LRP as an index of response activation most often produces ambiguous results, Laplacian-transformed response-locked ERPs obtained over motor areas allow resolving these ambiguities. Copyright © 2015 Elsevier B.V. All rights reserved.

  15. More 'mapping' in brain mapping: statistical comparison of effects

    DEFF Research Database (Denmark)

    Jernigan, Terry Lynne; Gamst, Anthony C.; Fennema-Notestine, Christine

    2003-01-01

    The term 'mapping' in the context of brain imaging conveys to most the concept of localization; that is, a brain map is meant to reveal a relationship between some condition or parameter and specific sites within the brain. However, in reality, conventional voxel-based maps of brain function......, or for that matter of brain structure, are generally constructed using analyses that yield no basis for inferences regarding the spatial nonuniformity of the effects. In the normal analysis path for functional images, for example, there is nowhere a statistical comparison of the observed effect in any voxel relative...... to that in any other voxel. Under these circumstances, strictly speaking, the presence of significant activation serves as a legitimate basis only for inferences about the brain as a unit. In their discussion of results, investigators rarely are content to confirm the brain's role, and instead generally prefer...

  16. Impact of metal artefacts due to EEG electrodes in brain PET/CT imaging

    International Nuclear Information System (INIS)

    Lemmens, Catherine; Nuyts, Johan; Dupont, Patrick; Montandon, Marie-Louise; Ratib, Osman; Zaidi, Habib

    2008-01-01

    The goal of this study is to investigate the impact of electroencephalogram (EEG) electrodes on the visual quality and quantification of 18 F-FDG PET images in neurological PET/CT examinations. For this purpose, the scans of 20 epilepsy patients with EEG monitoring were used. The CT data were reconstructed with filtered backprojection (FBP) and with a metal artefact reduction (MAR) algorithm. Both data sets were used for CT-based attenuation correction (AC) of the PET data. Also, a calculated AC (CALC) technique was considered. A volume of interest (VOI)-based analysis and a voxel-based quantitative analysis were performed to compare the different AC methods. Images were also evaluated visually by two observers. It was shown with simulations and phantom measurements that from the considered AC methods, the MAR-AC can be used as the reference in this setting. The visual assessment of PET images showed local hot spots outside the brain corresponding to the locations of the electrodes when using FBP-AC. In the brain, no abnormalities were observed. The quantitative analysis showed a very good correlation between PET-FBP-AC and PET-MAR-AC, with a statistically significant positive bias in the PET-FBP-AC images of about 5-7% in most brain voxels. There was also good correlation between PET-CALC-AC and PET-MAR-AC, but in the PET-CALC-AC images, regions with both a significant positive and negative bias were observed. EEG electrodes give rise to local hot spots outside the brain and a positive quantification bias in the brain. However, when diagnosis is made by mere visual assessment, the presence of EEG electrodes does not seem to alter the diagnosis. When quantification is performed, the bias becomes an issue especially when comparing brain images with and without EEG monitoring

  17. Impact of metal artefacts due to EEG electrodes in brain PET/CT imaging

    Energy Technology Data Exchange (ETDEWEB)

    Lemmens, Catherine; Nuyts, Johan; Dupont, Patrick [Department of Nuclear Medicine and Medical Imaging Center, University Hospital Gasthuisberg and Katholieke Universiteit Leuven, Leuven (Belgium); Montandon, Marie-Louise; Ratib, Osman; Zaidi, Habib [Division of Nuclear Medicine, Geneva University Hospital, CH-1211 Geneva (Switzerland)], E-mail: catherine.lemmens@uz.kuleuven.be

    2008-08-21

    The goal of this study is to investigate the impact of electroencephalogram (EEG) electrodes on the visual quality and quantification of {sup 18}F-FDG PET images in neurological PET/CT examinations. For this purpose, the scans of 20 epilepsy patients with EEG monitoring were used. The CT data were reconstructed with filtered backprojection (FBP) and with a metal artefact reduction (MAR) algorithm. Both data sets were used for CT-based attenuation correction (AC) of the PET data. Also, a calculated AC (CALC) technique was considered. A volume of interest (VOI)-based analysis and a voxel-based quantitative analysis were performed to compare the different AC methods. Images were also evaluated visually by two observers. It was shown with simulations and phantom measurements that from the considered AC methods, the MAR-AC can be used as the reference in this setting. The visual assessment of PET images showed local hot spots outside the brain corresponding to the locations of the electrodes when using FBP-AC. In the brain, no abnormalities were observed. The quantitative analysis showed a very good correlation between PET-FBP-AC and PET-MAR-AC, with a statistically significant positive bias in the PET-FBP-AC images of about 5-7% in most brain voxels. There was also good correlation between PET-CALC-AC and PET-MAR-AC, but in the PET-CALC-AC images, regions with both a significant positive and negative bias were observed. EEG electrodes give rise to local hot spots outside the brain and a positive quantification bias in the brain. However, when diagnosis is made by mere visual assessment, the presence of EEG electrodes does not seem to alter the diagnosis. When quantification is performed, the bias becomes an issue especially when comparing brain images with and without EEG monitoring.

  18. Cross-sensory gating in schizophrenia and autism spectrum disorder : EEG evidence for impaired brain connectivity?

    NARCIS (Netherlands)

    Magnee, Maurice J. C. M.; Oranje, Bob; van Engeland, Herman; Kahn, Rene S.; Kemner, Chantal

    Autism spectrum disorders (ASD) and schizophrenia are both neurodevelopmental disorders that have extensively been associated with impairments in functional brain connectivity. Using a cross-sensory P50 suppression paradigm, this study investigated low-level audiovisual interactions on cortical EEG

  19. Sleep EEG Changes during Adolescence: An Index of a Fundamental Brain Reorganization

    Science.gov (United States)

    Feinberg, Irwin; Campbell, Ian G.

    2010-01-01

    Delta (1-4 Hz) EEG power in non-rapid eye movement (NREM) sleep declines massively during adolescence. This observation stimulated the hypothesis that during adolescence the human brain undergoes an extensive reorganization driven by synaptic elimination. The parallel declines in synaptic density, delta wave amplitude and cortical metabolic rate…

  20. Patient Specific Characteristic of Brain Dynamic in Interpretation of Long Term EEG Analysis

    Czech Academy of Sciences Publication Activity Database

    Komárek, V.; Paluš, Milan; Hrnčíř, Z.

    2004-01-01

    Roč. 45, Suppl. 3 (2004), s. 51 ISSN 0013-9580. [European Congress on Epileptology /6./. 30.05.2004-03.06.2004, Vienna] R&D Projects: GA MŠk ME 701 Institutional research plan: CEZ:AV0Z1030915 Keywords : brain dynamic * long term EEG analysis Subject RIV: FH - Neurology

  1. [Intracerebral EEG functioning as a reflexion of the systemic brain organization in norm and pathology].

    Science.gov (United States)

    Boldyreva, G N; Zhavoronkova, L A; Sharova, E V; Dobronravova, I S

    2003-01-01

    The authors summarized the EEG findings and defined the nature of intercentral EEG relationships in different functional states of healthy subjects and patients with organic cerebral pathology based on coherence analysis. The EEG features typical of healthy subjects were identified: an anterior-posterior gradient of the mean coherence and the character of cortical-subcortical relationships in the anterior cerebral structures. Right- and lefthanded subjects showed the frequency and regional differences in EEG coherence, which reflected, mainly, specific intracortical relationships. Development and regression of pathologic signs in right- and lefthanded patients with organic brain lesions are thought to be determined by these differences. As distinct from cortical pathology, lesions of regulatory structures (diencephalic, brainstem, and limbic) were shown to produce more diffuse changes in intercentral relationships with a tendency to reciprocity. Intercentral relations, including their interhemispheric differences, varied with changes in the functional state of healthy subjects (increase and decrease in the level of functioning). A certain time course of changes in intercentral relationships was also revealed in patients with organic brain lesions during recovery of their consciousness and mental activity. Changes in the dominance of activity of individual regulatory structures are considered to be one of the most important factors that determine the dynamic character of EEG coherence.

  2. Spontaneous Slow Fluctuation of EEG Alpha Rhythm Reflects Activity in Deep-Brain Structures: A Simultaneous EEG-fMRI Study.

    Directory of Open Access Journals (Sweden)

    Kei Omata

    Full Text Available The emergence of the occipital alpha rhythm on brain electroencephalogram (EEG is associated with brain activity in the cerebral neocortex and deep brain structures. To further understand the mechanisms of alpha rhythm power fluctuation, we performed simultaneous EEGs and functional magnetic resonance imaging recordings in human subjects during a resting state and explored the dynamic relationship between alpha power fluctuation and blood oxygenation level-dependent (BOLD signals of the brain. Based on the frequency characteristics of the alpha power time series (APTS during 20-minute EEG recordings, we divided the APTS into two components: fast fluctuation (0.04-0.167 Hz and slow fluctuation (0-0.04 Hz. Analysis of the correlation between the MRI signal and each component revealed that the slow fluctuation component of alpha power was positively correlated with BOLD signal changes in the brain stem and the medial part of the thalamus and anterior cingulate cortex, while the fast fluctuation component was correlated with the lateral part of the thalamus and the anterior cingulate cortex, but not the brain stem. In summary, these data suggest that different subcortical structures contribute to slow and fast modulations of alpha spectra on brain EEG.

  3. The urban brain: analysing outdoor physical activity with mobile EEG.

    Science.gov (United States)

    Aspinall, Peter; Mavros, Panagiotis; Coyne, Richard; Roe, Jenny

    2015-02-01

    Researchers in environmental psychology, health studies and urban design are interested in the relationship between the environment, behaviour settings and emotions. In particular, happiness, or the presence of positive emotional mindsets, broadens an individual's thought-action repertoire with positive benefits to physical and intellectual activities, and to social and psychological resources. This occurs through play, exploration or similar activities. In addition, a body of restorative literature focuses on the potential benefits to emotional recovery from stress offered by green space and 'soft fascination'. However, access to the cortical correlates of emotional states of a person actively engaged within an environment has not been possible until recently. This study investigates the use of mobile electroencephalography (EEG) as a method to record and analyse the emotional experience of a group of walkers in three types of urban environment including a green space setting. Using Emotiv EPOC, a low-cost mobile EEG recorder, participants took part in a 25 min walk through three different areas of Edinburgh. The areas (of approximately equal length) were labelled zone 1 (urban shopping street), zone 2 (path through green space) and zone 3 (street in a busy commercial district). The equipment provided continuous recordings from five channels, labelled excitement (short-term), frustration, engagement, long-term excitement (or arousal) and meditation. A new form of high-dimensional correlated component logistic regression analysis showed evidence of lower frustration, engagement and arousal, and higher meditation when moving into the green space zone; and higher engagement when moving out of it. Systematic differences in EEG recordings were found between three urban areas in line with restoration theory. This has implications for promoting urban green space as a mood-enhancing environment for walking or for other forms of physical or reflective activity. Published

  4. Slow oscillation electrical brain stimulation during waking promotes EEG theta activity and memory encoding

    DEFF Research Database (Denmark)

    Kirov, Roumen; Weiss, Carsten; Siebner, Hartwig R

    2009-01-01

    typically occurring during this state of sleep were also enhanced. Here, we show that the same tSOS applied in the waking brain also induced an increase in endogenous EEG slow oscillations (0.4-1.2 Hz), although in a topographically restricted fashion. Applied during wakefulness tSOS, additionally, resulted......The application of transcranial slow oscillation stimulation (tSOS; 0.75 Hz) was previously shown to enhance widespread endogenous EEG slow oscillatory activity when applied during a sleep period characterized by emerging endogenous slow oscillatory activity. Processes of memory consolidation...... induced by tSOS critically depend on brain state. In response to tSOS during wakefulness the brain transposes stimulation by responding preferentially with theta oscillations and facilitated encoding....

  5. Calibrating EEG-based motor imagery brain-computer interface from passive movement.

    Science.gov (United States)

    Ang, Kai Keng; Guan, Cuntai; Wang, Chuanchu; Phua, Kok Soon; Tan, Adrian Hock Guan; Chin, Zheng Yang

    2011-01-01

    EEG data from performing motor imagery are usually collected to calibrate a subject-specific model for classifying the EEG data during the evaluation phase of motor imagery Brain-Computer Interface (BCI). However, there is no direct objective measure to determine if a subject is performing motor imagery correctly for proper calibration. Studies have shown that passive movement, which is directly observable, induces Event-Related Synchronization patterns that are similar to those induced from motor imagery. Hence, this paper investigates the feasibility of calibrating EEG-based motor imagery BCI from passive movement. EEG data of 12 healthy subjects were collected during motor imagery and passive movement of the hand by a haptic knob robot. The calibration models using the Filter Bank Common Spatial Pattern algorithm on the EEG data from motor imagery were compared against using the EEG data from passive movement. The performances were compared based on the 10×10-fold cross-validation accuracies of the calibration data, and off-line session-to-session transfer kappa values to other sessions of motor imagery performed on another day. The results showed that the calibration performed using passive movement yielded higher model accuracy and off-line session-to-session transfer (73.6% and 0.354) than the calibration performed using motor imagery (71.3% and 0.311), and no significant differences were observed between the two groups (p=0.20, 0.23). Hence, this study shows that it is feasible to calibrate EEG-based motor imagery BCI from passive movement.

  6. Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

    Science.gov (United States)

    Wang, Deng; Miao, Duoqian; Blohm, Gunnar

    2012-01-01

    Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. PMID:23087607

  7. Epileptic Seizure Detection based on Wavelet Transform Statistics Map and EMD Method for Hilbert-Huang Spectral Analyzing in Gamma Frequency Band of EEG Signals

    Directory of Open Access Journals (Sweden)

    Morteza Behnam

    2015-08-01

    Full Text Available Seizure detection using brain signal (EEG analysis is the important clinical methods in drug therapy and the decisions before brain surgery. In this paper, after signal conditioning using suitable filtering, the Gamma frequency band has been extracted and the other brain rhythms, ambient noises and the other bio-signal are canceled. Then, the wavelet transform of brain signal and the map of wavelet transform in multi levels are computed. By dividing the color map to different epochs, the histogram of each sub-image is obtained and the statistics of it based on statistical momentums and Negentropy values are calculated. Statistical feature vector using Principle Component Analysis (PCA is reduced to one dimension. By EMD algorithm and sifting procedure for analyzing the data by Intrinsic Mode Function (IMF and computing the residues of brain signal using spectrum of Hilbert transform and Hilbert – Huang spectrum forming, one spatial feature based on the Euclidian distance for signal classification is obtained. By K-Nearest Neighbor (KNN classifier and by considering the optimal neighbor parameter, EEG signals are classified in two classes, seizure and non-seizure signal, with the rate of accuracy 76.54% and with variance of error 0.3685 in the different tests.

  8. Bedside functional brain imaging in critically-ill children using high-density EEG source modeling and multi-modal sensory stimulation

    Directory of Open Access Journals (Sweden)

    Danny Eytan

    2016-01-01

    Full Text Available Acute brain injury is a common cause of death and critical illness in children and young adults. Fundamental management focuses on early characterization of the extent of injury and optimizing recovery by preventing secondary damage during the days following the primary injury. Currently, bedside technology for measuring neurological function is mainly limited to using electroencephalography (EEG for detection of seizures and encephalopathic features, and evoked potentials. We present a proof of concept study in patients with acute brain injury in the intensive care setting, featuring a bedside functional imaging set-up designed to map cortical brain activation patterns by combining high density EEG recordings, multi-modal sensory stimulation (auditory, visual, and somatosensory, and EEG source modeling. Use of source-modeling allows for examination of spatiotemporal activation patterns at the cortical region level as opposed to the traditional scalp potential maps. The application of this system in both healthy and brain-injured participants is demonstrated with modality-specific source-reconstructed cortical activation patterns. By combining stimulation obtained with different modalities, most of the cortical surface can be monitored for changes in functional activation without having to physically transport the subject to an imaging suite. The results in patients in an intensive care setting with anatomically well-defined brain lesions suggest a topographic association between their injuries and activation patterns. Moreover, we report the reproducible application of a protocol examining a higher-level cortical processing with an auditory oddball paradigm involving presentation of the patient's own name. This study reports the first successful application of a bedside functional brain mapping tool in the intensive care setting. This application has the potential to provide clinicians with an additional dimension of information to manage

  9. Eletrencefalograma digital com mapeamento em demência de Alzheimer e doença de Parkinson: estudo prospectivo controlado Digital EEG with brain mapping in Alzheimer's dementia and Parkinson's disease: a prospective controlled study

    Directory of Open Access Journals (Sweden)

    Marcos C. Sandmann

    1996-03-01

    Full Text Available Com o intuito de estudar a atividade eletrencefalográfica em vigilia da demência senil de tipo Alzheimer (DA e da doença de Parkinson (DP foi iniciado estudo prospectivo e controlado. Foram comparados 6 pacientes com DA e 11 pacientes com DP, com um grupo controle composto por 12 pacientes com depressão maior crônica leve a moderada (DSM-III-R, 1987. Nos três grupos, através de análise espectral, foi obtida a mediana da frequência da energia da atividade dominante posterior. O grupo controle apresentou atividade posterior com frequência de 8,79 ± 0,52 (m±dp. No grupo com DA este valor foi 6,65 ± 0,80 (m±dp e no grupo com DP 7,69± 1,39 (m±dp. A hipótese experimental de que pacientes com DA e DP diferem dos controles em relação à atividade de fundo (definida como anormal sendo In order to evaluate the EEG activity during wakefulness in senile dementia of the Alzheimer type (AD and Parkinson's disease (PD, a prospective controlled study was performed. We compared 6 AD and 11 PD patients with a control group of 12 patients with mild to moderate major chronic depression (DSM-III-R 1987. The median of the frequencies and the power of the posterior dominant activity was obtained in the three groups using spectral analysis. The posterior activity had a frequency of 8.79±0.52 (mean±sd in the contrc group, 6.65+0.80 (mean+sd in the AD group and 7.69+1.39 (mean±sd in the PD group. The experimental hypothesis that patients with AD and PD differ from controls in relation to the background activity (defined a abnormal <8 was confirmed by the chi square test (p=0.0l and the t test showed that the mean of the frequency of the posterior power was significantly lower in AD (p=0.01 and PD (p=0.05 patients, compared with the controls. The results indicate that this abnormality could be correlated with the degree of cortical damage and natural history of these disorders.

  10. Human brain networks in physiological aging: a graph theoretical analysis of cortical connectivity from EEG data.

    Science.gov (United States)

    Vecchio, Fabrizio; Miraglia, Francesca; Bramanti, Placido; Rossini, Paolo Maria

    2014-01-01

    Modern analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity. To test the hypothesis that aging processes modulate the brain connectivity network, EEG recording was conducted on 113 healthy volunteers. They were divided into three groups in accordance with their ages: 36 Young (15-45 years), 46 Adult (50-70 years), and 31 Elderly (>70 years). To evaluate the stability of the investigated parameters, a subgroup of 10 subjects underwent a second EEG recording two weeks later. Graph theory functions were applied to the undirected and weighted networks obtained by the lagged linear coherence evaluated by eLORETA on cortical sources. EEG frequency bands of interest were: delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz). The spectral connectivity analysis of cortical sources showed that the normalized Characteristic Path Length (λ) presented the pattern Young > Adult>Elderly in the higher alpha band. Elderly also showed a greater increase in delta and theta bands than Young. The correlation between age and λ showed that higher ages corresponded to higher λ in delta and theta and lower in the alpha2 band; this pattern reflects the age-related modulation of higher (alpha) and decreased (delta) connectivity. The Normalized Clustering coefficient (γ) and small-world network modeling (σ) showed non-significant age-modulation. Evidence from the present study suggests that graph theory can aid in the analysis of connectivity patterns estimated from EEG and can facilitate the study of the physiological and pathological brain aging features of functional connectivity networks.

  11. Quantum neural network-based EEG filtering for a brain-computer interface.

    Science.gov (United States)

    Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

    2014-02-01

    A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

  12. Rapid prototyping of an EEG-based brain-computer interface (BCI).

    Science.gov (United States)

    Guger, C; Schlögl, A; Neuper, C; Walterspacher, D; Strein, T; Pfurtscheller, G

    2001-03-01

    The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional digital signal processor (DSP) board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive (AAR) model and linear discriminant analysis (LDA).

  13. Analysis of a human brain transcriptome map

    Directory of Open Access Journals (Sweden)

    Greene Jonathan R

    2002-04-01

    Full Text Available Abstract Background Genome wide transcriptome maps can provide tools to identify candidate genes that are over-expressed or silenced in certain disease tissue and increase our understanding of the structure and organization of the genome. Expressed Sequence Tags (ESTs from the public dbEST and proprietary Incyte LifeSeq databases were used to derive a transcript map in conjunction with the working draft assembly of the human genome sequence. Results Examination of ESTs derived from brain tissues (excluding brain tumor tissues suggests that these genes are distributed on chromosomes in a non-random fashion. Some regions on the genome are dense with brain-enriched genes while some regions lack brain-enriched genes, suggesting a significant correlation between distribution of genes along the chromosome and tissue type. ESTs from brain tumor tissues have also been mapped to the human genome working draft. We reveal that some regions enriched in brain genes show a significant decrease in gene expression in brain tumors, and, conversely that some regions lacking in brain genes show an increased level of gene expression in brain tumors. Conclusions This report demonstrates a novel approach for tissue specific transcriptome mapping using EST-based quantitative assessment.

  14. Mining Time-Resolved Functional Brain Graphs to an EEG-Based Chronnectomic Brain Aged Index (CBAI

    Directory of Open Access Journals (Sweden)

    Stavros I. Dimitriadis

    2017-09-01

    Full Text Available The brain at rest consists of spatially and temporal distributed but functionally connected regions that called intrinsic connectivity networks (ICNs. Resting state electroencephalography (rs-EEG is a way to characterize brain networks without confounds associated with task EEG such as task difficulty and performance. A novel framework of how to study dynamic functional connectivity under the notion of functional connectivity microstates (FCμstates and symbolic dynamics is further discussed. Furthermore, we introduced a way to construct a single integrated dynamic functional connectivity graph (IDFCG that preserves both the strength of the connections between every pair of sensors but also the type of dominant intrinsic coupling modes (DICM. The whole methodology is demonstrated in a significant and unexplored task for EEG which is the definition of an objective Chronnectomic Brain Aged index (CBAI extracted from resting-state data (N = 94 subjects with both eyes-open and eyes-closed conditions. Novel features have been defined based on symbolic dynamics and the notion of DICM and FCμstates. The transition rate of FCμstates, the symbolic dynamics based on the evolution of FCμstates (the Markovian Entropy, the complexity index, the probability distribution of DICM, the novel Flexibility Index that captures the dynamic reconfiguration of DICM per pair of EEG sensors and the relative signal power constitute a valuable pool of features that can build the proposed CBAI. Here we applied a feature selection technique and Extreme Learning Machine (ELM classifier to discriminate young adults from middle-aged and a Support Vector Regressor to build a linear model of the actual age based on EEG-based spatio-temporal features. The most significant type of features for both prediction of age and discrimination of young vs. adults age groups was the dynamic reconfiguration of dominant coupling modes derived from a subset of EEG sensor pairs. Specifically

  15. 123I-iomazenil brain receptor SPECT in focal epilepsy. In comparison with 99mTc-HMPAO brain SPECT, MRI and Video/EEG monitoring

    International Nuclear Information System (INIS)

    Xu Hao; Wang Tongge; Huang Li; Michael Cordes

    1998-01-01

    Purpose: To evaluate the clinical value of 123 I-Iomazenil brain receptor SPECT in diagnosis of focal epilepsy in comparison with 99m Tc-HMPAO brain SPECT, MRI and Video/EEG monitoring. Methods 123 I-Iomazenil brain receptor SPECT was performed on 40 patients with focal epilepsy. The results were compared with those obtained by 99m Tc-HMPAO brain SPECT, MRI and Video/EEG monitoring. Results: In 40 patients, the sensitivity of Video/EEG monitoring for localization of epileptogenic area was 95% (38/40). The sensitivity of 123 I-iomazenil brain receptor SPECT, 99m Tc-HMPAO brain SPECT and MRI for localization of epileptogenic area compared with Video/EEG monitoring ('gold standard') was 65.8%(25/38), 55.3%(21/38) and 47.4%(18/38), respectively. The localization of epileptogenic area with 123 I-Iomazenil brain receptor SPECT was in concordance with Video/EEG monitoring in 20 patients, 99m Tc-HMPAO brain SPECT in 15 patients and MRI in 16 patients, respectively. The sensitivity of 123 I-Iomazenil brain receptor SPECT combined with MRI for localization of epileptogenic area was 84.2%(32/38). Conclusions: 123 I-Iomazenil brain receptor SPECT is a useful method in detecting and localizing epileptogenic area. The combination of 123 I-Iomazenil brain receptor SPECT and MRI has a high sensitivity for detecting epileptogenic area

  16. Analysis of EEG activity during sleep - brain hemisphere symmetry of two classes of sleep spindles

    Science.gov (United States)

    Smolen, Magdalena M.

    2009-01-01

    This paper presents automatic analysis of some selected human electroencephalographic patterns during deep sleep using the Matching Pursuit (MP) algorithm. The periodicity of deep sleep EEG patterns was observed by calculating autocorrelation functions of their percentage contributions. The study confirmed the increasing trend of amplitude-weighted average frequency of sleep spindles from frontal to posterior derivations. The dominant frequencies from the left and the right brain hemisphere were strongly correlated.

  17. Mapping the Alzheimer's brain with connectomics

    Directory of Open Access Journals (Sweden)

    Teng eXie

    2012-01-01

    Full Text Available Alzheimer’s disease (AD is the most common form of dementia. As an incurable, progressive and neurodegenerative disease, it causes cognitive and memory deficits. However, the biological mechanisms underlying the disease are not thoroughly understood. In recent years, non-invasive neuroimaging and neurophysiological techniques (e.g., structural MRI, diffusion MRI, functional MRI and EEG/MEG and graph theory based network analysis have provided a new perspective on structural and functional connectivity patterns of the human brain (i.e., the human connectome in health and disease. Using these powerful approaches, several recent studies of patients with AD exhibited abnormal topological organization in both global and regional properties of neuronal networks, indicating that AD not only affects specific brain regions, but also alters the structural and functional associations between distinct brain regions. Specifically, disruptive organization in the whole-brain networks in AD is involved in the loss of small-world characters and the re-organization of hub distributions. These aberrant neuronal connectivity patterns were associated with cognitive deficits in patients with AD, even with genetic factors in healthy aging. These studies provide empirical evidence to support the existence of an aberrant connectome of AD. In this review we will summarize recent advances discovered in large-scale brain network studies of AD, mainly focusing on graph theoretical analysis of brain connectivity abnormalities. These studies provide novel insights into the pathophysiological mechanisms of AD and could be helpful in developing imaging biomarkers for disease diagnosis and monitoring.

  18. Study of heart-brain interactions through EEG, ECG, and emotions

    Science.gov (United States)

    Ramasamy, Mouli; Varadan, Vijay K.

    2017-04-01

    Neurocardiology is the exploration of neurophysiological, neurological and neuroanatomical facets of neuroscience's influence in cardiology. The paraphernalia of emotions on the heart and brain are premeditated because of the interaction between the central and peripheral nervous system. This is an investigative attempt to study emotion based neurocardiology and the factors that influence this phenomenon. The factors include: interaction between sleep EEG (electroencephalogram) and ECG (electrocardiogram), relationship between emotion and music, psychophysiological coherence between the heart and brain, emotion recognition techniques, and biofeedback mechanisms. Emotions contribute vitally to the mundane life and are quintessential to a numerous biological and everyday-functional modality of a human being. Emotions are best represented through EEG signals, and to a certain extent, can be observed through ECG and body temperature. Confluence of medical and engineering science has enabled the monitoring and discrimination of emotions influenced by happiness, anxiety, distress, excitement and several other factors that influence the thinking patterns and the electrical activity of the brain. Similarly, HRV (Heart Rate Variability) widely investigated for its provision and discerning characteristics towards EEG and the perception in neurocardiology.

  19. EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

    Science.gov (United States)

    Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

    2016-01-01

    Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI.

  20. Using EEG/MEG Data of Cognitive Processes in Brain-Computer Interfaces

    International Nuclear Information System (INIS)

    Gutierrez, David

    2008-01-01

    Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using electroencephalographic (EEG) and, more recently, magnetoencephalographic (MEG) measurements of the brain function. Most of the current implementations of BCIs rely on EEG/MEG data of motor activities as such neural processes are well characterized, while the use of data related to cognitive activities has been neglected due to its intrinsic complexity. However, cognitive data usually has larger amplitude, lasts longer and, in some cases, cognitive brain signals are easier to control at will than motor signals. This paper briefy reviews the use of EEG/MEG data of cognitive processes in the implementation of BCIs. Specifically, this paper reviews some of the neuromechanisms, signal features, and processing methods involved. This paper also refers to some of the author's work in the area of detection and classifcation of cognitive signals for BCIs using variability enhancement, parametric modeling, and spatial fltering, as well as recent developments in BCI performance evaluation

  1. Body Maps in the Infant Brain

    Science.gov (United States)

    Marshall, Peter J.; Meltzoff, Andrew N.

    2015-01-01

    Researchers have examined representations of the body in the adult brain, but relatively little attention has been paid to ontogenetic aspects of neural body maps in human infants. Novel applications of methods for recording brain activity in infants are delineating cortical body maps in the first months of life. Body maps may facilitate infants’ registration of similarities between self and other—an ability that is foundational to developing social cognition. Alterations in interpersonal aspects of body representations might also contribute to social deficits in certain neurodevelopmental disorders. PMID:26231760

  2. Time is of the Essence: A Review of Electroencephalography (EEG) and Event-Related Brain Potentials (ERPs) in Language Research.

    Science.gov (United States)

    Beres, Anna M

    2017-12-01

    The discovery of electroencephalography (EEG) over a century ago has changed the way we understand brain structure and function, in terms of both clinical and research applications. This paper starts with a short description of EEG and then focuses on the event-related brain potentials (ERPs), and their use in experimental settings. It describes the typical set-up of an ERP experiment. A description of a number of ERP components typically involved in language research is presented. Finally, the advantages and disadvantages of using ERPs in language research are discussed. EEG has an extensive use in today's world, including medical, psychology, or linguistic research. The excellent temporal resolution of EEG information allows one to track a brain response in milliseconds and therefore makes it uniquely suited to research concerning language processing.

  3. Bayesian learning for spatial filtering in an EEG-based brain-computer interface.

    Science.gov (United States)

    Zhang, Haihong; Yang, Huijuan; Guan, Cuntai

    2013-07-01

    Spatial filtering for EEG feature extraction and classification is an important tool in brain-computer interface. However, there is generally no established theory that links spatial filtering directly to Bayes classification error. To address this issue, this paper proposes and studies a Bayesian analysis theory for spatial filtering in relation to Bayes error. Following the maximum entropy principle, we introduce a gamma probability model for describing single-trial EEG power features. We then formulate and analyze the theoretical relationship between Bayes classification error and the so-called Rayleigh quotient, which is a function of spatial filters and basically measures the ratio in power features between two classes. This paper also reports our extensive study that examines the theory and its use in classification, using three publicly available EEG data sets and state-of-the-art spatial filtering techniques and various classifiers. Specifically, we validate the positive relationship between Bayes error and Rayleigh quotient in real EEG power features. Finally, we demonstrate that the Bayes error can be practically reduced by applying a new spatial filter with lower Rayleigh quotient.

  4. On the use of EEG or MEG brain imaging tools in neuromarketing research.

    Science.gov (United States)

    Vecchiato, Giovanni; Astolfi, Laura; De Vico Fallani, Fabrizio; Toppi, Jlenia; Aloise, Fabio; Bez, Francesco; Wei, Daming; Kong, Wanzeng; Dai, Jounging; Cincotti, Febo; Mattia, Donatella; Babiloni, Fabio

    2011-01-01

    Here we present an overview of some published papers of interest for the marketing research employing electroencephalogram (EEG) and magnetoencephalogram (MEG) methods. The interest for these methodologies relies in their high-temporal resolution as opposed to the investigation of such problem with the functional Magnetic Resonance Imaging (fMRI) methodology, also largely used in the marketing research. In addition, EEG and MEG technologies have greatly improved their spatial resolution in the last decades with the introduction of advanced signal processing methodologies. By presenting data gathered through MEG and high resolution EEG we will show which kind of information it is possible to gather with these methodologies while the persons are watching marketing relevant stimuli. Such information will be related to the memorization and pleasantness related to such stimuli. We noted that temporal and frequency patterns of brain signals are able to provide possible descriptors conveying information about the cognitive and emotional processes in subjects observing commercial advertisements. These information could be unobtainable through common tools used in standard marketing research. We also show an example of how an EEG methodology could be used to analyze cultural differences between fruition of video commercials of carbonated beverages in Western and Eastern countries.

  5. On the Use of EEG or MEG Brain Imaging Tools in Neuromarketing Research

    Directory of Open Access Journals (Sweden)

    Giovanni Vecchiato

    2011-01-01

    Full Text Available Here we present an overview of some published papers of interest for the marketing research employing electroencephalogram (EEG and magnetoencephalogram (MEG methods. The interest for these methodologies relies in their high-temporal resolution as opposed to the investigation of such problem with the functional Magnetic Resonance Imaging (fMRI methodology, also largely used in the marketing research. In addition, EEG and MEG technologies have greatly improved their spatial resolution in the last decades with the introduction of advanced signal processing methodologies. By presenting data gathered through MEG and high resolution EEG we will show which kind of information it is possible to gather with these methodologies while the persons are watching marketing relevant stimuli. Such information will be related to the memorization and pleasantness related to such stimuli. We noted that temporal and frequency patterns of brain signals are able to provide possible descriptors conveying information about the cognitive and emotional processes in subjects observing commercial advertisements. These information could be unobtainable through common tools used in standard marketing research. We also show an example of how an EEG methodology could be used to analyze cultural differences between fruition of video commercials of carbonated beverages in Western and Eastern countries.

  6. EEG potentials associated with artificial grammar learning in the primate brain.

    Science.gov (United States)

    Attaheri, Adam; Kikuchi, Yukiko; Milne, Alice E; Wilson, Benjamin; Alter, Kai; Petkov, Christopher I

    2015-09-01

    Electroencephalography (EEG) has identified human brain potentials elicited by Artificial Grammar (AG) learning paradigms, which present participants with rule-based sequences of stimuli. Nonhuman animals are sensitive to certain AGs; therefore, evaluating which EEG Event Related Potentials (ERPs) are associated with AG learning in nonhuman animals could identify evolutionarily conserved processes. We recorded EEG potentials during an auditory AG learning experiment in two Rhesus macaques. The animals were first exposed to sequences of nonsense words generated by the AG. Then surface-based ERPs were recorded in response to sequences that were 'consistent' with the AG and 'violation' sequences containing illegal transitions. The AG violations strongly modulated an early component, potentially homologous to the Mismatch Negativity (mMMN), a P200 and a late frontal positivity (P500). The macaque P500 is similar in polarity and time of occurrence to a late EEG positivity reported in human AG learning studies but might differ in functional role. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  7. Reduction in time-to-sleep through EEG based brain state detection and audio stimulation.

    Science.gov (United States)

    Zhuo Zhang; Cuntai Guan; Ti Eu Chan; Juanhong Yu; Aung Aung Phyo Wai; Chuanchu Wang; Haihong Zhang

    2015-08-01

    We developed an EEG- and audio-based sleep sensing and enhancing system, called iSleep (interactive Sleep enhancement apparatus). The system adopts a closed-loop approach which optimizes the audio recording selection based on user's sleep status detected through our online EEG computing algorithm. The iSleep prototype comprises two major parts: 1) a sleeping mask integrated with a single channel EEG electrode and amplifier, a pair of stereo earphones and a microcontroller with wireless circuit for control and data streaming; 2) a mobile app to receive EEG signals for online sleep monitoring and audio playback control. In this study we attempt to validate our hypothesis that appropriate audio stimulation in relation to brain state can induce faster onset of sleep and improve the quality of a nap. We conduct experiments on 28 healthy subjects, each undergoing two nap sessions - one with a quiet background and one with our audio-stimulation. We compare the time-to-sleep in both sessions between two groups of subjects, e.g., fast and slow sleep onset groups. The p-value obtained from Wilcoxon Signed Rank Test is 1.22e-04 for slow onset group, which demonstrates that iSleep can significantly reduce the time-to-sleep for people with difficulty in falling sleep.

  8. [EEG markers of spontaneous recovery of vertical posture in patients with consequences of severe traumatic brain injury].

    Science.gov (United States)

    Zhavoronkova, L A; Zharikova, A V; Maksakova, O A

    2014-01-01

    9 patients (mean age 23.6 +/- 3.15 y.o.) with severe traumatic brain injury (TBI) and impairment of vertical posture were included in complex clinical and EEG study during spontaneous recovery of vertical posture (VP). Patients were included in three different groups according to severity of deficit according to MPAI, FIM and MMSE scales. EEG data have been compared to those of 10 healthy volunteers (mean age 22.8 +/- 0.67 yo.). In patients with moderate brain impairment and fast recovery of VP (over 2 weeks) change of posture from sitting to standup has been accompanied by EEG-signs similar to those of healthy people. These included predominant increase of coherence in right hemisphere for majority of frequency bands, although in more complex conditions EEG of these patients showed pathological signs. In patients with more severe deficit spontaneous recovery of VP has been accompanied by "hyper-reactive" change of EEG for all frequency bands without local specificity. This finding didn't depend on side ofbrain impairment and could be considered as marker of positive dynamics of VP restoration. In patients with most severe brain impairment and deficit of functions VP didn't recover after 3 month of observation. EEG-investigation has revealed absence of reactive change of EEG during passive verticalisation. This finding can be used as marker of negative prognosis.

  9. Comparative analysis of brain EEG signals generated from the right and left hand while writing

    Science.gov (United States)

    Sardesai, Neha; Jamali Mahabadi, S. E.; Meng, Qinglei; Choa, Fow-Sen

    2016-05-01

    This paper provides a comparative analysis of right handed people and left handed people when they write with both their hands. Two left handed and one right handed subject were asked to write their respective names on a paper using both, their left and right handed, and their brain signals were measured using EEG. Similarly, they were asked to perform simple mathematical calculations using both their hand. The data collected from the EEG from writing with both hands is compared. It is observed that though it is expected that the right brain only would contribute to left handed writing and vice versa, it is not so. When a right handed person writes with his/her left hand, the initial instinct is to go for writing with the right hand. Hence, both parts of the brain are active when a subject writes with the other hand. However, when the activity is repeated, the brain learns to expect to write with the other hand as the activity is repeated and then only the expected part of the brain is active.

  10. Study on Brain Dynamics by Non Linear Analysis of Music Induced EEG Signals

    Science.gov (United States)

    Banerjee, Archi; Sanyal, Shankha; Patranabis, Anirban; Banerjee, Kaushik; Guhathakurta, Tarit; Sengupta, Ranjan; Ghosh, Dipak; Ghose, Partha

    2016-02-01

    Music has been proven to be a valuable tool for the understanding of human cognition, human emotion, and their underlying brain mechanisms. The objective of this study is to analyze the effect of Hindustani music on brain activity during normal relaxing conditions using electroencephalography (EEG). Ten male healthy subjects without special musical education participated in the study. EEG signals were acquired at the frontal (F3/F4) lobes of the brain while listening to music at three experimental conditions (rest, with music and without music). Frequency analysis was done for the alpha, theta and gamma brain rhythms. The finding shows that arousal based activities were enhanced while listening to Hindustani music of contrasting emotions (romantic/sorrow) for all the subjects in case of alpha frequency bands while no significant changes were observed in gamma and theta frequency ranges. It has been observed that when the music stimulus is removed, arousal activities as evident from alpha brain rhythms remain for some time, showing residual arousal. This is analogous to the conventional 'Hysteresis' loop where the system retains some 'memory' of the former state. This is corroborated in the non linear analysis (Detrended Fluctuation Analysis) of the alpha rhythms as manifested in values of fractal dimension. After an input of music conveying contrast emotions, withdrawal of music shows more retention as evidenced by the values of fractal dimension.

  11. Hearing the Sound in the Brain: Influences of Different EEG References

    Directory of Open Access Journals (Sweden)

    Dan Wu

    2018-03-01

    Full Text Available If the scalp potential signals, the electroencephalogram (EEG, are due to neural “singers” in the brain, how could we listen to them with less distortion? One crucial point is that the data recording on the scalp should be faithful and accurate, thus the choice of reference electrode is a vital factor determining the faithfulness of the data. In this study, music on the scalp derived from data in the brain using three different reference electrodes were compared, including approximate zero reference—reference electrode standardization technique (REST, average reference (AR, and linked mastoids reference (LM. The classic music pieces in waveform format were used as simulated sources inside a head model, and they were forward calculated to scalp as standard potential recordings, i.e., waveform format music from the brain with true zero reference. Then these scalp music was re-referenced into REST, AR, and LM based data, and compared with the original forward data (true zero reference. For real data, the EEG recorded in an orthodontic pain control experiment were utilized for music generation with the three references, and the scale free index (SFI of these music pieces were compared. The results showed that in the simulation for only one source, different references do not change the music/waveform; for two sources or more, REST provide the most faithful music/waveform to the original ones inside the brain, and the distortions caused by AR and LM were spatial locations of both source and scalp electrode dependent. The brainwave music from the real EEG data showed that REST and AR make the differences of SFI between two states more recognized and found the frontal is the main region that producing the music. In conclusion, REST can reconstruct the true signals approximately, and it can be used to help to listen to the true voice of the neural singers in the brain.

  12. Whole-brain activity mapping onto a zebrafish brain atlas

    Science.gov (United States)

    Randlett, Owen; Wee, Caroline L.; Naumann, Eva A.; Nnaemeka, Onyeka; Schoppik, David; Fitzgerald, James E.; Portugues, Ruben; Lacoste, Alix M.B.; Riegler, Clemens; Engert, Florian; Schier, Alexander F.

    2015-01-01

    In order to localize the neural circuits involved in generating behaviors, it is necessary to assign activity onto anatomical maps of the nervous system. Using brain registration across hundreds of larval zebrafish, we have built an expandable open source atlas containing molecular labels and anatomical region definitions, the Z-Brain. Using this platform and immunohistochemical detection of phosphorylated-Extracellular signal-regulated kinase (ERK/MAPK) as a readout of neural activity, we have developed a system to create and contextualize whole brain maps of stimulus- and behavior-dependent neural activity. This MAP-Mapping (Mitogen Activated Protein kinase – Mapping) assay is technically simple, fast, inexpensive, and data analysis is completely automated. Since MAP-Mapping is performed on fish that are freely swimming, it is applicable to nearly any stimulus or behavior. We demonstrate the utility of our high-throughput approach using hunting/feeding, pharmacological, visual and noxious stimuli. The resultant maps outline hundreds of areas associated with behaviors. PMID:26778924

  13. Intracranial EEG fluctuates over months after implanting electrodes in human brain

    Science.gov (United States)

    Ung, Hoameng; Baldassano, Steven N.; Bink, Hank; Krieger, Abba M.; Williams, Shawniqua; Vitale, Flavia; Wu, Chengyuan; Freestone, Dean; Nurse, Ewan; Leyde, Kent; Davis, Kathryn A.; Cook, Mark; Litt, Brian

    2017-10-01

    Objective. Implanting subdural and penetrating electrodes in the brain causes acute trauma and inflammation that affect intracranial electroencephalographic (iEEG) recordings. This behavior and its potential impact on clinical decision-making and algorithms for implanted devices have not been assessed in detail. In this study we aim to characterize the temporal and spatial variability of continuous, prolonged human iEEG recordings. Approach. Intracranial electroencephalography from 15 patients with drug-refractory epilepsy, each implanted with 16 subdural electrodes and continuously monitored for an average of 18 months, was included in this study. Time and spectral domain features were computed each day for each channel for the duration of each patient’s recording. Metrics to capture post-implantation feature changes and inflexion points were computed on group and individual levels. A linear mixed model was used to characterize transient group-level changes in feature values post-implantation and independent linear models were used to describe individual variability. Main results. A significant decline in features important to seizure detection and prediction algorithms (mean line length, energy, and half-wave), as well as mean power in the Berger and high gamma bands, was observed in many patients over 100 d following implantation. In addition, spatial variability across electrodes declines post-implantation following a similar timeframe. All selected features decreased by 14-50% in the initial 75 d of recording on the group level, and at least one feature demonstrated this pattern in 13 of the 15 patients. Our findings indicate that iEEG signal features demonstrate increased variability following implantation, most notably in the weeks immediately post-implant. Significance. These findings suggest that conclusions drawn from iEEG, both clinically and for research, should account for spatiotemporal signal variability and that properly assessing the iEEG in

  14. Design of a 32-channel EEG system for brain control interface applications.

    Science.gov (United States)

    Wang, Ching-Sung

    2012-01-01

    This study integrates the hardware circuit design and the development support of the software interface to achieve a 32-channel EEG system for BCI applications. Since the EEG signals of human bodies are generally very weak, in addition to preventing noise interference, it also requires avoiding the waveform distortion as well as waveform offset and so on; therefore, the design of a preamplifier with high common-mode rejection ratio and high signal-to-noise ratio is very important. Moreover, the friction between the electrode pads and the skin as well as the design of dual power supply will generate DC bias which affects the measurement signals. For this reason, this study specially designs an improved single-power AC-coupled circuit, which effectively reduces the DC bias and improves the error caused by the effects of part errors. At the same time, the digital way is applied to design the adjustable amplification and filter function, which can design for different EEG frequency bands. For the analog circuit, a frequency band will be taken out through the filtering circuit and then the digital filtering design will be used to adjust the extracted frequency band for the target frequency band, combining with MATLAB to design man-machine interface for displaying brain wave. Finally the measured signals are compared to the traditional 32-channel EEG signals. In addition to meeting the IFCN standards, the system design also conducted measurement verification in the standard EEG isolation room in order to demonstrate the accuracy and reliability of this system design.

  15. BrainMap '95 workshop. Final report

    International Nuclear Information System (INIS)

    1995-01-01

    The fourth annual BrainMap workshop was held at La Mansion del Rio Hotel in San Antonio December 3--4, 1995. The conference title was ''Human Brain Mapping and Modeling.'' The meeting was attended by 137 registered participants and 30 observers from 82 institutions representing 12 countries. The meeting focused on the technical issues associated with brain mapping and modeling. A total of 23 papers were presented covering the following topics: spatial normalization and registration; functional image analysis; metanalysis and modeling; and new horizons in biological databases. The full program with abstracts was available on the Research Imaging Center's web site. A book will be published by John Wiley and Sons prior to the end of 1998

  16. Whole-brain activity mapping onto a zebrafish brain atlas.

    Science.gov (United States)

    Randlett, Owen; Wee, Caroline L; Naumann, Eva A; Nnaemeka, Onyeka; Schoppik, David; Fitzgerald, James E; Portugues, Ruben; Lacoste, Alix M B; Riegler, Clemens; Engert, Florian; Schier, Alexander F

    2015-11-01

    In order to localize the neural circuits involved in generating behaviors, it is necessary to assign activity onto anatomical maps of the nervous system. Using brain registration across hundreds of larval zebrafish, we have built an expandable open-source atlas containing molecular labels and definitions of anatomical regions, the Z-Brain. Using this platform and immunohistochemical detection of phosphorylated extracellular signal–regulated kinase (ERK) as a readout of neural activity, we have developed a system to create and contextualize whole-brain maps of stimulus- and behavior-dependent neural activity. This mitogen-activated protein kinase (MAP)-mapping assay is technically simple, and data analysis is completely automated. Because MAP-mapping is performed on freely swimming fish, it is applicable to studies of nearly any stimulus or behavior. Here we demonstrate our high-throughput approach using pharmacological, visual and noxious stimuli, as well as hunting and feeding. The resultant maps outline hundreds of areas associated with behaviors.

  17. Brain wave correlates of attentional states: Event related potentials and quantitative EEG analysis during performance of cognitive and perceptual tasks

    Science.gov (United States)

    Freeman, Frederick G.

    1993-01-01

    presented target stimulus. In addition to the task requirements, irrelevant tones were presented in the background. Research has shown that even though these stimuli are not attended, ERP's to them can still be elicited. The amplitude of the ERP waves has been shown to change as a function of a person's level of alertness. ERP's were also collected and analyzed for the target stimuli for each task. Brain maps were produced based on the ERP voltages for the different stimuli. In addition to the ERP's, a quantitative EEG (QEEG) was performed on the data using a fast Fourier technique to produce a power spectral analysis of the EEG. This analysis was conducted on the continuous EEG while the subjects were performing the tasks. Finally, a QEEG was performed on periods during the task when subjects indicated that they were in an altered state of awareness. During the tasks, subjects were asked to indicate by pressing a button when they realized their level of task awareness had changed. EEG epochs were collected for times just before and just after subjects made this reponse. The purpose of this final analysis was to determine whether or not subjective indices of level of awareness could be correlated with different patterns of EEG.

  18. Mining multi-channel EEG for its information content: An ANN-based method for a brain-computer interface

    DEFF Research Database (Denmark)

    Peters, B.O.; Pfurtscheller, G.; Flyvbjerg, H.

    1998-01-01

    . This high recognition rate makes the classifier suitable for a so-called 'Brain-Computer Interface', a system that allows one to control a computer, or another device, with ones brain waves. Our classifier Laplace filters the EEG spatially, but makes use of its entire frequency range, and automatically...

  19. A Review of EEG-Based Brain-Computer Interfaces as Access Pathways for Individuals with Severe Disabilities

    Science.gov (United States)

    Moghimi, Saba; Kushki, Azadeh; Guerguerian, Anne Marie; Chau, Tom

    2013-01-01

    Electroencephalography (EEG) is a non-invasive method for measuring brain activity and is a strong candidate for brain-computer interface (BCI) development. While BCIs can be used as a means of communication for individuals with severe disabilities, the majority of existing studies have reported BCI evaluations by able-bodied individuals.…

  20. EEG (Electroencephalogram)

    Science.gov (United States)

    ... in diagnosing brain disorders, especially epilepsy or another seizure disorder. An EEG might also be helpful for diagnosing ... Sometimes seizures are intentionally triggered in people with epilepsy during the test, but appropriate medical care is ...

  1. Brain-mapping projects using the common marmoset.

    Science.gov (United States)

    Okano, Hideyuki; Mitra, Partha

    2015-04-01

    Globally, there is an increasing interest in brain-mapping projects, including the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative project in the USA, the Human Brain Project (HBP) in Europe, and the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) project in Japan. These projects aim to map the structure and function of neuronal circuits to ultimately understand the vast complexity of the human brain. Brain/MINDS is focused on structural and functional mapping of the common marmoset (Callithrix jacchus) brain. This non-human primate has numerous advantages for brain mapping, including a well-developed frontal cortex and a compact brain size, as well as the availability of transgenic technologies. In the present review article, we discuss strategies for structural and functional mapping of the marmoset brain and the relation of the common marmoset to other animals models. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  2. Cross-sensory gating in schizophrenia and autism spectrum disorder: EEG evidence for impaired brain connectivity?

    DEFF Research Database (Denmark)

    Magnée, Maurice J C M; Oranje, Bob; van Engeland, Herman

    2009-01-01

    activation, which provides crucial information about functional integrity of connections between brain areas involved in cross-sensory processing in both disorders. Thirteen high functioning adult males with ASD, 13 high functioning adult males with schizophrenia, and 16 healthy adult males participated...... with the notion that filtering deficits may be secondary to earlier sensory dysfunction. Also, atypical cross-sensory suppression was found, which implies that the cognitive impairments seen in schizophrenia may be due to deficits in the integrity of connections between brain areas involved in low-level cross-sensory......Autism spectrum disorders (ASD) and schizophrenia are both neurodevelopmental disorders that have extensively been associated with impairments in functional brain connectivity. Using a cross-sensory P50 suppression paradigm, this study investigated low-level audiovisual interactions on cortical EEG...

  3. EEG classification of emotions using emotion-specific brain functional network.

    Science.gov (United States)

    Gonuguntla, V; Shafiq, G; Wang, Y; Veluvolu, K C

    2015-08-01

    The brain functional network perspective forms the basis to relate mechanisms of brain functions. This work analyzes the network mechanisms related to human emotion based on synchronization measure - phase-locking value in EEG to formulate the emotion specific brain functional network. Based on network dissimilarities between emotion and rest tasks, most reactive channel pairs and the reactive band corresponding to emotions are identified. With the identified most reactive pairs, the subject-specific functional network is formed. The identified subject-specific and emotion-specific dynamic network pattern show significant synchrony variation in line with the experiment protocol. The same network pattern are then employed for classification of emotions. With the study conducted on the 4 subjects, an average classification accuracy of 62 % was obtained with the proposed technique.

  4. Parameterized entropy analysis of EEG following hypoxic-ischemic brain injury

    International Nuclear Information System (INIS)

    Tong Shanbao; Bezerianos, Anastasios; Malhotra, Amit; Zhu Yisheng; Thakor, Nitish

    2003-01-01

    In the present study Tsallis and Renyi entropy methods were used to study the electric activity of brain following hypoxic-ischemic (HI) injury. We investigated the performances of these parameterized information measures in describing the electroencephalogram (EEG) signal of controlled experimental animal HI injury. The results show that (a): compared with Shannon and Renyi entropy, the parameterized Tsallis entropy acts like a spatial filter and the information rate can either tune to long range rhythms or to short abrupt changes, such as bursts or spikes during the beginning of recovery, by the entropic index q; (b): Renyi entropy is a compact and predictive indicator for monitoring the physiological changes during the recovery of brain injury. There is a reduction in the Renyi entropy after brain injury followed by a gradual recovery upon resuscitation

  5. Confused or not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks.

    Science.gov (United States)

    Ni, Zhaoheng; Yuksel, Ahmet Cem; Ni, Xiuyan; Mandel, Michael I; Xie, Lei

    2017-08-01

    Brain fog, also known as confusion, is one of the main reasons for low performance in the learning process or any kind of daily task that involves and requires thinking. Detecting confusion in a human's mind in real time is a challenging and important task that can be applied to online education, driver fatigue detection and so on. In this paper, we apply Bidirectional LSTM Recurrent Neural Networks to classify students' confusion in watching online course videos from EEG data. The results show that Bidirectional LSTM model achieves the state-of-the-art performance compared with other machine learning approaches, and shows strong robustness as evaluated by cross-validation. We can predict whether or not a student is confused in the accuracy of 73.3%. Furthermore, we find the most important feature to detecting the brain confusion is the gamma 1 wave of EEG signal. Our results suggest that machine learning is a potentially powerful tool to model and understand brain activity.

  6. Effects of Soft Drinks on Resting State EEG and Brain-Computer Interface Performance.

    Science.gov (United States)

    Meng, Jianjun; Mundahl, John; Streitz, Taylor; Maile, Kaitlin; Gulachek, Nicholas; He, Jeffrey; He, Bin

    2017-01-01

    Motor imagery-based (MI based) brain-computer interface (BCI) using electroencephalography (EEG) allows users to directly control a computer or external device by modulating and decoding the brain waves. A variety of factors could potentially affect the performance of BCI such as the health status of subjects or the environment. In this study, we investigated the effects of soft drinks and regular coffee on EEG signals under resting state and on the performance of MI based BCI. Twenty-six healthy human subjects participated in three or four BCI sessions with a resting period in each session. During each session, the subjects drank an unlabeled soft drink with either sugar (Caffeine Free Coca-Cola), caffeine (Diet Coke), neither ingredient (Caffeine Free Diet Coke), or a regular coffee if there was a fourth session. The resting state spectral power in each condition was compared; the analysis showed that power in alpha and beta band after caffeine consumption were decreased substantially compared to control and sugar condition. Although the attenuation of powers in the frequency range used for the online BCI control signal was shown, group averaged BCI online performance after consuming caffeine was similar to those of other conditions. This work, for the first time, shows the effect of caffeine, sugar intake on the online BCI performance and resting state brain signal.

  7. Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications.

    Science.gov (United States)

    Shin, Younghak; Lee, Seungchan; Ahn, Minkyu; Cho, Hohyun; Jun, Sung Chan; Lee, Heung-No

    2015-11-01

    One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks.

    Directory of Open Access Journals (Sweden)

    Alessio Paolo Buccino

    Full Text Available Non-invasive Brain-Computer Interfaces (BCI have demonstrated great promise for neuroprosthetics and assistive devices. Here we aim to investigate methods to combine Electroencephalography (EEG and functional Near-Infrared Spectroscopy (fNIRS in an asynchronous Sensory Motor rhythm (SMR-based BCI. We attempted to classify 4 different executed movements, namely, Right-Arm-Left-Arm-Right-Hand-Left-Hand tasks. Previous studies demonstrated the benefit of EEG-fNIRS combination. However, since normally fNIRS hemodynamic response shows a long delay, we investigated new features, involving slope indicators, in order to immediately detect changes in the signals. Moreover, Common Spatial Patterns (CSPs have been applied to both EEG and fNIRS signals. 15 healthy subjects took part in the experiments and since 25 trials per class were available, CSPs have been regularized with information from the entire population of participants and optimized using genetic algorithms. The different features have been compared in terms of performance and the dynamic accuracy over trials shows that the introduced methods diminish the fNIRS delay in the detection of changes.

  9. Transcranial electrical currents to probe EEG brain rhythms and memory consolidation during sleep in humans.

    Directory of Open Access Journals (Sweden)

    Lisa Marshall

    Full Text Available Previously the application of a weak electric anodal current oscillating with a frequency of the sleep slow oscillation (∼0.75 Hz during non-rapid eye movement sleep (NonREM sleep boosted endogenous slow oscillation activity and enhanced sleep-associated memory consolidation. The slow oscillations occurring during NonREM sleep and theta oscillations present during REM sleep have been considered of critical relevance for memory formation. Here transcranial direct current stimulation (tDCS oscillating at 5 Hz, i.e., within the theta frequency range (theta-tDCS is applied during NonREM and REM sleep. Theta-tDCS during NonREM sleep produced a global decrease in slow oscillatory activity conjoint with a local reduction of frontal slow EEG spindle power (8-12 Hz and a decrement in consolidation of declarative memory, underlining the relevance of these cortical oscillations for sleep-dependent memory consolidation. In contrast, during REM sleep theta-tDCS appears to increase global gamma (25-45 Hz activity, indicating a clear brain state-dependency of theta-tDCS. More generally, results demonstrate the suitability of oscillating-tDCS as a tool to analyze functions of endogenous EEG rhythms and underlying endogenous electric fields as well as the interactions between EEG rhythms of different frequencies.

  10. Brain water mapping with MR imaging

    International Nuclear Information System (INIS)

    Laine, F.J.; Fatouros, P.P.; Kraft, K.A.

    1990-01-01

    This paper reports on a recently developed MR imaging technique to determine the spatial distribution of brain water to healthy volunteers. A noninvasive MR imaging technique to obtain absolute measurements of brain water has been developed and validated with phantom and animal studies. Patient confirmation was obtained from independent gravimetric measurements of brain tissue samples harvested by biopsy. This approach entails the production of accurate T1 maps from multiple inversion recovery images of a selected anatomic section and their subsequent conversion into an absolute water image by means of a previously determined calibration curve. Twenty healthy volunteers were studied and their water distribution was determined in a standard section. The following brain water values means and SD grams of water per gram of tissue) were obtained for selected brain regions; white matter, 68.9% ± 1.0; corpus callosum, 67.4% ± 1.1; thalamus, 75.3% ± 1.4; and caudate nucleus, 80.3% ± 1.4. MR imaging water mapping is a valid means of determining water content in a variety of brain tissues

  11. Postnatal development of EEG patterns, catecholamine contents and myelination, and effect of hyperthyroidism in Suncus brain.

    Science.gov (United States)

    Takeuchi, T; Sitizyo, K; Harada, E

    1998-03-01

    The postnatal development of the central nervous system (CNS) in house musk shrew in the early stage of maturation was studied. The electroencephalogram (EEG) and visual evoked potential (VEP) in association with catecholamine contents and myelin basic protein (MBP) immunoreactivity were carried out from the 1st to the 20th day of postnatal age. Different EEG patterns which were specific to behavioral states (awake and drowsy) were first recorded on the 5th day, and the total power which was obtained by power spectrum analysis increased after this stage. The latencies of all peaks in VEP markedly shortened between the 5th and the 7th day. Noradrenalin (NA) content of the brain showed a slight increase after the 3rd day, and reached maximum levels on the 7th day, which was delayed a few days compared to dopamine (DA). In hyperthyroidism, the peak latency of VEP was shortened and biosynthesis of NA in cerebral cortex and DA in hippocampus was accelerated. The most obvious change in MBP-immunoreactivity of the telencephalon occurred from the 7th to the 10th day. These morphological changes in the brain advanced at the identical time-course to those in the electrophysiological development and increment of DA and NA contents.

  12. Connectivity maps based analysis of EEG for the advanced diagnosis of schizophrenia attributes.

    Directory of Open Access Journals (Sweden)

    Zack Dvey-Aharon

    Full Text Available This article presents a novel connectivity analysis method that is suitable for multi-node networks such as EEG, MEG or EcOG electrode recordings. Its diagnostic power and ability to interpret brain states in schizophrenia is demonstrated on a set of 50 subjects that constituted of 25 healthy and 25 diagnosed with schizophrenia and treated with medication. The method can also be used for the automatic detection of schizophrenia; it exhibits higher sensitivity than state-of-the-art methods with no false positives. The detection is based on an analysis from a minute long pattern-recognition computer task. Moreover, this connectivity analysis leads naturally to an optimal choice of electrodes and hence to highly statistically significant results that are based on data from only 3-5 electrodes. The method is general and can be used for the diagnosis of other psychiatric conditions, provided an appropriate computer task is devised.

  13. Brain-computer interface on the basis of EEG system Encephalan

    Science.gov (United States)

    Maksimenko, Vladimir; Badarin, Artem; Nedaivozov, Vladimir; Kirsanov, Daniil; Hramov, Alexander

    2018-04-01

    We have proposed brain-computer interface (BCI) for the estimation of the brain response on the presented visual tasks. Proposed BCI is based on the EEG recorder Encephalan-EEGR-19/26 (Medicom MTD, Russia) supplemented by a special home-made developed acquisition software. BCI is tested during experimental session while subject is perceiving the bistable visual stimuli and classifying them according to the interpretation. We have subjected the participant to the different external conditions and observed the significant decrease in the response, associated with the perceiving the bistable visual stimuli, during the presence of distraction. Based on the obtained results we have proposed possibility to use of BCI for estimation of the human alertness during solving the tasks required substantial visual attention.

  14. A New Generation of Brain-Computer Interfaces Driven by Discovery of Latent EEG-fMRI Linkages Using Tensor Decomposition

    OpenAIRE

    Gopikrishna Deshpande; Gopikrishna Deshpande; Gopikrishna Deshpande; D. Rangaprakash; D. Rangaprakash; Luke Oeding; Andrzej Cichocki; Andrzej Cichocki; Andrzej Cichocki; Xiaoping P. Hu

    2017-01-01

    A Brain-Computer Interface (BCI) is a setup permitting the control of external devices by decoding brain activity. Electroencephalography (EEG) has been extensively used for decoding brain activity since it is non-invasive, cheap, portable, and has high temporal resolution to allow real-time operation. Due to its poor spatial specificity, BCIs based on EEG can require extensive training and multiple trials to decode brain activity (consequently slowing down the operation of the BCI). On the o...

  15. [Dextrals and sinistrals (right-handers and left-handers): specificity of interhemispheric brain asymmetry and EEG coherence parameters].

    Science.gov (United States)

    Zhavoronkova, L A

    2007-01-01

    Data of literature about morphological, functional and biochemical specificity of the brain interhemispheric asymmetry of healthy right-handers and left-handers and about peculiarity of dynamics of cerebral pathology in patients with different individual asymmetry profiles are presented at the present article. Results of our investigation by using coherence parameters of electroencephalogram (EEG) in healthy right-handers and left-handers in state of rest, during functional tests and sleeping and in patients with different forms of the brain organic damage were analyzed too. EEG coherence analysis revealed the reciprocal changing of alpha-beta and theta-delta spectral bands in right-handers whilein left-handers synchronous changing of all EEG spectral bands were observed. Data about regional-frequent specificity of EEG coherence, peculiarity of EEG asymmetry in right-handers and left-handers, aslo about specificity of EEG spectral band genesis and point of view about a role of the brain regulator systems in forming of interhemispheric asymmetry in different functional states allowed to propose the conception about principle of interhermispheric brain asymmetry formation in left-handers and left-handers. Following this conception in dextrals elements of concurrent (summary-reciprocal) cooperation are predominant at the character of interhemispheric and cortical-subcortical interaction while in sinistrals a principle of concordance (supplementary) is preferable. These peculiarities the brain organization determine, from the first side, the quicker revovery of functions damaged after cranio-cerebral trauma in left-handers in comparison right-handers and from the other side - they determine the forming of the more expressed pathology in the remote terms after exposure the low dose of radiation.

  16. Interhemispheric EEG differences in olfactory bulbectomized rats with different cognitive abilities and brain beta-amyloid levels.

    Science.gov (United States)

    Bobkova, Natalia; Vorobyov, Vasily; Medvinskaya, Natalia; Aleksandrova, Irina; Nesterova, Inna

    2008-09-26

    Alterations in electroencephalogram (EEG) asymmetry and deficits in interhemispheric integration of information have been shown in patients with Alzheimer's disease (AD). However, no direct evidence of an association between EEG asymmetry, morphological markers in the brain, and cognition was found either in AD patients or in AD models. In this study we used rats with bilateral olfactory bulbectomy (OBX) as one of the AD models and measured their learning/memory abilities, brain beta-amyloid levels and EEG spectra in symmetrical frontal and occipital cortices. One year after OBX or sham-surgery, the rats were tested with the Morris water paradigm and assigned to three groups: sham-operated rats, SO, and OBX rats with virtually normal, OBX(+), or abnormal, OBX(-), learning (memory) abilities. In OBX vs. SO, the theta EEG activity was enhanced to a higher extent in the right frontal cortex and in the left occipital cortex. This produced significant interhemispheric differences in the frontal cortex of the OBX(-) rats and in the occipital cortex of both OBX groups. The beta1 EEG asymmetry in SO was attenuated in OBX(+) and completely eliminated in OBX(-). OBX produced highly significant beta2 EEG decline in the right frontal cortex, with OBX(-)>OBX(+) rank order of strength. The beta-amyloid level, examined by post-mortem immunological DOT-analysis in the cortex-hippocampus samples, was about six-fold higher in OBX(-) than in SO, but significantly less (enhanced by 82% vs. SO) in OBX(+) than in OBX(-). The involvement of the brain mediatory systems in the observed EEG asymmetry differences is discussed.

  17. Mapping (and modeling) physiological movements during EEG-fMRI recordings: the added value of the video acquired simultaneously.

    Science.gov (United States)

    Ruggieri, Andrea; Vaudano, Anna Elisabetta; Benuzzi, Francesca; Serafini, Marco; Gessaroli, Giuliana; Farinelli, Valentina; Nichelli, Paolo Frigio; Meletti, Stefano

    2015-01-15

    During resting-state EEG-fMRI studies in epilepsy, patients' spontaneous head-face movements occur frequently. We tested the usefulness of synchronous video recording to identify and model the fMRI changes associated with non-epileptic movements to improve sensitivity and specificity of fMRI maps related to interictal epileptiform discharges (IED). Categorization of different facial/cranial movements during EEG-fMRI was obtained for 38 patients [with benign epilepsy with centro-temporal spikes (BECTS, n=16); with idiopathic generalized epilepsy (IGE, n=17); focal symptomatic/cryptogenic epilepsy (n=5)]. We compared at single subject- and at group-level the IED-related fMRI maps obtained with and without additional regressors related to spontaneous movements. As secondary aim, we considered facial movements as events of interest to test the usefulness of video information to obtain fMRI maps of the following face movements: swallowing, mouth-tongue movements, and blinking. Video information substantially improved the identification and classification of the artifacts with respect to the EEG observation alone (mean gain of 28 events per exam). Inclusion of physiological activities as additional regressors in the GLM model demonstrated an increased Z-score and number of voxels of the global maxima and/or new BOLD clusters in around three quarters of the patients. Video-related fMRI maps for swallowing, mouth-tongue movements, and blinking were comparable to the ones obtained in previous task-based fMRI studies. Video acquisition during EEG-fMRI is a useful source of information. Modeling physiological movements in EEG-fMRI studies for epilepsy will lead to more informative IED-related fMRI maps in different epileptic conditions. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. Interictal Electroencephalography (EEG) Findings in Children with Epilepsy and Bilateral Brain Lesions on Magnetic Resonance Imaging (MRI).

    Science.gov (United States)

    Zubcevic, Smail; Milos, Maja; Catibusic, Feriha; Uzicanin, Sajra; Krdzalic, Belma

    2015-12-01

    Neuroimaging procedures and electroencephalography (EEG) are basic parts of investigation of patients with epilepsies. The aim is to try to assess relationship between bilaterally localized brain lesions found in routine management of children with newly diagnosed epilepsy and their interictal EEG findings. Total amount of 68 patients filled criteria for inclusion in the study that was performed at Neuropediatrics Department, Pediatric Hospital, University Clinical Center Sarajevo, or its outpatient clinic. There were 33 girls (48,5%) and 35 boys (51,5%). Average age at diagnosis of epilepsy was 3,5 years. Both neurological and neuropsychological examination in the moment of making diagnosis of epilepsy was normal in 27 (39,7%) patients, and showed some kind of delay or other neurological finding in 41 (60,3%). Brain MRI showed lesions that can be related to antenatal or perinatal events in most of the patients (ventricular dilation in 30,9%, delayed myelination and post-hypoxic changes in 27,9%). More than half of patients (55,9%) showed bilateral interictal epileptiform discharges on their EEGs, and further 14,7% had other kinds of bilateral abnormalities. Frequency of bilateral epileptic discharges showed statistically significant predominance on level of pEEG finding did not reveal significant type of EEG for assessed brain lesions. We conclude that there exists relationship between bilaterally localized brain MRI lesions and interictal bilateral epileptiform or nonspecific EEG findings in children with newly diagnosed epilepsies. These data are suggesting that in cases when they do not correlate there is a need for further investigation of seizure etiology.

  19. EEG Analysis of the Brain Activity during the Observation of Commercial, Political, or Public Service Announcements

    Directory of Open Access Journals (Sweden)

    Giovanni Vecchiato

    2010-01-01

    Full Text Available The use of modern brain imaging techniques could be useful to understand what brain areas are involved in the observation of video clips related to commercial advertising, as well as for the support of political campaigns, and also the areas of Public Service Announcements (PSAs. In this paper we describe the capability of tracking brain activity during the observation of commercials, political spots, and PSAs with advanced high-resolution EEG statistical techniques in time and frequency domains in a group of normal subjects. We analyzed the statistically significant cortical spectral power activity in different frequency bands during the observation of a commercial video clip related to the use of a beer in a group of 13 normal subjects. In addition, a TV speech of the Prime Minister of Italy was analyzed in two groups of swing and “supporter” voters. Results suggested that the cortical activity during the observation of commercial spots could vary consistently across the spot. This fact suggest the possibility to remove the parts of the spot that are not particularly attractive by using those cerebral indexes. The cortical activity during the observation of the political speech indicated a major cortical activity in the supporters group when compared to the swing voters. In this case, it is possible to conclude that the communication proposed has failed to raise attention or interest on swing voters. In conclusions, high-resolution EEG statistical techniques have been proved to able to generate useful insights about the particular fruition of TV messages, related to both commercial as well as political fields.

  20. EEG analysis of the brain activity during the observation of commercial, political, or public service announcements.

    Science.gov (United States)

    Vecchiato, Giovanni; Astolfi, Laura; Tabarrini, Alessandro; Salinari, Serenella; Mattia, Donatella; Cincotti, Febo; Bianchi, Luigi; Sorrentino, Domenica; Aloise, Fabio; Soranzo, Ramon; Babiloni, Fabio

    2010-01-01

    The use of modern brain imaging techniques could be useful to understand what brain areas are involved in the observation of video clips related to commercial advertising, as well as for the support of political campaigns, and also the areas of Public Service Announcements (PSAs). In this paper we describe the capability of tracking brain activity during the observation of commercials, political spots, and PSAs with advanced high-resolution EEG statistical techniques in time and frequency domains in a group of normal subjects. We analyzed the statistically significant cortical spectral power activity in different frequency bands during the observation of a commercial video clip related to the use of a beer in a group of 13 normal subjects. In addition, a TV speech of the Prime Minister of Italy was analyzed in two groups of swing and "supporter" voters. Results suggested that the cortical activity during the observation of commercial spots could vary consistently across the spot. This fact suggest the possibility to remove the parts of the spot that are not particularly attractive by using those cerebral indexes. The cortical activity during the observation of the political speech indicated a major cortical activity in the supporters group when compared to the swing voters. In this case, it is possible to conclude that the communication proposed has failed to raise attention or interest on swing voters. In conclusions, high-resolution EEG statistical techniques have been proved to able to generate useful insights about the particular fruition of TV messages, related to both commercial as well as political fields.

  1. EEG Brain Activity in Dynamic Health Qigong Training: Same Effects for Mental Practice and Physical Training?

    Science.gov (United States)

    Henz, Diana; Schöllhorn, Wolfgang I

    2017-01-01

    In recent years, there has been significant uptake of meditation and related relaxation techniques, as a means of alleviating stress and fostering an attentive mind. Several electroencephalogram (EEG) studies have reported changes in spectral band frequencies during Qigong meditation indicating a relaxed state. Much less is reported on effects of brain activation patterns induced by Qigong techniques involving bodily movement. In this study, we tested whether (1) physical Qigong training alters EEG theta and alpha activation, and (2) mental practice induces the same effect as a physical Qigong training. Subjects performed the dynamic Health Qigong technique Wu Qin Xi (five animals) physically and by mental practice in a within-subjects design. Experimental conditions were randomized. Two 2-min (eyes-open, eyes-closed) EEG sequences under resting conditions were recorded before and immediately after each 15-min exercise. Analyses of variance were performed for spectral power density data. Increased alpha power was found in posterior regions in mental practice and physical training for eyes-open and eyes-closed conditions. Theta power was increased after mental practice in central areas in eyes-open conditions, decreased in fronto-central areas in eyes-closed conditions. Results suggest that mental, as well as physical Qigong training, increases alpha activity and therefore induces a relaxed state of mind. The observed differences in theta activity indicate different attentional processes in physical and mental Qigong training. No difference in theta activity was obtained in physical and mental Qigong training for eyes-open and eyes-closed resting state. In contrast, mental practice of Qigong entails a high degree of internalized attention that correlates with theta activity, and that is dependent on eyes-open and eyes-closed resting state.

  2. Coherence and phase synchrony analyses of EEG signals in Mild Cognitive Impairment (MCI): A study of functional brain connectivity

    Science.gov (United States)

    Handayani, Nita; Haryanto, Freddy; Khotimah, Siti Nurul; Arif, Idam; Taruno, Warsito Purwo

    2018-03-01

    This paper presents an EEG study for coherence and phase synchrony in mild cognitive impairment (MCI) subjects. MCI is characterized by cognitive decline, which is an early stage of Alzheimer's disease (AD). AD is a neurodegenerative disorder with symptoms such as memory loss and cognitive impairment. EEG coherence is a statistical measure of correlation between signals from electrodes spatially separated on the scalp. The magnitude of phase synchrony is expressed in the phase locking value (PLV), a statistical measure of neuronal connectivity in the human brain. Brain signals were recorded using an Emotiv Epoc 14-channel wireless EEG at a sampling frequency of 128 Hz. In this study, we used 22 elderly subjects consisted of 10 MCI subjects and 12 healthy subjects as control group. The coherence between each electrode pair was measured for all frequency bands (delta, theta, alpha and beta). In the MCI subjects, the value of coherence and phase synchrony was generally lower than in the healthy subjects especially in the beta frequency. A decline of intrahemisphere coherence in the MCI subjects occurred in the left temporo-parietal-occipital region. The pattern of decline in MCI coherence is associated with decreased cholinergic connectivity along the path that connects the temporal, occipital, and parietal areas of the brain to the frontal area of the brain. EEG coherence and phase synchrony are able to distinguish persons who suffer AD in the early stages from healthy elderly subjects.

  3. New KF-PP-SVM classification method for EEG in brain-computer interfaces.

    Science.gov (United States)

    Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

    2014-01-01

    Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

  4. Evaluation of different EEG acquisition systems concerning their suitability for building a brain-computer interface

    Directory of Open Access Journals (Sweden)

    Andreas Pinegger

    2016-09-01

    Full Text Available One important aspect in non-invasive brain-computer interface (BCI research is to acquire the electroencephalogram (EEG in a proper way. From an end-user perspective this means with maximum comfort and without any extra inconveniences (e.g., washing the hair. Whereas from a technical perspective, the signal quality has to be optimal to make the BCI work effectively and efficiently.In this work we evaluated three different commercially available EEG acquisition systems that differ in the type of electrode (gel-, water-, and dry-based, the amplifier technique, and the data transmission method. Every system was tested regarding three different aspects, namely, technical, BCI effectiveness and efficiency (P300 communication and control, and user satisfaction (comfort.We found that the water-based system had the lowest short circuit noise level, the hydrogel-based system had the highest P300 spelling accuracies, and the dry electrode system caused the least inconveniences.Therefore, building a reliable BCI is possible with all evaluated systems and it is on the user to decide which system meets the given requirements best.

  5. Measuring brain activity cycling (BAC) in long term EEG monitoring of preterm babies

    International Nuclear Information System (INIS)

    Stevenson, Nathan J; Palmu, Kirsi; Wikström, Sverre; Hellström-Westas, Lena; Vanhatalo, Sampsa

    2014-01-01

    Measuring fluctuation of vigilance states in early preterm infants undergoing long term intensive care holds promise for monitoring their neurological well-being. There is currently, however, neither objective nor quantitative methods available for this purpose in a research or clinical environment. The aim of this proof-of-concept study was, therefore, to develop quantitative measures of the fluctuation in vigilance states or brain activity cycling (BAC) in early preterm infants. The proposed measures of BAC were summary statistics computed on a frequency domain representation of the proportional duration of spontaneous activity transients (SAT%) calculated from electroencephalograph (EEG) recordings. Eighteen combinations of three statistics and six frequency domain representations were compared to a visual interpretation of cycling in the SAT% signal. Three high performing measures (band energy/periodogram: R = 0.809, relative band energy/nonstationary frequency marginal: R = 0.711, g-statistic/nonstationary frequency marginal: R = 0.638) were then compared to a grading of sleep wake cycling based on the visual interpretation of the amplitude-integrated EEG trend. These measures of BAC are conceptually straightforward, correlate well with the visual scores of BAC and sleep wake cycling, are robust enough to cope with the technically compromised monitoring data available in intensive care units, and are recommended for further validation in prospective studies. (paper)

  6. Localization of extended brain sources from EEG/MEG: the ExSo-MUSIC approach.

    Science.gov (United States)

    Birot, Gwénaël; Albera, Laurent; Wendling, Fabrice; Merlet, Isabelle

    2011-05-01

    We propose a new MUSIC-like method, called 2q-ExSo-MUSIC (q ≥ 1). This method is an extension of the 2q-MUSIC (q ≥ 1) approach for solving the EEG/MEG inverse problem, when spatially-extended neocortical sources ("ExSo") are considered. It introduces a novel ExSo-MUSIC principle. The novelty is two-fold: i) the parameterization of the spatial source distribution that leads to an appropriate metric in the context of distributed brain sources and ii) the introduction of an original, efficient and low-cost way of optimizing this metric. In 2q-ExSo-MUSIC, the possible use of higher order statistics (q ≥ 2) offers a better robustness with respect to Gaussian noise of unknown spatial coherence and modeling errors. As a result we reduced the penalizing effects of both the background cerebral activity that can be seen as a Gaussian and spatially correlated noise, and the modeling errors induced by the non-exact resolution of the forward problem. Computer results on simulated EEG signals obtained with physiologically-relevant models of both the sources and the volume conductor show a highly increased performance of our 2q-ExSo-MUSIC method as compared to the classical 2q-MUSIC algorithms. Copyright © 2011 Elsevier Inc. All rights reserved.

  7. Relationship between brain function (aEEG) and brain structure (MRI) and their predictive value for neurodevelopmental outcome of preterm infants.

    Science.gov (United States)

    Hüning, Britta; Storbeck, Tobias; Bruns, Nora; Dransfeld, Frauke; Hobrecht, Julia; Karpienski, Julia; Sirin, Selma; Schweiger, Bernd; Weiss, Christel; Felderhoff-Müser, Ursula; Müller, Hanna

    2018-05-22

    To improve the prediction of neurodevelopmental outcome in very preterm infants, this study used the combination of amplitude-integrated electroencephalography (aEEG) within the first 72 h of life and cranial magnetic resonance imaging (MRI) at term equivalent age. A single-center cohort of 38 infants born before 32 weeks of gestation was subjected to both investigations. Structural measurements were performed on MRI. Multiple regression analysis was used to identify independent factors including functional and structural brain measurements associated with outcome at a corrected age of 24 months. aEEG parameters significantly correlated with MRI measurements. Reduced deep gray matter volume was associated with low Burdjalov Score on day 3 (p neurodevelopmental outcome: intraventricular hemorrhage (p = 0.0060) and interhemispheric distance (p = 0.0052) for mental developmental index; Burdjalov Score day 1 (p = 0.0201) and interhemispheric distance (p = 0.0142) for psychomotor developmental index. Functional aEEG parameters were associated with altered brain maturation on MRI. The combination of aEEG and MRI contributes to the prediction of outcome at 24 months. What is Known: • Prematurity remains a risk factor for impaired neurodevelopment. • aEEG is used to measure brain activity in preterm infants and cranial MRI is performed to identify structural gray and white matter abnormalities with impact on neurodevelopmental outcome. What is New: • aEEG parameters observed within the first 72 h of life were associated with altered deep gray matter volumes, biparietal width, and transcerebellar diameter at term equivalent age. • The combination of aEEG and MRI contributes to the prediction of neurodevelopmental outcome at 2 years of corrected age in very preterm infants.

  8. A Comparative Study of Standardized Infinity Reference and Average Reference for EEG of Three Typical Brain States

    Directory of Open Access Journals (Sweden)

    Gaoxing Zheng

    2018-03-01

    Full Text Available The choice of different reference electrodes plays an important role in deciphering the functional meaning of electroencephalography (EEG signals. In recent years, the infinity zero reference using the reference electrode standard technique (REST has been increasingly applied, while the average reference (AR was generally advocated as the best available reference option in previous classical EEG studies. Here, we designed EEG experiments and performed a direct comparison between the influences of REST and AR on EEG-revealed brain activity features for three typical brain behavior states (eyes-closed, eyes-open and music-listening. The analysis results revealed the following observations: (1 there is no significant difference in the alpha-wave-blocking effect during the eyes-open state compared with the eyes-closed state for both REST and AR references; (2 there was clear frontal EEG asymmetry during the resting state, and the degree of lateralization under REST was higher than that under AR; (3 the global brain functional connectivity density (FCD and local FCD have higher values for REST than for AR under different behavior states; and (4 the value of the small-world network characteristic in the eyes-closed state is significantly (in full, alpha, beta and gamma frequency bands higher than that in the eyes-open state, and the small-world effect under the REST reference is higher than that under AR. In addition, the music-listening state has a higher small-world network effect than the eyes-closed state. The above results suggest that typical EEG features might be more clearly presented by applying the REST reference than by applying AR when using a 64-channel recording.

  9. Joint Time-Frequency-Space Classification of EEG in a Brain-Computer Interface Application

    Directory of Open Access Journals (Sweden)

    Molina Gary N Garcia

    2003-01-01

    Full Text Available Brain-computer interface is a growing field of interest in human-computer interaction with diverse applications ranging from medicine to entertainment. In this paper, we present a system which allows for classification of mental tasks based on a joint time-frequency-space decorrelation, in which mental tasks are measured via electroencephalogram (EEG signals. The efficiency of this approach was evaluated by means of real-time experimentations on two subjects performing three different mental tasks. To do so, a number of protocols for visualization, as well as training with and without feedback, were also developed. Obtained results show that it is possible to obtain good classification of simple mental tasks, in view of command and control, after a relatively small amount of training, with accuracies around 80%, and in real time.

  10. Application of a brain-computer interface for person authentication using EEG responses to photo stimuli.

    Science.gov (United States)

    Mu, Zhendong; Yin, Jinhai; Hu, Jianfeng

    2018-01-01

    In this paper, a person authentication system that can effectively identify individuals by generating unique electroencephalogram signal features in response to self-face and non-self-face photos is presented. In order to achieve a good stability performance, the sequence of self-face photo including first-occurrence position and non-first-occurrence position are taken into account in the serial occurrence of visual stimuli. In addition, a Fisher linear classification method and event-related potential technique for feature analysis is adapted to yield remarkably better outcomes than that by most of the existing methods in the field. The results have shown that the EEG-based person authentications via brain-computer interface can be considered as a suitable approach for biometric authentication system.

  11. Cortical sources of resting state EEG rhythms are related to brain hypometabolism in subjects with Alzheimer's disease: an EEG-PET study.

    Science.gov (United States)

    Babiloni, Claudio; Del Percio, Claudio; Caroli, Anna; Salvatore, Elena; Nicolai, Emanuele; Marzano, Nicola; Lizio, Roberta; Cavedo, Enrica; Landau, Susan; Chen, Kewei; Jagust, William; Reiman, Eric; Tedeschi, Gioacchino; Montella, Patrizia; De Stefano, Manuela; Gesualdo, Loreto; Frisoni, Giovanni B; Soricelli, Andrea

    2016-12-01

    Cortical sources of resting state electroencephalographic (EEG) delta (2-4 Hz) and low-frequency alpha (8-10.5 Hz) rhythms show abnormal activity (i.e., current density) in patients with dementia due to Alzheimer's disease (AD). Here, we hypothesized that abnormality of this activity is related to relevant disease processes as revealed by cortical hypometabolism typically observed in AD patients by fluorodeoxyglucose positron emission tomography. Resting state eyes-closed EEG data were recorded in 19 AD patients with dementia and 40 healthy elderly (Nold) subjects. EEG frequency bands of interest were delta and low-frequency alpha. EEG sources were estimated in these bands by low-resolution brain electromagnetic tomography (LORETA). Fluorodeoxyglucose positron emission tomography images were recorded only in the AD patients, and cortical hypometabolism was indexed by the so-called Alzheimer's discrimination analysis tool (PALZ) in the frontal association, ventromedial frontal, temporoparietal association, posterior cingulate, and precuneus areas. Results showed that compared with the Nold group, the AD group pointed to higher activity of delta sources and lower activity of low-frequency alpha sources in a cortical region of interest formed by all cortical areas of the PALZ score. In the AD patients, there was a positive correlation between the PALZ score and the activity of delta sources in the cortical region of interest (p < 0.05). These results suggest a relationship between resting state cortical hypometabolism and synchronization of cortical neurons at delta rhythms in AD patients with dementia. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Resting state brain dynamics and its transients: a combined TMS-EEG study.

    Science.gov (United States)

    Bonnard, Mireille; Chen, Sophie; Gaychet, Jérôme; Carrere, Marcel; Woodman, Marmaduke; Giusiano, Bernard; Jirsa, Viktor

    2016-08-04

    The brain at rest exhibits a spatio-temporally rich dynamics which adheres to systematic behaviours that persist in task paradigms but appear altered in disease. Despite this hypothesis, many rest state paradigms do not act directly upon the rest state and therefore cannot confirm hypotheses about its mechanisms. To address this challenge, we combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to study brain's relaxation toward rest following a transient perturbation. Specifically, TMS targeted either the medial prefrontal cortex (MPFC), i.e. part of the Default Mode Network (DMN) or the superior parietal lobule (SPL), involved in the Dorsal Attention Network. TMS was triggered by a given brain state, namely an increase in occipital alpha rhythm power. Following the initial TMS-Evoked Potential, TMS at MPFC enhances the induced occipital alpha rhythm, called Event Related Synchronisation, with a longer transient lifetime than TMS at SPL, and a higher amplitude. Our findings show a strong coupling between MPFC and the occipital alpha power. Although the rest state is organized around a core of resting state networks, the DMN functionally takes a special role among these resting state networks.

  13. Optogenetic Approaches for Mesoscopic Brain Mapping.

    Science.gov (United States)

    Kyweriga, Michael; Mohajerani, Majid H

    2016-01-01

    Recent advances in identifying genetically unique neuronal proteins has revolutionized the study of brain circuitry. Researchers are now able to insert specific light-sensitive proteins (opsins) into a wide range of specific cell types via viral injections or by breeding transgenic mice. These opsins enable the activation, inhibition, or modulation of neuronal activity with millisecond control within distinct brain regions defined by genetic markers. Here we present a useful guide to implement this technique into any lab. We first review the materials needed and practical considerations and provide in-depth instructions for acute surgeries in mice. We conclude with all-optical mapping techniques for simultaneous recording and manipulation of population activity of many neurons in vivo by combining arbitrary point optogenetic stimulation and regional voltage-sensitive dye imaging. It is our intent to make these methods available to anyone wishing to use them.

  14. Connectome imaging for mapping human brain pathways.

    Science.gov (United States)

    Shi, Y; Toga, A W

    2017-09-01

    With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project. Characterization of the microstructural environment in the human brain is discussed from the tensor model to the general fiber orientation distribution (FOD) models that can resolve crossing fibers in each voxel of the image. Using FOD-based tractography, we describe novel methods for fiber bundle reconstruction and graph-based connectivity analysis. Building upon these novel developments, there have already been successful applications of connectome imaging techniques in reconstructing challenging brain pathways. Examples including retinofugal and brainstem pathways will be reviewed. Finally, we discuss future directions in connectome imaging and its interaction with other aspects of brain imaging research.

  15. Source-based neurofeedback methods using EEG recordings: training altered brain activity in a functional brain source derived from blind source separation

    Science.gov (United States)

    White, David J.; Congedo, Marco; Ciorciari, Joseph

    2014-01-01

    A developing literature explores the use of neurofeedback in the treatment of a range of clinical conditions, particularly ADHD and epilepsy, whilst neurofeedback also provides an experimental tool for studying the functional significance of endogenous brain activity. A critical component of any neurofeedback method is the underlying physiological signal which forms the basis for the feedback. While the past decade has seen the emergence of fMRI-based protocols training spatially confined BOLD activity, traditional neurofeedback has utilized a small number of electrode sites on the scalp. As scalp EEG at a given electrode site reflects a linear mixture of activity from multiple brain sources and artifacts, efforts to successfully acquire some level of control over the signal may be confounded by these extraneous sources. Further, in the event of successful training, these traditional neurofeedback methods are likely influencing multiple brain regions and processes. The present work describes the use of source-based signal processing methods in EEG neurofeedback. The feasibility and potential utility of such methods were explored in an experiment training increased theta oscillatory activity in a source derived from Blind Source Separation (BSS) of EEG data obtained during completion of a complex cognitive task (spatial navigation). Learned increases in theta activity were observed in two of the four participants to complete 20 sessions of neurofeedback targeting this individually defined functional brain source. Source-based EEG neurofeedback methods using BSS may offer important advantages over traditional neurofeedback, by targeting the desired physiological signal in a more functionally and spatially specific manner. Having provided preliminary evidence of the feasibility of these methods, future work may study a range of clinically and experimentally relevant brain processes where individual brain sources may be targeted by source-based EEG neurofeedback. PMID

  16. The Track of Brain Activity during the Observation of TV Commercials with the High-Resolution EEG Technology

    Directory of Open Access Journals (Sweden)

    Laura Astolfi

    2009-01-01

    Full Text Available We estimate cortical activity in normal subjects during the observation of TV commercials inserted within a movie by using high-resolution EEG techniques. The brain activity was evaluated in both time and frequency domains by solving the associate inverse problem of EEG with the use of realistic head models. In particular, we recover statistically significant information about cortical areas engaged by particular scenes inserted within the TV commercial proposed with respect to the brain activity estimated while watching a documentary. Results obtained in the population investigated suggest that the statistically significant brain activity during the observation of the TV commercial was mainly concentrated in frontoparietal cortical areas, roughly coincident with the Brodmann areas 8, 9, and 7, in the analyzed population.

  17. Multi-Variate EEG Analysis as a Novel Tool to Examine Brain Responses to Naturalistic Music Stimuli.

    Directory of Open Access Journals (Sweden)

    Irene Sturm

    Full Text Available Note onsets in music are acoustic landmarks providing auditory cues that underlie the perception of more complex phenomena such as beat, rhythm, and meter. For naturalistic ongoing sounds a detailed view on the neural representation of onset structure is hard to obtain, since, typically, stimulus-related EEG signatures are derived by averaging a high number of identical stimulus presentations. Here, we propose a novel multivariate regression-based method extracting onset-related brain responses from the ongoing EEG. We analyse EEG recordings of nine subjects who passively listened to stimuli from various sound categories encompassing simple tone sequences, full-length romantic piano pieces and natural (non-music soundscapes. The regression approach reduces the 61-channel EEG to one time course optimally reflecting note onsets. The neural signatures derived by this procedure indeed resemble canonical onset-related ERPs, such as the N1-P2 complex. This EEG projection was then utilized to determine the Cortico-Acoustic Correlation (CACor, a measure of synchronization between EEG signal and stimulus. We demonstrate that a significant CACor (i can be detected in an individual listener's EEG of a single presentation of a full-length complex naturalistic music stimulus, and (ii it co-varies with the stimuli's average magnitudes of sharpness, spectral centroid, and rhythmic complexity. In particular, the subset of stimuli eliciting a strong CACor also produces strongly coordinated tension ratings obtained from an independent listener group in a separate behavioral experiment. Thus musical features that lead to a marked physiological reflection of tone onsets also contribute to perceived tension in music.

  18. Orbitofrontal disinhibition of pain in migraine with aura: an interictal EEG-mapping study.

    Science.gov (United States)

    Lev, Rina; Granovsky, Yelena; Yarnitsky, David

    2010-08-01

    This study aimed to identify the cortical mechanisms underlying the processes of interictal dishabituation to experimental pain in subjects suffering from migraine with aura (MWA). In 21 subjects with MWA and 22 healthy controls, cortical responses to two successive trials of noxious contact-heat stimuli were analyzed using EEG-tomography software. When compared with controls, MWA patients showed significantly increased pain-evoked potential amplitudes accompanied by reduced activity in the orbitofrontal cortex (OFC) and increased activity in the pain matrix regions, including the primary somatosensory cortex (SI) (p < .05). Similarly to controls, MWA subjects displayed an inverse correlation between the OFC and SI activities, and positive interrelations between other pain-specific regions. The activity changes in the OFC negatively correlated with lifetime headache duration and longevity (p < .05). Reduced inhibitory functioning of the prefrontal cortex is a possible cause for disinhibition of the pain-related sensory cortices in migraine. The finding of OFC hypofunction over the disease course is in keeping with current concepts of migraine as a progressive brain disorder.

  19. Spatiotemporal mapping of interictal spike propagation: a novel methodology applied to pediatric intracranial EEG recordings.

    Directory of Open Access Journals (Sweden)

    Samuel Tomlinson

    2016-12-01

    Full Text Available Synchronized cortical activity is implicated in both normative cognitive functioning andmany neurological disorders. For epilepsy patients with intractable seizures, irregular patterns ofsynchronization within the epileptogenic zone (EZ is believed to provide the network substratethrough which seizures initiate and propagate. Mapping the EZ prior to epilepsy surgery is critical fordetecting seizure networks in order to achieve post-surgical seizure control. However, automatedtechniques for characterizing epileptic networks have yet to gain traction in the clinical setting.Recent advances in signal processing and spike detection have made it possible to examine thespatiotemporal propagation of interictal spike discharges across the epileptic cortex. In this study, wepresent a novel methodology for detecting, extracting, and visualizing spike propagation anddemonstrate its potential utility as a biomarker for the epileptogenic zone. Eighteen pre-surgicalintracranial EEG recordings were obtained from pediatric patients ultimately experiencing favorable(i.e., seizure-free, n = 9 or unfavorable (i.e., seizure-persistent, n = 9 surgical outcomes. Novelalgorithms were applied to extract multi-channel spike discharges and visualize their spatiotemporalpropagation. Quantitative analysis of spike propagation was performed using trajectory clusteringand spatial autocorrelation techniques. Comparison of interictal propagation patterns revealed anincrease in trajectory organization (i.e., spatial autocorrelation among Sz-Free patients compared toSz-Persist patients. The pathophysiological basis and clinical implications of these findings areconsidered.

  20. Mapping human preictal and ictal haemodynamic networks using simultaneous intracranial EEG-fMRI

    Directory of Open Access Journals (Sweden)

    Umair J. Chaudhary

    2016-01-01

    In conclusion, icEEG-fMRI allowed us to reveal BOLD changes within and beyond the SOZ linked to very localised ictal fluctuations in beta and gamma activity measured in the amygdala and hippocampus. Furthermore, the BOLD changes within the SOZ structures were better captured by the quantitative models, highlighting the interest in considering seizure-related EEG fluctuations across the entire spectrum.

  1. Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies.

    Directory of Open Access Journals (Sweden)

    Christian O'Reilly

    Full Text Available Although it is well recognized that autism is associated with altered patterns of over- and under-connectivity, specifics are still a matter of debate. Little has been done so far to synthesize available literature using whole-brain electroencephalography (EEG and magnetoencephalography (MEG recordings.1 To systematically review the literature on EEG/MEG functional and effective connectivity in autism spectrum disorder (ASD, 2 to synthesize and critically appraise findings related with the hypothesis that ASD is characterized by long-range underconnectivity and local overconnectivity, and 3 to provide, based on the literature, an analysis of tentative factors that are likely to mediate association between ASD and atypical connectivity (e.g., development, topography, lateralization.Literature reviews were done using PubMed and PsychInfo databases. Abstracts were screened, and only relevant articles were analyzed based on the objectives of this paper. Special attention was paid to the methodological characteristics that could have created variability in outcomes reported between studies.Our synthesis provides relatively strong support for long-range underconnectivity in ASD, whereas the status of local connectivity remains unclear. This observation was also mirrored by a similar relationship with lower frequencies being often associated with underconnectivity and higher frequencies being associated with both under- and over-connectivity. Putting together these observations, we propose that ASD is characterized by a general trend toward an under-expression of lower-band wide-spread integrative processes compensated by more focal, higher-frequency, locally specialized, and segregated processes. Further investigation is, however, needed to corroborate the conclusion and its generalizability across different tasks. Of note, abnormal lateralization in ASD, specifically an elevated left-over-right EEG and MEG functional connectivity ratio, has been also

  2. Mapping fetal brain development in utero using magnetic resonance imaging: the Big Bang of brain mapping.

    Science.gov (United States)

    Studholme, Colin

    2011-08-15

    The development of tools to construct and investigate probabilistic maps of the adult human brain from magnetic resonance imaging (MRI) has led to advances in both basic neuroscience and clinical diagnosis. These tools are increasingly being applied to brain development in adolescence and childhood, and even to neonatal and premature neonatal imaging. Even earlier in development, parallel advances in clinical fetal MRI have led to its growing use as a tool in challenging medical conditions. This has motivated new engineering developments encompassing optimal fast MRI scans and techniques derived from computer vision, the combination of which allows full 3D imaging of the moving fetal brain in utero without sedation. These promise to provide a new and unprecedented window into early human brain growth. This article reviews the developments that have led us to this point, examines the current state of the art in the fields of fast fetal imaging and motion correction, and describes the tools to analyze dynamically changing fetal brain structure. New methods to deal with developmental tissue segmentation and the construction of spatiotemporal atlases are examined, together with techniques to map fetal brain growth patterns.

  3. A hybrid NIRS-EEG system for self-paced brain computer interface with online motor imagery.

    Science.gov (United States)

    Koo, Bonkon; Lee, Hwan-Gon; Nam, Yunjun; Kang, Hyohyeong; Koh, Chin Su; Shin, Hyung-Cheul; Choi, Seungjin

    2015-04-15

    For a self-paced motor imagery based brain-computer interface (BCI), the system should be able to recognize the occurrence of a motor imagery, as well as the type of the motor imagery. However, because of the difficulty of detecting the occurrence of a motor imagery, general motor imagery based BCI studies have been focusing on the cued motor imagery paradigm. In this paper, we present a novel hybrid BCI system that uses near infrared spectroscopy (NIRS) and electroencephalography (EEG) systems together to achieve online self-paced motor imagery based BCI. We designed a unique sensor frame that records NIRS and EEG simultaneously for the realization of our system. Based on this hybrid system, we proposed a novel analysis method that detects the occurrence of a motor imagery with the NIRS system, and classifies its type with the EEG system. An online experiment demonstrated that our hybrid system had a true positive rate of about 88%, a false positive rate of 7% with an average response time of 10.36 s. As far as we know, there is no report that explored hemodynamic brain switch for self-paced motor imagery based BCI with hybrid EEG and NIRS system. From our experimental results, our hybrid system showed enough reliability for using in a practical self-paced motor imagery based BCI. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors

    Science.gov (United States)

    2012-01-01

    A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering. PMID:22284235

  5. Mobile Phone Chips Reduce Increases in EEG Brain Activity Induced by Mobile Phone-Emitted Electromagnetic Fields.

    Science.gov (United States)

    Henz, Diana; Schöllhorn, Wolfgang I; Poeggeler, Burkhard

    2018-01-01

    Recent neurophysiological studies indicate that exposure to electromagnetic fields (EMFs) generated by mobile phone radiation can exert effects on brain activity. One technical solution to reduce effects of EMFs in mobile phone use is provided in mobile phone chips that are applied to mobile phones or attached to their surfaces. To date, there are no systematical studies on the effects of mobile phone chip application on brain activity and the underlying neural mechanisms. The present study investigated whether mobile phone chips that are applied to mobile phones reduce effects of EMFs emitted by mobile phone radiation on electroencephalographic (EEG) brain activity in a laboratory study. Thirty participants volunteered in the present study. Experimental conditions (mobile phone chip, placebo chip, no chip) were set up in a randomized within-subjects design. Spontaneous EEG was recorded before and after mobile phone exposure for two 2-min sequences at resting conditions. During mobile phone exposure, spontaneous EEG was recorded for 30 min during resting conditions, and 5 min during performance of an attention test (d2-R). Results showed increased activity in the theta, alpha, beta and gamma bands during EMF exposure in the placebo and no chip conditions. Application of the mobile phone chip reduced effects of EMFs on EEG brain activity and attentional performance significantly. Attentional performance level was maintained regarding number of edited characters. Further, a dipole analysis revealed different underlying activation patterns in the chip condition compared to the placebo chip and no chip conditions. Finally, a correlational analysis for the EEG frequency bands and electromagnetic high-frequency (HF) emission showed significant correlations in the placebo chip and no chip condition for the theta, alpha, beta, and gamma bands. In the chip condition, a significant correlation of HF with the theta and alpha bands, but not with the beta and gamma bands was

  6. Mobile Phone Chips Reduce Increases in EEG Brain Activity Induced by Mobile Phone-Emitted Electromagnetic Fields

    Science.gov (United States)

    Henz, Diana; Schöllhorn, Wolfgang I.; Poeggeler, Burkhard

    2018-01-01

    Recent neurophysiological studies indicate that exposure to electromagnetic fields (EMFs) generated by mobile phone radiation can exert effects on brain activity. One technical solution to reduce effects of EMFs in mobile phone use is provided in mobile phone chips that are applied to mobile phones or attached to their surfaces. To date, there are no systematical studies on the effects of mobile phone chip application on brain activity and the underlying neural mechanisms. The present study investigated whether mobile phone chips that are applied to mobile phones reduce effects of EMFs emitted by mobile phone radiation on electroencephalographic (EEG) brain activity in a laboratory study. Thirty participants volunteered in the present study. Experimental conditions (mobile phone chip, placebo chip, no chip) were set up in a randomized within-subjects design. Spontaneous EEG was recorded before and after mobile phone exposure for two 2-min sequences at resting conditions. During mobile phone exposure, spontaneous EEG was recorded for 30 min during resting conditions, and 5 min during performance of an attention test (d2-R). Results showed increased activity in the theta, alpha, beta and gamma bands during EMF exposure in the placebo and no chip conditions. Application of the mobile phone chip reduced effects of EMFs on EEG brain activity and attentional performance significantly. Attentional performance level was maintained regarding number of edited characters. Further, a dipole analysis revealed different underlying activation patterns in the chip condition compared to the placebo chip and no chip conditions. Finally, a correlational analysis for the EEG frequency bands and electromagnetic high-frequency (HF) emission showed significant correlations in the placebo chip and no chip condition for the theta, alpha, beta, and gamma bands. In the chip condition, a significant correlation of HF with the theta and alpha bands, but not with the beta and gamma bands was

  7. 99mTc-HMPAO perfusion indices and brain-mapping in stroke patients

    International Nuclear Information System (INIS)

    Minchev, D.; Klisarova, A.

    1997-01-01

    It is the purpose of the study to establish correlations between 99mTc-HMPAO (hexamethylpropylenaminoxym) perfusion indices and changes in brain-mapping among patients with acute stroke. Forty-six patients with definitely proved stroke syndrome are investigated in the first 72 hours and 15 days after the onset of cerebrovascular accident using clinical, neuro-physiological and 99mTc-HMPAO SPECT methods. Regional and hemispheric perfusion asymmetry correlate with the brain-mapping cerebral disturbance (p < 0.001). In patients presenting focal hypoperfusion there is a significant correlation between perfusion indices and local EEG disturbance (r = 0.87). The dynamic study demonstrates a significant correlation between perfusion indices and electrical cerebral disturbance in the first 72 hours after the onset of the cerebrovascular accident. Fifteen days later no such correlation is documented. The obtained results demonstrate the essential practical bearing of 99mTc-HMPAO SPECT indices on the objective assessment of perfusion hemispheric and regional asymmetry in stroke patients, and the possibility of being used for indirect estimation of the regional cerebral blood flow in acute stroke patients against the background of visual and quantitative EEG changes (author)

  8. The effect of epoch length on estimated EEG functional connectivity and brain network organisation

    Science.gov (United States)

    Fraschini, Matteo; Demuru, Matteo; Crobe, Alessandra; Marrosu, Francesco; Stam, Cornelis J.; Hillebrand, Arjan

    2016-06-01

    Objective. Graph theory and network science tools have revealed fundamental mechanisms of functional brain organization in resting-state M/EEG analysis. Nevertheless, it is still not clearly understood how several methodological aspects may bias the topology of the reconstructed functional networks. In this context, the literature shows inconsistency in the chosen length of the selected epochs, impeding a meaningful comparison between results from different studies. Approach. The aim of this study was to provide a network approach insensitive to the effects that epoch length has on functional connectivity and network reconstruction. Two different measures, the phase lag index (PLI) and the amplitude envelope correlation (AEC) were applied to EEG resting-state recordings for a group of 18 healthy volunteers using non-overlapping epochs with variable length (1, 2, 4, 6, 8, 10, 12, 14 and 16 s). Weighted clustering coefficient (CCw), weighted characteristic path length (L w) and minimum spanning tree (MST) parameters were computed to evaluate the network topology. The analysis was performed on both scalp and source-space data. Main results. Results from scalp analysis show a decrease in both mean PLI and AEC values with an increase in epoch length, with a tendency to stabilize at a length of 12 s for PLI and 6 s for AEC. Moreover, CCw and L w show very similar behaviour, with metrics based on AEC more reliable in terms of stability. In general, MST parameters stabilize at short epoch lengths, particularly for MSTs based on PLI (1-6 s versus 4-8 s for AEC). At the source-level the results were even more reliable, with stability already at 1 s duration for PLI-based MSTs. Significance. The present work suggests that both PLI and AEC depend on epoch length and that this has an impact on the reconstructed network topology, particularly at the scalp-level. Source-level MST topology is less sensitive to differences in epoch length, therefore enabling the comparison of brain

  9. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update.

    Science.gov (United States)

    Lotte, F; Bougrain, L; Cichocki, A; Clerc, M; Congedo, M; Rakotomamonjy, A; Yger, F

    2018-06-01

    Most current electroencephalography (EEG)-based brain-computer interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately ten years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.

  10. EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone

    OpenAIRE

    Blum, Sarah; Debener, Stefan; Emkes, Reiner; Volkening, Nils; Fudickar, Sebastian; Bleichner, Martin G.

    2017-01-01

    Objective. Our aim was the development and validation of a modular signal processing and classification application enabling online electroencephalography (EEG) signal processing on off-the-shelf mobile Android devices. The software application SCALA (Signal ProCessing and CLassification on Android) supports a standardized communication interface to exchange information with external software and hardware. Approach. In order to implement a closed-loop brain-computer interface (BCI) on the sma...

  11. Spatiotemporal dynamics of the brain at rest--exploring EEG microstates as electrophysiological signatures of BOLD resting state networks.

    Science.gov (United States)

    Yuan, Han; Zotev, Vadim; Phillips, Raquel; Drevets, Wayne C; Bodurka, Jerzy

    2012-05-01

    Neuroimaging research suggests that the resting cerebral physiology is characterized by complex patterns of neuronal activity in widely distributed functional networks. As studied using functional magnetic resonance imaging (fMRI) of the blood-oxygenation-level dependent (BOLD) signal, the resting brain activity is associated with slowly fluctuating hemodynamic signals (~10s). More recently, multimodal functional imaging studies involving simultaneous acquisition of BOLD-fMRI and electroencephalography (EEG) data have suggested that the relatively slow hemodynamic fluctuations of some resting state networks (RSNs) evinced in the BOLD data are related to much faster (~100 ms) transient brain states reflected in EEG signals, that are referred to as "microstates". To further elucidate the relationship between microstates and RSNs, we developed a fully data-driven approach that combines information from simultaneously recorded, high-density EEG and BOLD-fMRI data. Using independent component analysis (ICA) of the combined EEG and fMRI data, we identified thirteen microstates and ten RSNs that are organized independently in their temporal and spatial characteristics, respectively. We hypothesized that the intrinsic brain networks that are active at rest would be reflected in both the EEG data and the fMRI data. To test this hypothesis, the rapid fluctuations associated with each microstate were correlated with the BOLD-fMRI signal associated with each RSN. We found that each RSN was characterized further by a specific electrophysiological signature involving from one to a combination of several microstates. Moreover, by comparing the time course of EEG microstates to that of the whole-brain BOLD signal, on a multi-subject group level, we unraveled for the first time a set of microstate-associated networks that correspond to a range of previously described RSNs, including visual, sensorimotor, auditory, attention, frontal, visceromotor and default mode networks. These

  12. A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients.

    Science.gov (United States)

    Sarasola-Sanz, Andrea; Irastorza-Landa, Nerea; Lopez-Larraz, Eduardo; Bibian, Carlos; Helmhold, Florian; Broetz, Doris; Birbaumer, Niels; Ramos-Murguialday, Ander

    2017-07-01

    Including supplementary information from the brain or other body parts in the control of brain-machine interfaces (BMIs) has been recently proposed and investigated. Such enriched interfaces are referred to as hybrid BMIs (hBMIs) and have been proven to be more robust and accurate than regular BMIs for assistive and rehabilitative applications. Electromyographic (EMG) activity is one of the most widely utilized biosignals in hBMIs, as it provides a quite direct measurement of the motion intention of the user. Whereas most of the existing non-invasive EEG-EMG-hBMIs have only been subjected to offline testings or are limited to one degree of freedom (DoF), we present an EEG-EMG-hBMI that allows the simultaneous control of 7-DoFs of the upper limb with a robotic exoskeleton. Moreover, it establishes a biologically-inspired hierarchical control flow, requiring the active participation of central and peripheral structures of the nervous system. Contingent visual and proprioceptive feedback about the user's EEG and EMG activity is provided in the form of velocity modulation during functional task training. We believe that training with this closed-loop system may facilitate functional neuroplastic processes and eventually elicit a joint brain and muscle motor rehabilitation. Its usability is validated during a real-time operation session in a healthy participant and a chronic stroke patient, showing encouraging results for its application to a clinical rehabilitation scenario.

  13. Longitudinal Dynamics of 3-Dimensional Components of Selfhood After Severe Traumatic Brain Injury: A qEEG Case Study.

    Science.gov (United States)

    Fingelkurts, Andrew A; Fingelkurts, Alexander A

    2017-09-01

    In this report, we describe the case of a patient who sustained extremely severe traumatic brain damage with diffuse axonal injury in a traffic accident and whose recovery was monitored during 6 years. Specifically, we were interested in the recovery dynamics of 3-dimensional components of selfhood (a 3-dimensional construct model for the complex experiential selfhood has been recently proposed based on the empirical findings on the functional-topographical specialization of 3 operational modules of brain functional network responsible for the self-consciousness processing) derived from the electroencephalographic (EEG) signal. The analysis revealed progressive (though not monotonous) restoration of EEG functional connectivity of 3 modules of brain functional network responsible for the self-consciousness processing, which was also paralleled by the clinically significant functional recovery. We propose that restoration of normal integrity of the operational modules of the self-referential brain network may underlie the positive dynamics of 3 aspects of selfhood and provide a neurobiological mechanism for their recovery. The results are discussed in the context of recent experimental studies that support this inference. Studies of ongoing recovery after severe brain injury utilizing knowledge about each separate aspect of complex selfhood will likely help to develop more efficient and targeted rehabilitation programs for patients with brain trauma.

  14. Source-space EEG neurofeedback links subjective experience with brain activity during effortless awareness meditation.

    Science.gov (United States)

    van Lutterveld, Remko; Houlihan, Sean D; Pal, Prasanta; Sacchet, Matthew D; McFarlane-Blake, Cinque; Patel, Payal R; Sullivan, John S; Ossadtchi, Alex; Druker, Susan; Bauer, Clemens; Brewer, Judson A

    2017-05-01

    Meditation is increasingly showing beneficial effects for psychiatric disorders. However, learning to meditate is not straightforward as there are no easily discernible outward signs of performance and thus no direct feedback is possible. As meditation has been found to correlate with posterior cingulate cortex (PCC) activity, we tested whether source-space EEG neurofeedback from the PCC followed the subjective experience of effortless awareness (a major component of meditation), and whether participants could volitionally control the signal. Sixteen novice meditators and sixteen experienced meditators participated in the study. Novice meditators were briefly trained to perform a basic meditation practice to induce the subjective experience of effortless awareness in a progressively more challenging neurofeedback test-battery. Experienced meditators performed a self-selected meditation practice to induce this state in the same test-battery. Neurofeedback was provided based on gamma-band (40-57Hz) PCC activity extracted using a beamformer algorithm. Associations between PCC activity and the subjective experience of effortless awareness were assessed by verbal probes. Both groups reported that decreased PCC activity corresponded with effortless awareness (Pneurofeedback to link an objective measure of brain activity with the subjective experience of effortless awareness, and suggest potential utility of this paradigm as a tool for meditation training. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving.

    Science.gov (United States)

    Zander, Thorsten O; Andreessen, Lena M; Berg, Angela; Bleuel, Maurice; Pawlitzki, Juliane; Zawallich, Lars; Krol, Laurens R; Gramann, Klaus

    2017-01-01

    We tested the applicability and signal quality of a 16 channel dry electroencephalography (EEG) system in a laboratory environment and in a car under controlled, realistic conditions. The aim of our investigation was an estimation how well a passive Brain-Computer Interface (pBCI) can work in an autonomous driving scenario. The evaluation considered speed and accuracy of self-applicability by an untrained person, quality of recorded EEG data, shifts of electrode positions on the head after driving-related movements, usability, and complexity of the system as such and wearing comfort over time. An experiment was conducted inside and outside of a stationary vehicle with running engine, air-conditioning, and muted radio. Signal quality was sufficient for standard EEG analysis in the time and frequency domain as well as for the use in pBCIs. While the influence of vehicle-induced interferences to data quality was insignificant, driving-related movements led to strong shifts in electrode positions. In general, the EEG system used allowed for a fast self-applicability of cap and electrodes. The assessed usability of the system was still acceptable while the wearing comfort decreased strongly over time due to friction and pressure to the head. From these results we conclude that the evaluated system should provide the essential requirements for an application in an autonomous driving context. Nevertheless, further refinement is suggested to reduce shifts of the system due to body movements and increase the headset's usability and wearing comfort.

  16. EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis

    Science.gov (United States)

    Mak, Joseph N.; McFarland, Dennis J.; Vaughan, Theresa M.; McCane, Lynn M.; Tsui, Phillippa Z.; Zeitlin, Debra J.; Sellers, Eric W.; Wolpaw, Jonathan R.

    2012-04-01

    The purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain-computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three types of EEG features were found to be good predictors of P300 BCI performance: (1) the root-mean-square amplitude and (2) the negative peak amplitude of the event-related potential to target stimuli (target ERP) at Fz, Cz, P3, Pz, and P4; and (3) EEG theta frequency (4.5-8 Hz) power at Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz. A statistical prediction model that used a subset of these features accounted for >60% of the variance in copy-spelling performance (p < 0.001, mean R2 = 0.6175). The correlations reflected between-subject, rather than within-subject, effects. The results enhance understanding of performance differences among P300 BCI users. The predictors found in this study might help in: (1) identifying suitable candidates for long-term P300 BCI operation; (2) assessing performance online. Further work on within-subject effects needs to be done to establish whether P300 BCI user performance could be improved by optimizing one or more of these EEG features.

  17. Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain.

    Science.gov (United States)

    Giacometti, Paolo; Perdue, Katherine L; Diamond, Solomon G

    2014-05-30

    Interpretation and analysis of electroencephalography (EEG) measurements relies on the correspondence of electrode scalp coordinates to structural and functional regions of the brain. An algorithm is introduced for automatic calculation of the International 10-20, 10-10, and 10-5 scalp coordinates of EEG electrodes on a boundary element mesh of a human head. The EEG electrode positions are then used to generate parcellation regions of the cerebral cortex based on proximity to the EEG electrodes. The scalp electrode calculation method presented in this study effectively and efficiently identifies EEG locations without prior digitization of coordinates. The average of electrode proximity parcellations of the cortex were tabulated with respect to structural and functional regions of the brain in a population of 20 adult subjects. Parcellations based on electrode proximity and EEG sensitivity were compared. The parcellation regions based on sensitivity and proximity were found to have 44.0 ± 11.3% agreement when demarcated by the International 10-20, 32.4 ± 12.6% by the 10-10, and 24.7 ± 16.3% by the 10-5 electrode positioning system. The EEG positioning algorithm is a fast and easy method of locating EEG scalp coordinates without the need for digitized electrode positions. The parcellation method presented summarizes the EEG scalp locations with respect to brain regions without computation of a full EEG forward model solution. The reference table of electrode proximity versus cortical regions may be used by experimenters to select electrodes that correspond to anatomical and functional regions of interest. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. Studies of the correlations between morphological brain changes on MRI and computerized EEG changes in schizophrenics

    International Nuclear Information System (INIS)

    Takeuchi, Kouzou

    1992-01-01

    Twenty eight schizophrenic patients, who ranged in age from 21 to 39 years with a mean of 30.2, and 21 age- and sex-matched normal volunteers were studied by magnetic resonance (MR) imaging and electroencephalography (EEG). ALl subjects were given informed consent prior to the present study. They were all right-handed. Schizophrenic patients showed a significantly larger ventricular brain ratio (VBR) on the axial and coronal planes as compared with the control. The bilateral anterior horns, left body, left posterior horn of the lateral ventricle and the third ventricle were significantly larger in schizophrenic patients than the control. The middle half of the corpus callosum was smaller in schizophrenic patients than the control. Schizophrenia was more likely associated not only with delta and theta activities in the centro-parieto-occipital regions but also with beta 1 and beta 2 activities in the front-central regions. In schizophrenic patients, however, alpha 2 activity was markedly decreased in all regions. There were significant positive correlations between the total scores for brief psychiatric rating scale (BPRS) and the areas of the left anterior and posterior horns of the lateral ventricle. The total BPRS scores positively correlated with the area of the third ventricle. In addition, positive correlations were significant between delata activity and the area of the left anterior horn of the lateral ventricle, between delta activity and the area of the third ventricle, and between beta 1 activity and the area of left posteior horn of the lateral ventricle. These results suggest that a dilated third ventricle is associated with electrophysiological brain pathology and psychopathology in schizophrenic patients. (N.K.) 76 refs

  19. Oscillatory brain correlates of live joint attention: a dual-EEG study

    Directory of Open Access Journals (Sweden)

    Fanny eLachat

    2012-06-01

    Full Text Available Joint attention consists in following another’s gaze onto an environmental object, which leads to the alignment of both subjects' attention onto this object. It is a fundamental mechanism of non-verbal communication, and it is essential for dynamic, online, interindividual synchronization during interactions. We aimed at investigating the oscillatory brain correlates of joint attention in a face-to-face paradigm where dyads of participants dynamically oriented their attention toward objects during joint and no-joint attention periods respectively. We also manipulated task instruction: in socially-driven instructions, the participants had to follow explicitly their partner’s gaze, while in color-driven instructions, the objects to be looked at were designated at by their color so that no explicit gaze following was required. We focused on oscillatory activities in the 10 Hz frequency range, where parieto-occipital alpha and the centro-parietal mu rhythms have been described, as these rhythms have been associated with attention and social coordination processes respectively. We tested the hypothesis of a modulation of these oscillatory activities by joint attention. We used dual EEG to record simultaneously the brain activities of the participant dyads during our live, face-to-face joint attention paradigm. We showed that joint attention periods – as compared to the no-joint attention periods – were associated with a decrease of signal power between 11 and 13 Hz over a large set of left centro-parieto-occipital electrodes, encompassing the scalp regions where alpha and mu rhythms have been described. This 11-13 Hz signal power decrease was observed independently of the task instruction: it was similar when joint versus no-joint attention situations were socially-driven and when they were color-driven. These results are interpreted in terms of the processes of attention mirroring, social coordination, and mutual attentiveness associated

  20. Comparative studies of '18F-FDG PET/CT brain imaging and EEG in preoperative localization of temporal lobe epileptic focus

    International Nuclear Information System (INIS)

    Chen Ziqian; Zhao Chunlei; Liu Yao; Ni Ping; Zhong Qun; Bai Wei; Peng Dexin

    2012-01-01

    Objective: To compare the value of 18 F-FDG PET/CT brain imaging and EEG in preoperative localization of the epileptic focus at the temporal lobe. Methods: A total of 152 patients (108 males, 44 females, age ranged from 3 to 59 years old) with past history of temporal lobe epilepsy were included.All patients underwent 18 F-FDG PET/CT brain imaging and long-range or video EEG, and 29 patients underwent intracranial electrode EEG due to the failure to localize the disease focus by non-invasive methods.Histopathologic findings after operative treatment were considered the gold standard for disease localization. All patients were followed up for at least six months after the operation. The accuracy of the 18 F-FDG PET/CT brain imaging and long-range or video EEG examination were compared using χ 2 test. Results: The accuracy of locating the epileptic focus was 80.92% (123/152) for 18 F-FDG PET/CT brain imaging and 43.42% (66/152) for long-range or video EEG (χ 2 =22.72, P<0.01). The accuracy of locating the epileptic focus for the 29 cases with intracranial electrode EEG was 100%. Conclusions: Interictal 18 F-FDG PET/CT brain imaging is a sensitive and effective method to locate the temporal lobe epileptic focus and is better than long-range or video EEG. The combination of 18 F-FDG PET/CT brain imaging and intracranial electrode EEG examination can further improve the accuracy of locating the epileptic focus. (authors)

  1. Brain-Computer Interfaces for 1-D and 2-D Cursor Control: Designs Using Volitional Control of the EEG Spectrum or Steady-State Visual Evoked Potentials

    Science.gov (United States)

    Trejo, Leonard J.; Matthews, Bryan; Rosipal, Roman

    2005-01-01

    We have developed and tested two EEG-based brain-computer interfaces (BCI) for users to control a cursor on a computer display. Our system uses an adaptive algorithm, based on kernel partial least squares classification (KPLS), to associate patterns in multichannel EEG frequency spectra with cursor controls. Our first BCI, Target Practice, is a system for one-dimensional device control, in which participants use biofeedback to learn voluntary control of their EEG spectra. Target Practice uses a KF LS classifier to map power spectra of 30-electrode EEG signals to rightward or leftward position of a moving cursor on a computer display. Three subjects learned to control motion of a cursor on a video display in multiple blocks of 60 trials over periods of up to six weeks. The best subject s average skill in correct selection of the cursor direction grew from 58% to 88% after 13 training sessions. Target Practice also implements online control of two artifact sources: a) removal of ocular artifact by linear subtraction of wavelet-smoothed vertical and horizontal EOG signals, b) control of muscle artifact by inhibition of BCI training during periods of relatively high power in the 40-64 Hz band. The second BCI, Think Pointer, is a system for two-dimensional cursor control. Steady-state visual evoked potentials (SSVEP) are triggered by four flickering checkerboard stimuli located in narrow strips at each edge of the display. The user attends to one of the four beacons to initiate motion in the desired direction. The SSVEP signals are recorded from eight electrodes located over the occipital region. A KPLS classifier is individually calibrated to map multichannel frequency bands of the SSVEP signals to right-left or up-down motion of a cursor on a computer display. The display stops moving when the user attends to a central fixation point. As for Target Practice, Think Pointer also implements wavelet-based online removal of ocular artifact; however, in Think Pointer muscle

  2. Using Brain Electrical Activity Mapping to Diagnose Learning Disabilities.

    Science.gov (United States)

    Torello, Michael, W.; Duffy, Frank H.

    1985-01-01

    Cognitive neuroscience assumes that measurement of brain electrical activity should relate to cognition. Brain Electrical Activity Mapping (BEAM), a non-invasive technique, is used to record changes in activity from one brain area to another and is 80 to 90 percent successful in classifying subjects as dyslexic or normal. (MT)

  3. Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

    Science.gov (United States)

    Lie, Octavian V; van Mierlo, Pieter

    2017-01-01

    The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

  4. Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems.

    Science.gov (United States)

    Chai, Rifai; Naik, Ganesh R; Ling, Sai Ho; Nguyen, Hung T

    2017-01-07

    One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.

  5. Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia.

    Science.gov (United States)

    Blokland, Yvonne; Spyrou, Loukianos; Thijssen, Dick; Eijsvogels, Thijs; Colier, Willy; Floor-Westerdijk, Marianne; Vlek, Rutger; Bruhn, Jorgen; Farquhar, Jason

    2014-03-01

    Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.

  6. Neural basis for brain responses to TV commercials: a high-resolution EEG study.

    Science.gov (United States)

    Astolfi, Laura; De Vico Fallani, F; Cincotti, F; Mattia, D; Bianchi, L; Marciani, M G; Salinari, S; Colosimo, A; Tocci, A; Soranzo, R; Babiloni, F

    2008-12-01

    We investigated brain activity during the observation of TV commercials by tracking the cortical activity and the functional connectivity changes in normal subjects. The aim was to elucidate if the TV commercials that were remembered by the subjects several days after their first observation elicited particular brain activity and connectivity compared with those generated during the observation of TV commercials that were quickly forgotten. High-resolution electroencephalogram (EEG) recordings were performed in a group of healthy subjects and the cortical activity during the observation of TV commercials was evaluated in several regions of interest coincident with the Brodmann areas (BAs). The patterns of cortical connectivity were obtained in the four principal frequency bands, Theta (3-7 Hz), Alpha (8-12 Hz), Beta (13-30 Hz), Gamma (30-40 Hz) and the directed influences between any given pair of the estimated cortical signals were evaluated by use of a multivariate spectral technique known as partial directed coherence. The topology of the cortical networks has been identified with tools derived from graph theory. Results suggest that the cortical activity and connectivity elicited by the viewing of the TV commercials that were remembered by the experimental subjects are markedly different from the brain activity elicited during the observation of the TV commercials that were forgotten. In particular, during the observation of the TV commercials that were remembered, the amount of cortical spectral activity from the frontal areas (BA 8 and 9) and from the parietal areas (BA 5, 7, and 40) is higher compared with the activity elicited by the observation of TV commercials that were forgotten. In addition, network analysis suggests a clear role of the parietal areas as a target of the incoming flow of information from all the other parts of the cortex during the observation of TV commercials that have been remembered. The techniques presented here shed new light on

  7. Mapping brain structure and function: cellular resolution, global perspective.

    Science.gov (United States)

    Zupanc, Günther K H

    2017-04-01

    A comprehensive understanding of the brain requires analysis, although from a global perspective, with cellular, and even subcellular, resolution. An important step towards this goal involves the establishment of three-dimensional high-resolution brain maps, incorporating brain-wide information about the cells and their connections, as well as the chemical architecture. The progress made in such anatomical brain mapping in recent years has been paralleled by the development of physiological techniques that enable investigators to generate global neural activity maps, also with cellular resolution, while simultaneously recording the organism's behavioral activity. Combination of the high-resolution anatomical and physiological maps, followed by theoretical systems analysis of the deduced network, will offer unprecedented opportunities for a better understanding of how the brain, as a whole, processes sensory information and generates behavior.

  8. Phase congruency map driven brain tumour segmentation

    Science.gov (United States)

    Szilágyi, Tünde; Brady, Michael; Berényi, Ervin

    2015-03-01

    Computer Aided Diagnostic (CAD) systems are already of proven value in healthcare, especially for surgical planning, nevertheless much remains to be done. Gliomas are the most common brain tumours (70%) in adults, with a survival time of just 2-3 months if detected at WHO grades III or higher. Such tumours are extremely variable, necessitating multi-modal Magnetic Resonance Images (MRI). The use of Gadolinium-based contrast agents is only relevant at later stages of the disease where it highlights the enhancing rim of the tumour. Currently, there is no single accepted method that can be used as a reference. There are three main challenges with such images: to decide whether there is tumour present and is so localize it; to construct a mask that separates healthy and diseased tissue; and to differentiate between the tumour core and the surrounding oedema. This paper presents two contributions. First, we develop tumour seed selection based on multiscale multi-modal texture feature vectors. Second, we develop a method based on a local phase congruency based feature map to drive level-set segmentation. The segmentations achieved with our method are more accurate than previously presented methods, particularly for challenging low grade tumours.

  9. Cyto- and receptor architectonic mapping of the human brain.

    Science.gov (United States)

    Palomero-Gallagher, Nicola; Zilles, Karl

    2018-01-01

    Mapping of the human brain is more than the generation of an atlas-based parcellation of brain regions using histologic or histochemical criteria. It is the attempt to provide a topographically informed model of the structural and functional organization of the brain. To achieve this goal a multimodal atlas of the detailed microscopic and neurochemical structure of the brain must be registered to a stereotaxic reference space or brain, which also serves as reference for topographic assignment of functional data, e.g., functional magnet resonance imaging, electroencephalography, or magnetoencephalography, as well as metabolic imaging, e.g., positron emission tomography. Although classic maps remain pioneering steps, they do not match recent concepts of the functional organization in many regions, and suffer from methodic drawbacks. This chapter provides a summary of the recent status of human brain mapping, which is based on multimodal approaches integrating results of quantitative cyto- and receptor architectonic studies with focus on the cerebral cortex in a widely used reference brain. Descriptions of the methods for observer-independent and statistically testable cytoarchitectonic parcellations, quantitative multireceptor mapping, and registration to the reference brain, including the concept of probability maps and a toolbox for using the maps in functional neuroimaging studies, are provided. Copyright © 2018 Elsevier B.V. All rights reserved.

  10. Transcranial direct current stimulation generates a transient increase of small-world in brain connectivity: an EEG graph theoretical analysis.

    Science.gov (United States)

    Vecchio, Fabrizio; Di Iorio, Riccardo; Miraglia, Francesca; Granata, Giuseppe; Romanello, Roberto; Bramanti, Placido; Rossini, Paolo Maria

    2018-04-01

    Transcranial direct current stimulation (tDCS) is a non-invasive technique able to modulate cortical excitability in a polarity-dependent way. At present, only few studies investigated the effects of tDCS on the modulation of functional connectivity between remote cortical areas. The aim of this study was to investigate-through graph theory analysis-how bipolar tDCS modulate cortical networks high-density EEG recordings were acquired before and after bipolar cathodal, anodal and sham tDCS involving the primary motor and pre-motor cortices of the dominant hemispherein 14 healthy subjects. Results showed that, after bipolar anodal tDCS stimulation, brain networks presented a less evident "small world" organization with a global tendency to be more random in its functional connections with respect to prestimulus condition in both hemispheres. Results suggest that tDCS globally modulates the cortical connectivity of the brain, modifying the underlying functional organization of the stimulated networks, which might be related to changes in synaptic efficiency of the motor network and related brain areas. This study demonstrated that graph analysis approach to EEG recordings is able to intercept changes in cortical functions mediated by bipolar anodal tDCS mainly involving the dominant M1 and related motor areas. Concluding, tDCS could be an useful technique to help understanding brain rhythms and their topographic functional organization and specificity.

  11. Post-task Effects on EEG Brain Activity Differ for Various Differential Learning and Contextual Interference Protocols

    Directory of Open Access Journals (Sweden)

    Diana Henz

    2018-01-01

    Full Text Available A large body of research has shown superior learning rates in variable practice compared to repetitive practice. More specifically, this has been demonstrated in the contextual interference (CI and in the differential learning (DL approach that are both representatives of variable practice. Behavioral studies have indicate different learning processes in CI and DL. Aim of the present study was to examine immediate post-task effects on electroencephalographic (EEG brain activation patterns after CI and DL protocols that reveal underlying neural processes at the early stage of motor consolidation. Additionally, we tested two DL protocols (gradual DL, chaotic DL to examine the effect of different degrees of stochastic fluctuations within the DL approach with a low degree of fluctuations in gradual DL and a high degree of fluctuations in chaotic DL. Twenty-two subjects performed badminton serves according to three variable practice protocols (CI, gradual DL, chaotic DL, and a repetitive learning protocol in a within-subjects design. Spontaneous EEG activity was measured before, and immediately after each 20-min practice session from 19 electrodes. Results showed distinguishable neural processes after CI, DL, and repetitive learning. Increases in EEG theta and alpha power were obtained in somatosensory regions (electrodes P3, P7, Pz, P4, P8 in both DL conditions compared to CI, and repetitive learning. Increases in theta and alpha activity in motor areas (electrodes C3, Cz, C4 were found after chaotic DL compared to gradual DL, and CI. Anterior areas (electrodes F3, F7, Fz, F4, F8 showed increased activity in the beta and gamma bands after CI. Alpha activity was increased in occipital areas (electrodes O1, O2 after repetitive learning. Post-task EEG brain activation patterns suggest that DL stimulates the somatosensory and motor system, and engages more regions of the cortex than repetitive learning due to a tighter stimulation of the motor and

  12. A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State.

    Science.gov (United States)

    Shin, Jaeyoung; Kwon, Jinuk; Im, Chang-Hwan

    2018-01-01

    The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the "one-versus-one" (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs ( p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 ± 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study.

  13. Resting-state EEG oscillatory dynamics in fragile X syndrome: abnormal functional connectivity and brain network organization.

    Directory of Open Access Journals (Sweden)

    Melle J W van der Molen

    Full Text Available Disruptions in functional connectivity and dysfunctional brain networks are considered to be a neurological hallmark of neurodevelopmental disorders. Despite the vast literature on functional brain connectivity in typical brain development, surprisingly few attempts have been made to characterize brain network integrity in neurodevelopmental disorders. Here we used resting-state EEG to characterize functional brain connectivity and brain network organization in eight males with fragile X syndrome (FXS and 12 healthy male controls. Functional connectivity was calculated based on the phase lag index (PLI, a non-linear synchronization index that is less sensitive to the effects of volume conduction. Brain network organization was assessed with graph theoretical analysis. A decrease in global functional connectivity was observed in FXS males for upper alpha and beta frequency bands. For theta oscillations, we found increased connectivity in long-range (fronto-posterior and short-range (frontal-frontal and posterior-posterior clusters. Graph theoretical analysis yielded evidence of increased path length in the theta band, suggesting that information transfer between brain regions is particularly impaired for theta oscillations in FXS. These findings are discussed in terms of aberrant maturation of neuronal oscillatory dynamics, resulting in an imbalance in excitatory and inhibitory neuronal circuit activity.

  14. Development of grouped icEEG for the study of cognitive processing

    Directory of Open Access Journals (Sweden)

    Cihan Mehmet Kadipasaoglu

    2015-07-01

    Full Text Available Invasive intracranial EEG (icEEG offers a unique opportunity to study human cognitive networks at an unmatched spatiotemporal resolution. To date, the contributions of icEEG have been limited to the individual-level analyses or cohorts whose data are not integrated in any way. Here we discuss how grouped approaches to icEEG overcome challenges related to sparse-sampling, correct for individual variations in response and provide statistically valid models of brain activity in a population. By the generation of whole-brain activity maps, grouped icEEG enables the study of intra and interregional dynamics between distributed cortical substrates exhibiting task-dependent activity. In this fashion, grouped icEEG analyses can provide significant advances in understanding the mechanisms by which cortical networks give rise to cognitive functions.

  15. Mutated Genes in Schizophrenia Map to Brain Networks

    Science.gov (United States)

    ... Matters NIH Research Matters August 12, 2013 Mutated Genes in Schizophrenia Map to Brain Networks Schizophrenia networks ... have a high number of spontaneous mutations in genes that form a network in the front region ...

  16. EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone

    Science.gov (United States)

    Debener, Stefan; Emkes, Reiner; Volkening, Nils; Fudickar, Sebastian; Bleichner, Martin G.

    2017-01-01

    Objective Our aim was the development and validation of a modular signal processing and classification application enabling online electroencephalography (EEG) signal processing on off-the-shelf mobile Android devices. The software application SCALA (Signal ProCessing and CLassification on Android) supports a standardized communication interface to exchange information with external software and hardware. Approach In order to implement a closed-loop brain-computer interface (BCI) on the smartphone, we used a multiapp framework, which integrates applications for stimulus presentation, data acquisition, data processing, classification, and delivery of feedback to the user. Main Results We have implemented the open source signal processing application SCALA. We present timing test results supporting sufficient temporal precision of audio events. We also validate SCALA with a well-established auditory selective attention paradigm and report above chance level classification results for all participants. Regarding the 24-channel EEG signal quality, evaluation results confirm typical sound onset auditory evoked potentials as well as cognitive event-related potentials that differentiate between correct and incorrect task performance feedback. Significance We present a fully smartphone-operated, modular closed-loop BCI system that can be combined with different EEG amplifiers and can easily implement other paradigms. PMID:29349070

  17. EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone.

    Science.gov (United States)

    Blum, Sarah; Debener, Stefan; Emkes, Reiner; Volkening, Nils; Fudickar, Sebastian; Bleichner, Martin G

    2017-01-01

    Our aim was the development and validation of a modular signal processing and classification application enabling online electroencephalography (EEG) signal processing on off-the-shelf mobile Android devices. The software application SCALA (Signal ProCessing and CLassification on Android) supports a standardized communication interface to exchange information with external software and hardware. In order to implement a closed-loop brain-computer interface (BCI) on the smartphone, we used a multiapp framework, which integrates applications for stimulus presentation, data acquisition, data processing, classification, and delivery of feedback to the user. We have implemented the open source signal processing application SCALA. We present timing test results supporting sufficient temporal precision of audio events. We also validate SCALA with a well-established auditory selective attention paradigm and report above chance level classification results for all participants. Regarding the 24-channel EEG signal quality, evaluation results confirm typical sound onset auditory evoked potentials as well as cognitive event-related potentials that differentiate between correct and incorrect task performance feedback. We present a fully smartphone-operated, modular closed-loop BCI system that can be combined with different EEG amplifiers and can easily implement other paradigms.

  18. EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone

    Directory of Open Access Journals (Sweden)

    Sarah Blum

    2017-01-01

    Full Text Available Objective. Our aim was the development and validation of a modular signal processing and classification application enabling online electroencephalography (EEG signal processing on off-the-shelf mobile Android devices. The software application SCALA (Signal ProCessing and CLassification on Android supports a standardized communication interface to exchange information with external software and hardware. Approach. In order to implement a closed-loop brain-computer interface (BCI on the smartphone, we used a multiapp framework, which integrates applications for stimulus presentation, data acquisition, data processing, classification, and delivery of feedback to the user. Main Results. We have implemented the open source signal processing application SCALA. We present timing test results supporting sufficient temporal precision of audio events. We also validate SCALA with a well-established auditory selective attention paradigm and report above chance level classification results for all participants. Regarding the 24-channel EEG signal quality, evaluation results confirm typical sound onset auditory evoked potentials as well as cognitive event-related potentials that differentiate between correct and incorrect task performance feedback. Significance. We present a fully smartphone-operated, modular closed-loop BCI system that can be combined with different EEG amplifiers and can easily implement other paradigms.

  19. Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

    Science.gov (United States)

    Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

    2017-08-01

    Objective. Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. Approach. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. Main results. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. Significance. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

  20. Sources of EEG activity most relevant to performance of brain-computer interface based on motor imagery

    Czech Academy of Sciences Publication Activity Database

    Frolov, A.; Húsek, Dušan; Bobrov, P.; Korshakov, A.V.; Chernikova, L.; Konovalov, R.; Mokienko, O.

    2012-01-01

    Roč. 22, č. 1 (2012), s. 21-37 ISSN 1210-0552 R&D Projects: GA ČR GAP202/10/0262 Grant - others:GA MŠk(CZ) ED1.1.00/02.0070 Program:ED Institutional research plan: CEZ:AV0Z10300504 Keywords : brain-computer interface * independent component analysis * pattern classification * motor imagery * inverse problem * fMRI * EEG Subject RIV: IN - Informatics, Computer Science Impact factor: 0.362, year: 2012

  1. Brain electric correlates of strong belief in paranormal phenomena: intracerebral EEG source and regional Omega complexity analyses.

    Science.gov (United States)

    Pizzagalli, D; Lehmann, D; Gianotti, L; Koenig, T; Tanaka, H; Wackermann, J; Brugger, P

    2000-12-22

    The neurocognitive processes underlying the formation and maintenance of paranormal beliefs are important for understanding schizotypal ideation. Behavioral studies indicated that both schizotypal and paranormal ideation are based on an overreliance on the right hemisphere, whose coarse rather than focussed semantic processing may favor the emergence of 'loose' and 'uncommon' associations. To elucidate the electrophysiological basis of these behavioral observations, 35-channel resting EEG was recorded in pre-screened female strong believers and disbelievers during resting baseline. EEG data were subjected to FFT-Dipole-Approximation analysis, a reference-free frequency-domain dipole source modeling, and Regional (hemispheric) Omega Complexity analysis, a linear approach estimating the complexity of the trajectories of momentary EEG map series in state space. Compared to disbelievers, believers showed: more right-located sources of the beta2 band (18.5-21 Hz, excitatory activity); reduced interhemispheric differences in Omega complexity values; higher scores on the Magical Ideation scale; more general negative affect; and more hypnagogic-like reveries after a 4-min eyes-closed resting period. Thus, subjects differing in their declared paranormal belief displayed different active, cerebral neural populations during resting, task-free conditions. As hypothesized, believers showed relatively higher right hemispheric activation and reduced hemispheric asymmetry of functional complexity. These markers may constitute the neurophysiological basis for paranormal and schizotypal ideation.

  2. Analysis of behavioral and EEG correlatives of attention in the dynamics of recovery of consciousness following severe brain injury

    Directory of Open Access Journals (Sweden)

    E. V. Sharova

    2016-01-01

    Full Text Available Objective: to determine the behavioral manifestations and electroencephalographic correlates of modality-nonspecific attention using a clinical model of severe brain injury (SBI.Patients and methods. 35 patients with SBI in the dynamics of post-coma recovery of mental activity (a study group and 23 healthy subjects (a control group were examined. The behavioral manifestations of NSA from coma to clear consciousness were analyzed in the patients. Changes in the pattern of EEG and in the indices of its coherence in the presence and activation of different forms of attention (an orienting response to the sound and eye opening; involuntary and voluntary visual forms, by applying specially developed computerized techniques, were investigated. The features of associated with attention changes in interhemispheric EEG coherence (IHC with the data of 3T diffusion tensor tractography of the corpus callosum (CC were compared.Results. Attention disorders were shown to be essential and an «axial disorder» in patients with SBI. There were statistically confirmed qualitative and quantitative differences attention-associated changes in the EEG pattern and IHC in reversible and chronic unconsciousness. The important favorable prognostic sign proved to be reactive changes in interhemispheric EEG relations, including frontal ones characterized by the absence of clear external manifestations of consciousness in the very earliest stages. There was a significant correlation between the preservation of CC tracts (primarily, the rostrum, anterior portion, and splenium and attention-related reactivity of IHC, which reflects the specific, though nonrigid, structural determinacy of the latter.

  3. Exploring resting-state EEG brain oscillatory activity in relation to cognitive functioning in multiple sclerosis.

    Science.gov (United States)

    Keune, Philipp M; Hansen, Sascha; Weber, Emily; Zapf, Franziska; Habich, Juliane; Muenssinger, Jana; Wolf, Sebastian; Schönenberg, Michael; Oschmann, Patrick

    2017-09-01

    Neurophysiologic monitoring parameters related to cognition in Multiple Sclerosis (MS) are sparse. Previous work reported an association between magnetoencephalographic (MEG) alpha-1 activity and information processing speed. While this remains to be replicated by more available electroencephalographic (EEG) methods, also other established EEG markers, e.g. the slow-wave/fast-wave ratio (theta/beta ratio), remain to be explored in this context. Performance on standard tests addressing information processing speed and attention (Symbol-Digit Modalities Test, SDMT; Test of Attention Performance, TAP) was examined in relation to resting-state EEG alpha-1 and alpha-2 activity and the theta/beta ratio in 25MS patients. Increased global alpha-1 and alpha-2 activity and an increased frontal theta/beta ratio (pronounced slow-wave relative to fast-wave activity) were associated with lower SDMT processing speed. In an exploratory analysis, clinically impaired attention was associated with a significantly increased frontal theta/beta ratio whereas alpha power did not show sensitivity to clinical impairment. EEG global alpha power and the frontal theta/beta ratio were both associated with attention. The theta/beta ratio involved potential clinical sensitivity. Resting-state EEG recordings can be obtained during the routine clinical process. The examined resting-state measures may represent feasible monitoring parameters in MS. This notion should be explored in future intervention studies. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  4. EEG Brain Wave Activity at Rest and during Evoked Attention in Children with Attention-Deficit/Hyperactivity Disorder and Effects of Methylphenidate.

    Science.gov (United States)

    Thomas, Bianca Lee; Viljoen, Margaretha

    2016-01-01

    The aim of this study was to assess baseline EEG brain wave activity in children with attention-deficit/hyperactivity disorder (ADHD) and to examine the effects of evoked attention and methylphenidate on this activity. Children with ADHD (n = 19) were tested while they were stimulant free and during a period in which they were on stimulant (methylphenidate) medication. Control subjects (n = 18) were tested once. EEG brain wave activity was tested both at baseline and during focussed attention. Attention was evoked and EEG brain wave activity was determined by means of the BioGraph Infiniti biofeedback apparatus. The main finding of this study was that control subjects and stimulant-free children with ADHD exhibited the expected reactivity in high alpha-wave activity (11-12 Hz) from baseline to focussed attention; however, methylphenidate appeared to abolish this reactivity. Methylphenidate attenuates the normal cortical response to a cognitive challenge. © 2016 S. Karger AG, Basel.

  5. [EEG and brain-stem evoked potentials in 125 recent concussions].

    Science.gov (United States)

    Geets, W; Louette, N

    1983-12-01

    EEG and ipsi/contralateral BEPs have been recorded in 125 cases of concussion at most 48 h after the cerebral trauma. In 100 cases of minor concussion the temporary loss of consciousness lasted not more than 2 min. In 25 cases of mild concussion, the loss of consciousness lasted until their arrival at the hospital. In minor concussions an abnormal EEG was found in 17% of the cases and in mild concussions, in 56%. The abnormalities of the BEP, more often seen in mild concussions (60%) than in minor concussions (8%), are an increase of interpeak latencies or distorted responses with average to bad reproducibility. The results are discussed.

  6. Novel Methods for Measuring Depth of Anesthesia by Quantifying Dominant Information Flow in Multichannel EEGs

    Directory of Open Access Journals (Sweden)

    Kab-Mun Cha

    2017-01-01

    Full Text Available In this paper, we propose novel methods for measuring depth of anesthesia (DOA by quantifying dominant information flow in multichannel EEGs. Conventional methods mainly use few EEG channels independently and most of multichannel EEG based studies are limited to specific regions of the brain. Therefore the function of the cerebral cortex over wide brain regions is hardly reflected in DOA measurement. Here, DOA is measured by the quantification of dominant information flow obtained from principle bipartition. Three bipartitioning methods are used to detect the dominant information flow in entire EEG channels and the dominant information flow is quantified by calculating information entropy. High correlation between the proposed measures and the plasma concentration of propofol is confirmed from the experimental results of clinical data in 39 subjects. To illustrate the performance of the proposed methods more easily we present the results for multichannel EEG on a two-dimensional (2D brain map.

  7. Localization of spontaneous bursting neuronal activity in the preterm human brain with simultaneous EEG-fMRI.

    Science.gov (United States)

    Arichi, Tomoki; Whitehead, Kimberley; Barone, Giovanni; Pressler, Ronit; Padormo, Francesco; Edwards, A David; Fabrizi, Lorenzo

    2017-09-12

    Electroencephalographic recordings from the developing human brain are characterized by spontaneous neuronal bursts, the most common of which is the delta brush. Although similar events in animal models are known to occur in areas of immature cortex and drive their development, their origin in humans has not yet been identified. Here, we use simultaneous EEG-fMRI to localise the source of delta brush events in 10 preterm infants aged 32-36 postmenstrual weeks. The most frequent patterns were left and right posterior-temporal delta brushes which were associated in the left hemisphere with ipsilateral BOLD activation in the insula only; and in the right hemisphere in both the insular and temporal cortices. This direct measure of neural and hemodynamic activity shows that the insula, one of the most densely connected hubs in the developing cortex, is a major source of the transient bursting events that are critical for brain maturation.

  8. Resting and reactive frontal brain electrical activity (EEG among a non-clinical sample of socially anxious adults: Does concurrent depressive mood matter?

    Directory of Open Access Journals (Sweden)

    Elliott A Beaton

    2008-03-01

    Full Text Available Elliott A Beaton1, Louis A Schmidt2, Andrea R Ashbaugh2,5, Diane L Santesso2, Martin M Antony1,3,4, Randi E McCabe1,31Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada; 2Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, Canada; 3Anxiety Treatment and Research Centre, St. Joseph’s Healthcare, Hamilton, Ontario, Canada; 4Department of Psychology, Ryerson University, Toronto, Ontario, Canada; 5Concordia University, Montreal, Quebec, CanadaAbstract: A number of studies have noted that the pattern of resting frontal brain electrical activity (EEG is related to individual differences in affective style in healthy infants, children, and adults and some clinical populations when symptoms are reduced or in remission. We measured self-reported trait shyness and sociability, concurrent depressive mood, and frontal brain electrical activity (EEG at rest and in anticipation of a speech task in a non-clinical sample of healthy young adults selected for high and low social anxiety. Although the patterns of resting and reactive frontal EEG asymmetry did not distinguish among individual differences in social anxiety, the pattern of resting frontal EEG asymmetry was related to trait shyness after controlling for concurrent depressive mood. Individuals who reported a higher degree of shyness were likely to exhibit greater relative right frontal EEG activity at rest. However, trait shyness was not related to frontal EEG asymmetry measured during the speech-preparation task, even after controlling for concurrent depressive mood. These findings replicate and extend prior work on resting frontal EEG asymmetry and individual differences in affective style in adults. Findings also highlight the importance of considering concurrent emotional states of participants when examining psychophysiological correlates of personality.Keywords: social anxiety, shyness, sociability

  9. EEG: Origin and measurement

    NARCIS (Netherlands)

    Lopes da Silva, F.; Mulert, C.; Lemieux, L.

    2010-01-01

    The existence of the electrical activity of the brain (i.e. the electroencephalogram or EEG) was discovered more than a century ago by Caton. After the demonstration that the EEG could be recorded from the human scalp by Berger in the 1920s, it made a slow start before it became accepted as a method

  10. High-density EEG characterization of brain responses to auditory rhythmic stimuli during wakefulness and NREM sleep.

    Science.gov (United States)

    Lustenberger, Caroline; Patel, Yogi A; Alagapan, Sankaraleengam; Page, Jessica M; Price, Betsy; Boyle, Michael R; Fröhlich, Flavio

    2018-04-01

    Auditory rhythmic sensory stimulation modulates brain oscillations by increasing phase-locking to the temporal structure of the stimuli and by increasing the power of specific frequency bands, resulting in Auditory Steady State Responses (ASSR). The ASSR is altered in different diseases of the central nervous system such as schizophrenia. However, in order to use the ASSR as biological markers for disease states, it needs to be understood how different vigilance states and underlying brain activity affect the ASSR. Here, we compared the effects of auditory rhythmic stimuli on EEG brain activity during wake and NREM sleep, investigated the influence of the presence of dominant sleep rhythms on the ASSR, and delineated the topographical distribution of these modulations. Participants (14 healthy males, 20-33 years) completed on the same day a 60 min nap session and two 30 min wakefulness sessions (before and after the nap). During these sessions, amplitude modulated (AM) white noise auditory stimuli at different frequencies were applied. High-density EEG was continuously recorded and time-frequency analyses were performed to assess ASSR during wakefulness and NREM periods. Our analysis revealed that depending on the electrode location, stimulation frequency applied and window/frequencies analysed the ASSR was significantly modulated by sleep pressure (before and after sleep), vigilance state (wake vs. NREM sleep), and the presence of slow wave activity and sleep spindles. Furthermore, AM stimuli increased spindle activity during NREM sleep but not during wakefulness. Thus, (1) electrode location, sleep history, vigilance state and ongoing brain activity needs to be carefully considered when investigating ASSR and (2) auditory rhythmic stimuli during sleep might represent a powerful tool to boost sleep spindles. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Topographic pharmaco-EEG mapping of the effects of the South American psychoactive beverage ayahuasca in healthy volunteers

    Science.gov (United States)

    Riba, Jordi; Anderer, Peter; Morte, Adelaida; Urbano, Gloria; Jané, Francesc; Saletu, Bernd; Barbanoj, Manel J

    2002-01-01

    Aims Ayahuasca is a traditional South American psychoactive beverage used in Amazonian shamanism, and in the religious ceremonies of Brazilian-based syncretic religious groups with followers in the US and several European countries. This tea contains measurable amounts of the psychotropic indole N,N-dimethyltryptamine (DMT), and β-carboline alkaloids with MAO-inhibiting properties. In a previous report we described a profile of stimulant and psychedelic effects for ayahuasca as measured by subjective report self-assessment instruments. In the present study the cerebral bioavailability and time-course of effects of ayahuasca were assessed in humans by means of topographic quantitative-electroencephalography (q-EEG), a noninvasive method measuring drug-induced variations in brain electrical activity. Methods Two doses (one low and one high) of encapsulated freeze-dried ayahuasca, equivalent to 0.6 and 0.85 mg DMT kg−1 body weight, were administered to 18 healthy volunteers with previous experience in psychedelic drug use in a double-blind crossover placebo-controlled clinical trial. Nineteen-lead recordings were undertaken from baseline to 8 h after administration. Subjective effects were measured by means of the Hallucinogen Rating Scale (HRS). Results Ayahuasca induced a pattern of psychoactive effects which resulted in significant dose-dependent increases in all subscales of the HRS, and in significant and dose-dependent modifications of brain electrical activity. Absolute power decreased in all frequency bands, most prominently in the theta band. Mean absolute power decreases (95% CI) at a representative lead (P3) 90 min after the high dose were −20.20±15.23 µV2 and −2.70±2.21 µV2 for total power and theta power, respectively. Relative power decreased in the delta (−1.20±1.31% after 120 min at P3) and theta (−3.30±2.59% after 120 min at P3) bands, and increased in the beta band, most prominently in the faster beta-3 (1.00±0.88% after 90 min at P

  12. myBrain: a novel EEG embedded system for epilepsy monitoring.

    Science.gov (United States)

    Pinho, Francisco; Cerqueira, João; Correia, José; Sousa, Nuno; Dias, Nuno

    2017-10-01

    The World Health Organisation has pointed that a successful health care delivery, requires effective medical devices as tools for prevention, diagnosis, treatment and rehabilitation. Several studies have concluded that longer monitoring periods and outpatient settings might increase diagnosis accuracy and success rate of treatment selection. The long-term monitoring of epileptic patients through electroencephalography (EEG) has been considered a powerful tool to improve the diagnosis, disease classification, and treatment of patients with such condition. This work presents the development of a wireless and wearable EEG acquisition platform suitable for both long-term and short-term monitoring in inpatient and outpatient settings. The developed platform features 32 passive dry electrodes, analogue-to-digital signal conversion with 24-bit resolution and a variable sampling frequency from 250 Hz to 1000 Hz per channel, embedded in a stand-alone module. A computer-on-module embedded system runs a Linux ® operating system that rules the interface between two software frameworks, which interact to satisfy the real-time constraints of signal acquisition as well as parallel recording, processing and wireless data transmission. A textile structure was developed to accommodate all components. Platform performance was evaluated in terms of hardware, software and signal quality. The electrodes were characterised through electrochemical impedance spectroscopy and the operating system performance running an epileptic discrimination algorithm was evaluated. Signal quality was thoroughly assessed in two different approaches: playback of EEG reference signals and benchmarking with a clinical-grade EEG system in alpha-wave replacement and steady-state visual evoked potential paradigms. The proposed platform seems to efficiently monitor epileptic patients in both inpatient and outpatient settings and paves the way to new ambulatory clinical regimens as well as non-clinical EEG

  13. A Single Session of rTMS Enhances Small-Worldness in Writer’s Cramp: Evidence from Simultaneous EEG-fMRI Multi-Modal Brain Graph

    Directory of Open Access Journals (Sweden)

    Rose D. Bharath

    2017-09-01

    Full Text Available Background and Purpose: Repetitive transcranial magnetic stimulation (rTMS induces widespread changes in brain connectivity. As the network topology differences induced by a single session of rTMS are less known we undertook this study to ascertain whether the network alterations had a small-world morphology using multi-modal graph theory analysis of simultaneous EEG-fMRI.Method: Simultaneous EEG-fMRI was acquired in duplicate before (R1 and after (R2 a single session of rTMS in 14 patients with Writer’s Cramp (WC. Whole brain neuronal and hemodynamic network connectivity were explored using the graph theory measures and clustering coefficient, path length and small-world index were calculated for EEG and resting state fMRI (rsfMRI. Multi-modal graph theory analysis was used to evaluate the correlation of EEG and fMRI clustering coefficients.Result: A single session of rTMS was found to increase the clustering coefficient and small-worldness significantly in both EEG and fMRI (p < 0.05. Multi-modal graph theory analysis revealed significant modulations in the fronto-parietal regions immediately after rTMS. The rsfMRI revealed additional modulations in several deep brain regions including cerebellum, insula and medial frontal lobe.Conclusion: Multi-modal graph theory analysis of simultaneous EEG-fMRI can supplement motor physiology methods in understanding the neurobiology of rTMS in vivo. Coinciding evidence from EEG and rsfMRI reports small-world morphology for the acute phase network hyper-connectivity indicating changes ensuing low-frequency rTMS is probably not “noise”.

  14. Mapping human whole-brain structural networks with diffusion MRI.

    Directory of Open Access Journals (Sweden)

    Patric Hagmann

    Full Text Available Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world.

  15. Reflectance diffuse optical tomography. Its application to human brain mapping

    International Nuclear Information System (INIS)

    Ueda, Yukio; Yamanaka, Takeshi; Yamashita, Daisuke; Suzuki, Toshihiko; Ohmae, Etsuko; Oda, Motoki; Yamashita, Yutaka

    2005-01-01

    We report the successful application of reflectance diffuse optical tomography (DOT) using near-infrared light with the new reconstruction algorithm that we developed to the observation of regional hemodynamic changes in the brain under specific mental tasks. Our results reveal the heterogeneous distribution of oxyhemoglobin and deoxyhemoglobin in the brain, showing complementary images of oxyhemoglobin and deoxyhemoglobin changes in certain regions. We conclude that our reflectance DOT has practical potential for human brain mapping, as well as in the diagnostic imaging of brain diseases. (author)

  16. Tactile exploration of virtual objects for blind and sighted people: the role of beta 1 EEG band in sensory substitution and supramodal mental mapping

    OpenAIRE

    Campus, C.; Brayda, L.; De Carli, F.; Chellali, R.; Famà, F.; Bruzzo, C.; Lucagrossi, L.; Rodriguez, G.

    2012-01-01

    The neural correlates of exploration and cognitive mapping in blindness remain elusive. The role of visuo-spatial pathways in blind vs. sighted subjects is still under debate. In this preliminary study, we investigate, as a possible estimation of the activity in the visuo-spatial pathways, the EEG patterns of blind and blindfolded-sighted subjects during the active tactile construction of cognitive maps from virtual objects compared with rest and passive tactile stimulation. Ten blind and ten...

  17. Brain mapping in tumors: intraoperative or extraoperative?

    Science.gov (United States)

    Duffau, Hugues

    2013-12-01

    In nontumoral epilepsy surgery, the main goal for all preoperative investigation is to first determine the epileptogenic zone, and then to analyze its relation to eloquent cortex, in order to control seizures while avoiding adverse postoperative neurologic outcome. To this end, in addition to neuropsychological assessment, functional neuroimaging and scalp electroencephalography, extraoperative recording, and electrical mapping, especially using subdural strip- or grid-electrodes, has been reported extensively. Nonetheless, in tumoral epilepsy surgery, the rationale is different. Indeed, the first aim is rather to maximize the extent of tumor resection while minimizing postsurgical morbidity, in order to increase the median survival as well as to preserve quality of life. As a consequence, as frequently seen in infiltrating tumors such as gliomas, where these lesions not only grow but also migrate along white matter tracts, the resection should be performed according to functional boundaries both at cortical and subcortical levels. With this in mind, extraoperative mapping by strips/grids is often not sufficient in tumoral surgery, since in essence, it allows study of the cortex but cannot map subcortical pathways. Therefore, intraoperative electrostimulation mapping, especially in awake patients, is more appropriate in tumor surgery, because this technique allows real-time detection of areas crucial for cerebral functions--eloquent cortex and fibers--throughout the resection. In summary, rather than choosing one or the other of different mapping techniques, methodology should be adapted to each pathology, that is, extraoperative mapping in nontumoral epilepsy surgery and intraoperative mapping in tumoral surgery. Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.

  18. EEG-based decoding of error-related brain activity in a real-world driving task

    Science.gov (United States)

    Zhang, H.; Chavarriaga, R.; Khaliliardali, Z.; Gheorghe, L.; Iturrate, I.; Millán, J. d. R.

    2015-12-01

    Objectives. Recent studies have started to explore the implementation of brain-computer interfaces (BCI) as part of driving assistant systems. The current study presents an EEG-based BCI that decodes error-related brain activity. Such information can be used, e.g., to predict driver’s intended turning direction before reaching road intersections. Approach. We executed experiments in a car simulator (N = 22) and a real car (N = 8). While subject was driving, a directional cue was shown before reaching an intersection, and we classified the presence or not of an error-related potentials from EEG to infer whether the cued direction coincided with the subject’s intention. In this protocol, the directional cue can correspond to an estimation of the driving direction provided by a driving assistance system. We analyzed ERPs elicited during normal driving and evaluated the classification performance in both offline and online tests. Results. An average classification accuracy of 0.698 ± 0.065 was obtained in offline experiments in the car simulator, while tests in the real car yielded a performance of 0.682 ± 0.059. The results were significantly higher than chance level for all cases. Online experiments led to equivalent performances in both simulated and real car driving experiments. These results support the feasibility of decoding these signals to help estimating whether the driver’s intention coincides with the advice provided by the driving assistant in a real car. Significance. The study demonstrates a BCI system in real-world driving, extending the work from previous simulated studies. As far as we know, this is the first online study in real car decoding driver’s error-related brain activity. Given the encouraging results, the paradigm could be further improved by using more sophisticated machine learning approaches and possibly be combined with applications in intelligent vehicles.

  19. A brain-computer interface for potential non-verbal facial communication based on EEG signals related to specific emotions.

    Science.gov (United States)

    Kashihara, Koji

    2014-01-01

    Unlike assistive technology for verbal communication, the brain-machine or brain-computer interface (BMI/BCI) has not been established as a non-verbal communication tool for amyotrophic lateral sclerosis (ALS) patients. Face-to-face communication enables access to rich emotional information, but individuals suffering from neurological disorders, such as ALS and autism, may not express their emotions or communicate their negative feelings. Although emotions may be inferred by looking at facial expressions, emotional prediction for neutral faces necessitates advanced judgment. The process that underlies brain neuronal responses to neutral faces and causes emotional changes remains unknown. To address this problem, therefore, this study attempted to decode conditioned emotional reactions to neutral face stimuli. This direction was motivated by the assumption that if electroencephalogram (EEG) signals can be used to detect patients' emotional responses to specific inexpressive faces, the results could be incorporated into the design and development of BMI/BCI-based non-verbal communication tools. To these ends, this study investigated how a neutral face associated with a negative emotion modulates rapid central responses in face processing and then identified cortical activities. The conditioned neutral face-triggered event-related potentials that originated from the posterior temporal lobe statistically significantly changed during late face processing (600-700 ms) after stimulus, rather than in early face processing activities, such as P1 and N170 responses. Source localization revealed that the conditioned neutral faces increased activity in the right fusiform gyrus (FG). This study also developed an efficient method for detecting implicit negative emotional responses to specific faces by using EEG signals. A classification method based on a support vector machine enables the easy classification of neutral faces that trigger specific individual emotions. In

  20. Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain.

    Science.gov (United States)

    Wang, Xingyuan; Meng, Juan; Tan, Guilin; Zou, Lixian

    2010-04-27

    Using phase space reconstruct technique from one-dimensional and multi-dimensional time series and the quantitative criterion rule of system chaos, and combining the neural network; analyses, computations and sort are conducted on electroencephalogram (EEG) signals of five kinds of human consciousness activities (relaxation, mental arithmetic of multiplication, mental composition of a letter, visualizing a 3-dimensional object being revolved about an axis, and visualizing numbers being written or erased on a blackboard). Through comparative studies on the determinacy, the phase graph, the power spectra, the approximate entropy, the correlation dimension and the Lyapunov exponent of EEG signals of 5 kinds of consciousness activities, the following conclusions are shown: (1) The statistic results of the deterministic computation indicate that chaos characteristic may lie in human consciousness activities, and central tendency measure (CTM) is consistent with phase graph, so it can be used as a division way of EEG attractor. (2) The analyses of power spectra show that ideology of single subject is almost identical but the frequency channels of different consciousness activities have slight difference. (3) The approximate entropy between different subjects exist discrepancy. Under the same conditions, the larger the approximate entropy of subject is, the better the subject's innovation is. (4) The results of the correlation dimension and the Lyapunov exponent indicate that activities of human brain exist in attractors with fractional dimensions. (5) Nonlinear quantitative criterion rule, which unites the neural network, can classify different kinds of consciousness activities well. In this paper, the results of classification indicate that the consciousness activity of arithmetic has better differentiation degree than that of abstract.

  1. Analysis of brain activity and response to colour stimuli during learning tasks: an EEG study

    Science.gov (United States)

    Folgieri, Raffaella; Lucchiari, Claudio; Marini, Daniele

    2013-02-01

    The research project intends to demonstrate how EEG detection through BCI device can improve the analysis and the interpretation of colours-driven cognitive processes through the combined approach of cognitive science and information technology methods. To this end, firstly it was decided to design an experiment based on comparing the results of the traditional (qualitative and quantitative) cognitive analysis approach with the EEG signal analysis of the evoked potentials. In our case, the sensorial stimulus is represented by the colours, while the cognitive task consists in remembering the words appearing on the screen, with different combination of foreground (words) and background colours. In this work we analysed data collected from a sample of students involved in a learning process during which they received visual stimuli based on colour variation. The stimuli concerned both the background of the text to learn and the colour of the characters. The experiment indicated some interesting results concerning the use of primary (RGB) and complementary (CMY) colours.

  2. [Digital electroencephalography in brain death diagnostics : Technical requirements and results of a survey on the compatibility with medical guidelines of digital EEG systems from providers in Germany].

    Science.gov (United States)

    Walter, U; Noachtar, S; Hinrichs, H

    2018-02-01

    The guidelines of the German Medical Association and the German Society for Clinical Neurophysiology and Functional Imaging (DGKN) require a high procedural and technical standard for electroencephalography (EEG) as an ancillary method for diagnosing the irreversible cessation of brain function (brain death). Nowadays, digital EEG systems are increasingly being applied in hospitals. So far it is unclear to what extent the digital EEG systems currently marketed in Germany meet the guidelines for diagnosing brain death. In the present article, the technical und safety-related requirements for digital EEG systems and the EEG documentation for diagnosing brain death are described in detail. On behalf of the DGKN, the authors sent out a questionnaire to all identified distributors of digital EEG systems in Germany with respect to the following technical demands: repeated recording of the calibration signals during an ongoing EEG recording, repeated recording of all electrode impedances during an ongoing EEG recording, assessability of intrasystem noise and galvanic isolation of measurement earthing from earthing conductor (floating input). For 15 of the identified 20 different digital EEG systems the specifications were provided by the distributors (among them all distributors based in Germany). All of these EEG systems are provided with a galvanic isolation (floating input). The internal noise can be tested with all systems; however, some systems do not allow repeated recording of the calibration signals and/or the electrode impedances during an ongoing EEG recording. The majority but not all of the currently available digital EEG systems offered for clinical use are eligible for use in brain death diagnostics as per German guidelines.

  3. To what extent can dry and water-based EEG electrodes replace conductive gel ones?: A Steady State Visual Evoked Potential Brain-Computer Interface Case Study

    NARCIS (Netherlands)

    Mihajlovic, V.; Garcia Molina, G.; Peuscher, J

    2011-01-01

    Recent technological advances in the field of skin electrodes and on-body sensors indicate a possibility of having an alternative to the traditionally used conductive gel electrodes for measuring electrical signals of the brain (electroencephalogram, EEG). This paper evaluates whether water-based

  4. Brain functional connectivity during the experience of thought blocks in schizophrenic patients with persistent auditory verbal hallucinations: an EEG study.

    Science.gov (United States)

    Angelopoulos, Elias; Koutsoukos, Elias; Maillis, Antonis; Papadimitriou, George N; Stefanis, Costas

    2014-03-01

    Thought blocks (TBs) are characterized by regular interruptions in the stream of thought. Outward signs are abrupt and repeated interruptions in the flow of conversation or actions while subjective experience is that of a total and uncontrollable emptying of the mind. In the very limited bibliography regarding TB, the phenomenon is thought to be conceptualized as a disturbance of consciousness that can be attributed to stoppages of continuous information processing due to an increase in the volume of information to be processed. In an attempt to investigate potential expression of the phenomenon on the functional properties of electroencephalographic (EEG) activity, an EEG study was contacted in schizophrenic patients with persisting auditory verbal hallucinations (AVHs) who additionally exhibited TBs. In this case, we hypothesized that the persistent and dense AVHs could serve the role of an increased information flow that the brain is unable to process, a condition that is perceived by the person as TB. Phase synchronization analyses performed on EEG segments during the experience of TBs showed that synchrony values exhibited a long-range common mode of coupling (grouped behavior) among the left temporal area and the remaining central and frontal brain areas. These common synchrony-fluctuation schemes were observed for 0.5 to 2s and were detected in a 4-s window following the estimated initiation of the phenomenon. The observation was frequency specific and detected in the broad alpha band region (6-12Hz). The introduction of synchrony entropy (SE) analysis applied on the cumulative synchrony distribution showed that TB states were characterized by an explicit preference of the system to be functioned at low values of synchrony, while the synchrony values are broadly distributed during the recovery state. Our results indicate that during TB states, the phase locking of several brain areas were converged uniformly in a narrow band of low synchrony values and in a

  5. EEG-based emergency braking intention prediction for brain-controlled driving considering one electrode falling-off.

    Science.gov (United States)

    Huikang Wang; Luzheng Bi; Teng Teng

    2017-07-01

    This paper proposes a novel method of electroencephalography (EEG)-based driver emergency braking intention detection system for brain-controlled driving considering one electrode falling-off. First, whether one electrode falls off is discriminated based on EEG potentials. Then, the missing signals are estimated by using the signals collected from other channels based on multivariate linear regression. Finally, a linear decoder is applied to classify driver intentions. Experimental results show that the falling-off discrimination accuracy is 99.63% on average and the correlation coefficient and root mean squared error (RMSE) between the estimated and experimental data are 0.90 and 11.43 μV, respectively, on average. Given one electrode falls off, the system accuracy of the proposed intention prediction method is significantly higher than that of the original method (95.12% VS 79.11%) and is close to that (95.95%) of the original system under normal situations (i. e., no electrode falling-off).

  6. An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain computer interface

    Science.gov (United States)

    Wang, Tao; He, Bin

    2004-03-01

    The recognition of mental states during motor imagery tasks is crucial for EEG-based brain computer interface research. We have developed a new algorithm by means of frequency decomposition and weighting synthesis strategy for recognizing imagined right- and left-hand movements. A frequency range from 5 to 25 Hz was divided into 20 band bins for each trial, and the corresponding envelopes of filtered EEG signals for each trial were extracted as a measure of instantaneous power at each frequency band. The dimensionality of the feature space was reduced from 200 (corresponding to 2 s) to 3 by down-sampling of envelopes of the feature signals, and subsequently applying principal component analysis. The linear discriminate analysis algorithm was then used to classify the features, due to its generalization capability. Each frequency band bin was weighted by a function determined according to the classification accuracy during the training process. The present classification algorithm was applied to a dataset of nine human subjects, and achieved a success rate of classification of 90% in training and 77% in testing. The present promising results suggest that the present classification algorithm can be used in initiating a general-purpose mental state recognition based on motor imagery tasks.

  7. Template based rodent brain extraction and atlas mapping.

    Science.gov (United States)

    Weimin Huang; Jiaqi Zhang; Zhiping Lin; Su Huang; Yuping Duan; Zhongkang Lu

    2016-08-01

    Accurate rodent brain extraction is the basic step for many translational studies using MR imaging. This paper presents a template based approach with multi-expert refinement to automatic rodent brain extraction. We first build the brain appearance model based on the learning exemplars. Together with the template matching, we encode the rodent brain position into the search space to reliably locate the rodent brain and estimate the rough segmentation. With the initial mask, a level-set segmentation and a mask-based template learning are implemented further to the brain region. The multi-expert fusion is used to generate a new mask. We finally combine the region growing based on the histogram distribution learning to delineate the final brain mask. A high-resolution rodent atlas is used to illustrate that the segmented low resolution anatomic image can be well mapped to the atlas. Tested on a public data set, all brains are located reliably and we achieve the mean Jaccard similarity score at 94.99% for brain segmentation, which is a statistically significant improvement compared to two other rodent brain extraction methods.

  8. Stimulus-response mappings shape inhibition processes: a combined EEG-fMRI study of contextual stopping.

    Directory of Open Access Journals (Sweden)

    Christina F Lavallee

    Full Text Available Humans are rarely faced with one simple task, but are typically confronted with complex stimulus constellations and varying stimulus-relevance in a given situation. Through modifying the prototypical stop-signal task and by combined recording and analysis of electroencephalography (EEG and functional magnetic resonance imaging (fMRI, we studied the effects of stimulus relevance for the generation of a response or its inhibition. Stimulus response mappings were modified by contextual cues, indicating which of two different stimuli following a go stimulus was relevant for stopping. Overall, response inhibition, that is comparing successful stopping to a stop-signal against go-signal related processes, was associated with increased activity in right inferior and left midfrontal regions, as well as increased EEG delta and theta power; however, stimulus-response conditions in which the most infrequent stop-signal was relevant for inhibition, were associated with decreased activity in regions typically involved in response inhibition, as well as decreased activity in the delta and theta bands as compared to conditions wherein the relevant stop-signal frequency was higher. Behaviorally, this (aforementioned condition, which demanded inhibition only from the most infrequent stimulus, was also associated with reduced reaction times and lower error rates. This pattern of results does not align with typical stimulus frequency-driven findings and suggests interplay between task relevance and stimulus frequency of the stop-signal. Moreover, with a multimodal EEG-fMRI analysis, we demonstrated significant parameterization for response inhibition with delta, theta and beta time-frequency values, which may be interpreted as reflecting conflict monitoring, evaluative and/or motor processes as suggested by previous work (Huster et al., 2013; Aron, 2011. Further multimodal results suggest a possible neurophysiological and behavioral benefit under conditions

  9. Information-Theoretical Analysis of EEG Microstate Sequences in Python

    Directory of Open Access Journals (Sweden)

    Frederic von Wegner

    2018-06-01

    Full Text Available We present an open-source Python package to compute information-theoretical quantities for electroencephalographic data. Electroencephalography (EEG measures the electrical potential generated by the cerebral cortex and the set of spatial patterns projected by the brain's electrical potential on the scalp surface can be clustered into a set of representative maps called EEG microstates. Microstate time series are obtained by competitively fitting the microstate maps back into the EEG data set, i.e., by substituting the EEG data at a given time with the label of the microstate that has the highest similarity with the actual EEG topography. As microstate sequences consist of non-metric random variables, e.g., the letters A–D, we recently introduced information-theoretical measures to quantify these time series. In wakeful resting state EEG recordings, we found new characteristics of microstate sequences such as periodicities related to EEG frequency bands. The algorithms used are here provided as an open-source package and their use is explained in a tutorial style. The package is self-contained and the programming style is procedural, focusing on code intelligibility and easy portability. Using a sample EEG file, we demonstrate how to perform EEG microstate segmentation using the modified K-means approach, and how to compute and visualize the recently introduced information-theoretical tests and quantities. The time-lagged mutual information function is derived as a discrete symbolic alternative to the autocorrelation function for metric time series and confidence intervals are computed from Markov chain surrogate data. The software package provides an open-source extension to the existing implementations of the microstate transform and is specifically designed to analyze resting state EEG recordings.

  10. Independent EEG sources are dipolar.

    Directory of Open Access Journals (Sweden)

    Arnaud Delorme

    Full Text Available Independent component analysis (ICA and blind source separation (BSS methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR effected by each decomposition, and decomposition 'dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA; best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison.

  11. Quantitative EEG and neurofeedback in children and adolescents: anxiety disorders, depressive disorders, comorbid addiction and attention-deficit/hyperactivity disorder, and brain injury.

    Science.gov (United States)

    Simkin, Deborah R; Thatcher, Robert W; Lubar, Joel

    2014-07-01

    This article explores the science surrounding neurofeedback. Both surface neurofeedback (using 2-4 electrodes) and newer interventions, such as real-time z-score neurofeedback (electroencephalogram [EEG] biofeedback) and low-resolution electromagnetic tomography neurofeedback, are reviewed. The limited literature on neurofeedback research in children and adolescents is discussed regarding treatment of anxiety, mood, addiction (with comorbid attention-deficit/hyperactivity disorder), and traumatic brain injury. Future potential applications, the use of quantitative EEG for determining which patients will be responsive to medications, the role of randomized controlled studies in neurofeedback research, and sensible clinical guidelines are considered. Copyright © 2014 Elsevier Inc. All rights reserved.

  12. Speaking and cognitive distractions during EEG-based brain control of a virtual neuroprosthesis-arm

    OpenAIRE

    Foldes, Stephen T; Taylor, Dawn M

    2013-01-01

    Background Brain-computer interface (BCI) systems have been developed to provide paralyzed individuals the ability to command the movements of an assistive device using only their brain activity. BCI systems are typically tested in a controlled laboratory environment were the user is focused solely on the brain-control task. However, for practical use in everyday life people must be able to use their brain-controlled device while mentally engaged with the cognitive responsibilities of daily a...

  13. Network Analysis of Resting State EEG in the Developing Young Brain: Structure Comes With Maturation

    NARCIS (Netherlands)

    Boersma, M.; Smit, D.J.A.; de Bie, H.M.A.; van Baal, G.C.M.; Boomsma, D.I.; de Geus, E.J.C.; Delemarre-van de Waal, H.A.; Stam, C.J.

    2011-01-01

    During childhood, brain structure and function changes substantially. Recently, graph theory has been introduced to model connectivity in the brain. Small-world networks, such as the brain, combine optimal properties of both ordered and random networks, i.e., high clustering and short path lengths.

  14. Detection of alcoholism based on EEG signals and functional brain network features extraction

    NARCIS (Netherlands)

    Ahmadi, N.; Pei, Y.; Pechenizkiy, M.

    2017-01-01

    Alcoholism is a common disorder that leads to brain defects and associated cognitive, emotional and behavioral impairments. Finding and extracting discriminative biological markers, which are correlated to healthy brain pattern and alcoholic brain pattern, helps us to utilize automatic methods for

  15. Cerebral perfusion abnormalities in therapy-resistant epilepsy in childhood: comparison between EEG, MRI and 99Tcm-ECD brain SPET.

    Science.gov (United States)

    Vattimo, A; Burroni, L; Bertelli, P; Volterrani, D; Vella, A

    1996-01-01

    We performed 99Tcm-ethyl cysteinate dimer (ECD) interictal single photon emission tomography (SPET) in 26 children with severe therapy-resistant epilepsy. All the children underwent a detailed clinical examination, an electroencephalogram (EEG) investigation and brain magnetic resonance imaging (MRI). In 21 of the 26 children, SPET demonstrated brain blood flow abnormalities, in 13 cases in the same territories that showed EEG alterations. MRI showed structural lesions in 6 of the 26 children, while SPET imaging confirmed these abnormalities in only 5 children. The lesion not detected on SPET was shown to be 3 mm thick on MRI. Five symptomatic patients had normal SPET. In one of these patients, the EEG findings were normal and MRI revealed a small calcific nodule (4 mm thick); in the others, the EEG showed non-focal but diffuse abnormalities. These data confirm that brain SPET is sensitive in detecting and localizing hypoperfused areas that could be associated with epileptic foci in this group of patients, even when the MRI image is normal.

  16. Detection of Drug Effects on Brain Activity using EEG-P300 with Similar Stimuli

    Science.gov (United States)

    Turnip, Arjon; Dwi Esti, K.; Faizal Amri, M.; Simbolon, Artha I.; Agung Suhendra, M.; IsKandar, Shelly; Wirakusumah, Firman F.

    2017-07-01

    Drug addiction poses a serious problem to our species. The worry that our significant family might be involved in drug use and are concerned to know how to detect drug use. Examinations of thirty taped EEG recordings were performed. The subjects consist of three group: addictive, methadone treatment (rehabilitation), and control (normal) which 10 subjects for each group. Statistical analysis was performed for the relative frequency of wave bands. The higher average amplitude is obtained from the addiction subjects. In the comparison with the signals source, channels P3 provide slightly higher average amplitude than other channels for all of subjects.

  17. Large Scale Functional Brain Networks Underlying Temporal Integration of Audio-Visual Speech Perception: An EEG Study.

    Science.gov (United States)

    Kumar, G Vinodh; Halder, Tamesh; Jaiswal, Amit K; Mukherjee, Abhishek; Roy, Dipanjan; Banerjee, Arpan

    2016-01-01

    Observable lip movements of the speaker influence perception of auditory speech. A classical example of this influence is reported by listeners who perceive an illusory (cross-modal) speech sound (McGurk-effect) when presented with incongruent audio-visual (AV) speech stimuli. Recent neuroimaging studies of AV speech perception accentuate the role of frontal, parietal, and the integrative brain sites in the vicinity of the superior temporal sulcus (STS) for multisensory speech perception. However, if and how does the network across the whole brain participates during multisensory perception processing remains an open question. We posit that a large-scale functional connectivity among the neural population situated in distributed brain sites may provide valuable insights involved in processing and fusing of AV speech. Varying the psychophysical parameters in tandem with electroencephalogram (EEG) recordings, we exploited the trial-by-trial perceptual variability of incongruent audio-visual (AV) speech stimuli to identify the characteristics of the large-scale cortical network that facilitates multisensory perception during synchronous and asynchronous AV speech. We evaluated the spectral landscape of EEG signals during multisensory speech perception at varying AV lags. Functional connectivity dynamics for all sensor pairs was computed using the time-frequency global coherence, the vector sum of pairwise coherence changes over time. During synchronous AV speech, we observed enhanced global gamma-band coherence and decreased alpha and beta-band coherence underlying cross-modal (illusory) perception compared to unisensory perception around a temporal window of 300-600 ms following onset of stimuli. During asynchronous speech stimuli, a global broadband coherence was observed during cross-modal perception at earlier times along with pre-stimulus decreases of lower frequency power, e.g., alpha rhythms for positive AV lags and theta rhythms for negative AV lags. Thus, our

  18. Architectonic Mapping of the Human Brain beyond Brodmann.

    Science.gov (United States)

    Amunts, Katrin; Zilles, Karl

    2015-12-16

    Brodmann has pioneered structural brain mapping. He considered functional and pathological criteria for defining cortical areas in addition to cytoarchitecture. Starting from this idea of structural-functional relationships at the level of cortical areas, we will argue that the cortical architecture is more heterogeneous than Brodmann's map suggests. A triple-scale concept is proposed that includes repetitive modular-like structures and micro- and meso-maps. Criteria for defining a cortical area will be discussed, considering novel preparations, imaging and optical methods, 2D and 3D quantitative architectonics, as well as high-performance computing including analyses of big data. These new approaches contribute to an understanding of the brain on multiple levels and challenge the traditional, mosaic-like segregation of the cerebral cortex. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Predictable Internal Brain Dynamics in EEG and Its Relation to Conscious States

    Directory of Open Access Journals (Sweden)

    Jaewook eYoo

    2014-06-01

    Full Text Available Consciousness is a complex and multi-faceted phenomenon defying scientific explanation. Part of the reason why this is the case is due to its subjective nature. In our previous computational experiments, to avoid such a subjective trap, we took a strategy to investigate objective necessary conditions of consciousness. Our basic hypothesis was that predictive internal dynamics serves as such a condition. This is in line with theories of consciousness that treat retention (memory, protention (anticipation, and primary impression as the tripartite temporal structure of consciousness. To test our hypothesis, we analyzed publicly available sleep and awake electroencephalogram (EEG data. Our results show that EEG signals from awake or rapid eye movement (REM sleep states have more predictable dynamics compared to those from slow-wave sleep (SWS. Since awakeness and REM sleep are associated with conscious states and SWS with unconscious or less consciousness states, these results support our hypothesis. The results suggest an intricate relationship among prediction, consciousness, and time, with potential applications to time perception and neurorobotics.

  20. Performance evaluation of a motor-imagery-based EEG-Brain computer interface using a combined cue with heterogeneous training data in BCI-Naive subjects

    Directory of Open Access Journals (Sweden)

    Lee Youngbum

    2011-10-01

    Full Text Available Abstract Background The subjects in EEG-Brain computer interface (BCI system experience difficulties when attempting to obtain the consistent performance of the actual movement by motor imagery alone. It is necessary to find the optimal conditions and stimuli combinations that affect the performance factors of the EEG-BCI system to guarantee equipment safety and trust through the performance evaluation of using motor imagery characteristics that can be utilized in the EEG-BCI testing environment. Methods The experiment was carried out with 10 experienced subjects and 32 naive subjects on an EEG-BCI system. There were 3 experiments: The experienced homogeneous experiment, the naive homogeneous experiment and the naive heterogeneous experiment. Each experiment was compared in terms of the six audio-visual cue combinations and consisted of 50 trials. The EEG data was classified using the least square linear classifier in case of the naive subjects through the common spatial pattern filter. The accuracy was calculated using the training and test data set. The p-value of the accuracy was obtained through the statistical significance test. Results In the case in which a naive subject was trained by a heterogeneous combined cue and tested by a visual cue, the result was not only the highest accuracy (p Conclusions We propose the use of this measuring methodology of a heterogeneous combined cue for training data and a visual cue for test data by the typical EEG-BCI algorithm on the EEG-BCI system to achieve effectiveness in terms of consistence, stability, cost, time, and resources management without the need for a trial and error process.

  1. Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

    Science.gov (United States)

    Murta, Teresa; Leite, Marco; Carmichael, David W; Figueiredo, Patrícia; Lemieux, Louis

    2015-01-01

    Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are important tools in cognitive and clinical neuroscience. Combined EEG–fMRI has been shown to help to characterise brain networks involved in epileptic activity, as well as in different sensory, motor and cognitive functions. A good understanding of the electrophysiological correlates of the blood oxygen level-dependent (BOLD) signal is necessary to interpret fMRI maps, particularly when obtained in combination with EEG. We review the current understanding of electrophysiological–haemodynamic correlates, during different types of brain activity. We start by describing the basic mechanisms underlying EEG and BOLD signals and proceed by reviewing EEG-informed fMRI studies using fMRI to map specific EEG phenomena over the entire brain (EEG–fMRI mapping), or exploring a range of EEG-derived quantities to determine which best explain colocalised BOLD fluctuations (local EEG–fMRI coupling). While reviewing studies of different forms of brain activity (epileptic and nonepileptic spontaneous activity; cognitive, sensory and motor functions), a significant attention is given to epilepsy because the investigation of its haemodynamic correlates is the most common application of EEG-informed fMRI. Our review is focused on EEG-informed fMRI, an asymmetric approach of data integration. We give special attention to the invasiveness of electrophysiological measurements and the simultaneity of multimodal acquisitions because these methodological aspects determine the nature of the conclusions that can be drawn from EEG-informed fMRI studies. We emphasise the advantages of, and need for, simultaneous intracranial EEG–fMRI studies in humans, which recently became available and hold great potential to improve our understanding of the electrophysiological correlates of BOLD fluctuations. PMID:25277370

  2. Dynamic Quantitative T1 Mapping in Orthotopic Brain Tumor Xenografts

    Directory of Open Access Journals (Sweden)

    Kelsey Herrmann

    2016-04-01

    Full Text Available Human brain tumors such as glioblastomas are typically detected using conventional, nonquantitative magnetic resonance imaging (MRI techniques, such as T2-weighted and contrast enhanced T1-weighted MRI. In this manuscript, we tested whether dynamic quantitative T1 mapping by MRI can localize orthotopic glioma tumors in an objective manner. Quantitative T1 mapping was performed by MRI over multiple time points using the conventional contrast agent Optimark. We compared signal differences to determine the gadolinium concentration in tissues over time. The T1 parametric maps made it easy to identify the regions of contrast enhancement and thus tumor location. Doubling the typical human dose of contrast agent resulted in a clearer demarcation of these tumors. Therefore, T1 mapping of brain tumors is gadolinium dose dependent and improves detection of tumors by MRI. The use of T1 maps provides a quantitative means to evaluate tumor detection by gadolinium-based contrast agents over time. This dynamic quantitative T1 mapping technique will also enable future quantitative evaluation of various targeted MRI contrast agents.

  3. Functional Maps of Mechanosensory Features in the Drosophila Brain.

    Science.gov (United States)

    Patella, Paola; Wilson, Rachel I

    2018-04-09

    Johnston's organ is the largest mechanosensory organ in Drosophila. It contributes to hearing, touch, vestibular sensing, proprioception, and wind sensing. In this study, we used in vivo 2-photon calcium imaging and unsupervised image segmentation to map the tuning properties of Johnston's organ neurons (JONs) at the site where their axons enter the brain. We then applied the same methodology to study two key brain regions that process signals from JONs: the antennal mechanosensory and motor center (AMMC) and the wedge, which is downstream of the AMMC. First, we identified a diversity of JON response types that tile frequency space and form a rough tonotopic map. Some JON response types are direction selective; others are specialized to encode amplitude modulations over a specific range (dynamic range fractionation). Next, we discovered that both the AMMC and the wedge contain a tonotopic map, with a significant increase in tonotopy-and a narrowing of frequency tuning-at the level of the wedge. Whereas the AMMC tonotopic map is unilateral, the wedge tonotopic map is bilateral. Finally, we identified a subregion of the AMMC/wedge that responds preferentially to the coherent rotation of the two mechanical organs in the same angular direction, indicative of oriented steady air flow (directional wind). Together, these maps reveal the broad organization of the primary and secondary mechanosensory regions of the brain. They provide a framework for future efforts to identify the specific cell types and mechanisms that underlie the hierarchical re-mapping of mechanosensory information in this system. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening.

    Science.gov (United States)

    Adamos, Dimitrios A; Laskaris, Nikolaos A; Micheloyannis, Sifis

    2018-06-01

    Music, being a multifaceted stimulus evolving at multiple timescales, modulates brain function in a manifold way that encompasses not only the distinct stages of auditory perception, but also higher cognitive processes like memory and appraisal. Network theory is apparently a promising approach to describe the functional reorganization of brain oscillatory dynamics during music listening. However, the music induced changes have so far been examined within the functional boundaries of isolated brain rhythms. Using naturalistic music, we detected the functional segregation patterns associated with different cortical rhythms, as these were reflected in the surface electroencephalography (EEG) measurements. The emerged structure was compared across frequency bands to quantify the interplay among rhythms. It was also contrasted against the structure from the rest and noise listening conditions to reveal the specific components stemming from music listening. Our methodology includes an efficient graph-partitioning algorithm, which is further utilized for mining prototypical modular patterns, and a novel algorithmic procedure for identifying 'switching nodes' (i.e. recording sites) that consistently change module during music listening. Our results suggest the multiplex character of the music-induced functional reorganization and particularly indicate the dependence between the networks reconstructed from the δ and β H rhythms. This dependence is further justified within the framework of nested neural oscillations and fits perfectly within the context of recently introduced cortical entrainment to music. Complying with the contemporary trends towards a multi-scale examination of the brain network organization, our approach specifies the form of neural coordination among rhythms during music listening. Considering its computational efficiency, and in conjunction with the flexibility of in situ electroencephalography, it may lead to novel assistive tools for real

  5. Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening

    Science.gov (United States)

    Adamos, Dimitrios A.; Laskaris, Nikolaos A.; Micheloyannis, Sifis

    2018-06-01

    Objective. Music, being a multifaceted stimulus evolving at multiple timescales, modulates brain function in a manifold way that encompasses not only the distinct stages of auditory perception, but also higher cognitive processes like memory and appraisal. Network theory is apparently a promising approach to describe the functional reorganization of brain oscillatory dynamics during music listening. However, the music induced changes have so far been examined within the functional boundaries of isolated brain rhythms. Approach. Using naturalistic music, we detected the functional segregation patterns associated with different cortical rhythms, as these were reflected in the surface electroencephalography (EEG) measurements. The emerged structure was compared across frequency bands to quantify the interplay among rhythms. It was also contrasted against the structure from the rest and noise listening conditions to reveal the specific components stemming from music listening. Our methodology includes an efficient graph-partitioning algorithm, which is further utilized for mining prototypical modular patterns, and a novel algorithmic procedure for identifying ‘switching nodes’ (i.e. recording sites) that consistently change module during music listening. Main results. Our results suggest the multiplex character of the music-induced functional reorganization and particularly indicate the dependence between the networks reconstructed from the δ and β H rhythms. This dependence is further justified within the framework of nested neural oscillations and fits perfectly within the context of recently introduced cortical entrainment to music. Significance. Complying with the contemporary trends towards a multi-scale examination of the brain network organization, our approach specifies the form of neural coordination among rhythms during music listening. Considering its computational efficiency, and in conjunction with the flexibility of in situ electroencephalography

  6. Three-dimensional brain mapping using fMRI

    International Nuclear Information System (INIS)

    Fukunaga, Masaki; Tanaka, Chuzo; Umeda, Masahiro; Ebisu, Toshihiko; Aoki, Ichio; Higuchi, Toshihiro; Naruse, Shoji.

    1997-01-01

    Functional mapping of the activated brain, the location and extent of the activated area were determined, during motor tasks and sensory stimulation using fMRI superimposed on 3D anatomical MRI. Twelve volunteers were studied. The fMR images were acquired using a 2D gradient echo echo planar imaging sequence. The 3D anatomical MR images of the whole brain were acquired using a conventional 3D gradient echo sequence. Motor tasks were sequential opposition of fingers, clenching a hand and elbow flexion. Somatosensory stimulation were administered by scrubbing the palm and sole with a washing sponge. Visual stimulation consisted of full visual field stimulation. Data were analyzed by the cross-correlation method. Transversal fMR images and anatomical images were reconstructed using both volume-, surface-rendering methods, and reconstructed for coronal and sagittal sections. Activated areas were expressed using the three primary colors. Motor tasks activated the contralateral primary motor area (M1), the primary somatosensory area (S1) and the supplementary motor area (SMA). Somatosensory tasks activated the contralateral S1, M1 and secondary sensory area (S2). Activated areas during full visual field stimulation was observed in the bilateral occipital lobe, including both the primary cortex. Three-dimensional brain mapping allowed visualization of the anatomical location and extent of the activated brain during both motor task and sensory stimulation. Using this method we could obtain a functional map similar to the Penfield's schema. (author)

  7. Directed cortical information flow during human object recognition: analyzing induced EEG gamma-band responses in brain's source space.

    Directory of Open Access Journals (Sweden)

    Gernot G Supp

    Full Text Available The increase of induced gamma-band responses (iGBRs; oscillations >30 Hz elicited by familiar (meaningful objects is well established in electroencephalogram (EEG research. This frequency-specific change at distinct locations is thought to indicate the dynamic formation of local neuronal assemblies during the activation of cortical object representations. As analytically power increase is just a property of a single location, phase-synchrony was introduced to investigate the formation of large-scale networks between spatially distant brain sites. However, classical phase-synchrony reveals symmetric, pair-wise correlations and is not suited to uncover the directionality of interactions. Here, we investigated the neural mechanism of visual object processing by means of directional coupling analysis going beyond recording sites, but rather assessing the directionality of oscillatory interactions between brain areas directly. This study is the first to identify the directionality of oscillatory brain interactions in source space during human object recognition and suggests that familiar, but not unfamiliar, objects engage widespread reciprocal information flow. Directionality of cortical information-flow was calculated based upon an established Granger-Causality coupling-measure (partial-directed coherence; PDC using autoregressive modeling. To enable comparison with previous coupling studies lacking directional information, phase-locking analysis was applied, using wavelet-based signal decompositions. Both, autoregressive modeling and wavelet analysis, revealed an augmentation of iGBRs during the presentation of familiar objects relative to unfamiliar controls, which was localized to inferior-temporal, superior-parietal and frontal brain areas by means of distributed source reconstruction. The multivariate analysis of PDC evaluated each possible direction of brain interaction and revealed widespread reciprocal information-transfer during familiar

  8. Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol.

    Science.gov (United States)

    Areces, Debora; Cueli, Marisol; García, Trinidad; González-Castro, Paloma; Rodríguez, Celestino

    2018-04-01

    Attention Deficit Hyperactivity Disorder (ADHD) is a problem that impacts academic performance and has serious consequences that result in difficulties in scholastic, social and familial contexts. One of the most common problems in the identification of this disorder relates to the apparent over diagnosis of the disorder due to the absence of global protocols for assessment. The research group of School Learning, Difficulties and Academic Performance (ADIR) from the University of Oviedo, has developed a complete protocol that suggests the existence of certain patterns of cortical activation and executive control for identifying ADHD more objectively. This protocol takes into consideration some of the hypothetical determinants of ADHD, including the relationship between activation of selected areas of the brain, and differences in performance on various aspects of executive functioning such as omissions, commissions or response times, using innovative tools of Continuous Performance Testing (based on Virtual Reality CPT and Traditional CPT) and brain activation measures (two different tools, based on Hemoencephalography- nirHEG; and Quantified Electroencephalography --Q-EEG, respectively). This model of assessment aims to provide an effective assessment of ADHD symptomatology in order to design an accurate intervention and make appropriate recommendations for parents and teachers.

  9. Competition in the Brain. The Contribution of EEG and fNIRS Modulation and Personality Effects in Social Ranking.

    Science.gov (United States)

    Balconi, Michela; Vanutelli, Maria E

    2016-01-01

    In the present study, the social ranking perception in competition was explored. Brain response (alpha band oscillations, EEG; hemodynamic activity, O2Hb), as well as self-perception of social ranking, cognitive performance, and personality trait (Behavioral Activation System, BAS) were considered during a competitive joint-action. Subjects were required to develop a strategy to obtain a better outcome than a competitor (C) (in term of error rate, and response time, RT). A pre-feedback (without a specific feedback on the performance) and a post-feedback condition (which reinforced the improved performance) were provided. It was found that higher-BAS participants responded in greater measure to perceived higher cognitive performance (post-feedback condition), with increased left prefrontal activity, higher ranking perception, and a better real performance (reduced RTs). These results were explained in term of increased sense of self-efficacy and social position, probably based on higher-BAS sensitivity to reinforcing conditions. In addition, the hemispheric effect in favor of the left side characterized the competitive behavior, showing an imbalance for high-BAS in comparison to low-BAS in the case of a rewarding (post-feedback) context. Therefore, the present results confirmed the significance of BAS in modulating brain responsiveness, self-perceived social position, and real performance during an interpersonal competitive action which is considered highly relevant for social status.

  10. How does a surgeon’s brain buzz? An EEG coherence study on the interaction between humans and robot

    Science.gov (United States)

    2013-01-01

    Introduction In humans, both primary and non-primary motor areas are involved in the control of voluntary movements. However, the dynamics of functional coupling among different motor areas have not been fully clarified yet. There is to date no research looking to the functional dynamics in the brain of surgeons working in laparoscopy compared with those trained and working in robotic surgery. Experimental procedures We enrolled 16 right-handed trained surgeons and assessed changes in intra- and inter-hemispheric EEG coherence with a 32-channels device during the same motor task with either a robotic or a laparoscopic approach. Estimates of auto and coherence spectra were calculated by a fast Fourier transform algorithm implemented on Matlab 5.3. Results We found increase of coherence in surgeons performing laparoscopy, especially in theta and lower alpha activity, in all experimental conditions (M1 vs. SMA, S1 vs. SMA, S1 vs. pre-SMA and M1 vs. S1; p with different approaches. To the best of our knowledge, this is the first study that tried to assess semi-quantitative differences during the interaction between normal human brain and robotic devices. PMID:23607324

  11. How does a surgeon's brain buzz? An EEG coherence study on the interaction between humans and robot.

    Science.gov (United States)

    Bocci, Tommaso; Moretto, Carlo; Tognazzi, Silvia; Briscese, Lucia; Naraci, Megi; Leocani, Letizia; Mosca, Franco; Ferrari, Mauro; Sartucci, Ferdinando

    2013-04-22

    In humans, both primary and non-primary motor areas are involved in the control of voluntary movements. However, the dynamics of functional coupling among different motor areas have not been fully clarified yet. There is to date no research looking to the functional dynamics in the brain of surgeons working in laparoscopy compared with those trained and working in robotic surgery. We enrolled 16 right-handed trained surgeons and assessed changes in intra- and inter-hemispheric EEG coherence with a 32-channels device during the same motor task with either a robotic or a laparoscopic approach. Estimates of auto and coherence spectra were calculated by a fast Fourier transform algorithm implemented on Matlab 5.3. We found increase of coherence in surgeons performing laparoscopy, especially in theta and lower alpha activity, in all experimental conditions (M1 vs. SMA, S1 vs. SMA, S1 vs. pre-SMA and M1 vs. S1; p right vs. left M1, right vs. left S1, right pre-SMA vs. left M1, left pre-SMA vs. right M1; p brain and robotic devices.

  12. Effects of tissue susceptibility on brain temperature mapping.

    Science.gov (United States)

    Maudsley, Andrew A; Goryawala, Mohammed Z; Sheriff, Sulaiman

    2017-02-01

    A method for mapping of temperature over a large volume of the brain using volumetric proton MR spectroscopic imaging has been implemented and applied to 150 normal subjects. Magnetic susceptibility-induced frequency shifts in gray- and white-matter regions were measured and included as a correction in the temperature mapping calculation. Additional sources of magnetic susceptibility variations of the individual metabolite resonance frequencies were also observed that reflect the cellular-level organization of the brain metabolites, with the most notable differences being attributed to changes of the N-Acetylaspartate resonance frequency that reflect the intra-axonal distribution and orientation of the white-matter tracts with respect to the applied magnetic field. These metabolite-specific susceptibility effects are also shown to change with age. Results indicate no change of apparent brain temperature with age from 18 to 84 years old, with a trend for increased brain temperature throughout the cerebrum in females relative for males on the order of 0.1°C; slightly increased temperatures in the left hemisphere relative to the right; and a lower temperature of 0.3°C in the cerebellum relative to that of cerebral white-matter. This study presents a novel acquisition method for noninvasive measurement of brain temperature that is of potential value for diagnostic purposes and treatment monitoring, while also demonstrating limitations of the measurement due to the confounding effects of tissue susceptibility variations. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. Mapping the calcitonin receptor in human brain stem

    DEFF Research Database (Denmark)

    Bower, Rebekah L; Eftekhari, Sajedeh; Waldvogel, Henry J

    2016-01-01

    understanding of these hormone systems by mapping CTR expression in the human brain stem, specifically the medulla oblongata. Widespread CTR-like immunoreactivity was observed throughout the medulla. Dense CTR staining was noted in several discrete nuclei, including the nucleus of the solitary tract...... receptors (AMY) are a heterodimer formed by the coexpression of CTR with receptor activity-modifying proteins (RAMPs). CTR with RAMP1 responds potently to both amylin and CGRP. The brain stem is a major site of action for circulating amylin and is a rich site of CGRP binding. This study aimed to enhance our...

  14. On brain activity mapping: insights and lessons from Brain Decoding Project to map memory patterns in the hippocampus.

    Science.gov (United States)

    Tsien, Joe Z; Li, Meng; Osan, Remus; Chen, Guifen; Lin, Longnian; Wang, Phillip Lei; Frey, Sabine; Frey, Julietta; Zhu, Dajiang; Liu, Tianming; Zhao, Fang; Kuang, Hui

    2013-09-01

    The BRAIN project recently announced by the president Obama is the reflection of unrelenting human quest for cracking the brain code, the patterns of neuronal activity that define who we are and what we are. While the Brain Activity Mapping proposal has rightly emphasized on the need to develop new technologies for measuring every spike from every neuron, it might be helpful to consider both the theoretical and experimental aspects that would accelerate our search for the organizing principles of the brain code. Here we share several insights and lessons from the similar proposal, namely, Brain Decoding Project that we initiated since 2007. We provide a specific example in our initial mapping of real-time memory traces from one part of the memory circuit, namely, the CA1 region of the mouse hippocampus. We show how innovative behavioral tasks and appropriate mathematical analyses of large datasets can play equally, if not more, important roles in uncovering the specific-to-general feature-coding cell assembly mechanism by which episodic memory, semantic knowledge, and imagination are generated and organized. Our own experiences suggest that the bottleneck of the Brain Project is not only at merely developing additional new technologies, but also the lack of efficient avenues to disseminate cutting edge platforms and decoding expertise to neuroscience community. Therefore, we propose that in order to harness unique insights and extensive knowledge from various investigators working in diverse neuroscience subfields, ranging from perception and emotion to memory and social behaviors, the BRAIN project should create a set of International and National Brain Decoding Centers at which cutting-edge recording technologies and expertise on analyzing large datasets analyses can be made readily available to the entire community of neuroscientists who can apply and schedule to perform cutting-edge research.

  15. The role of image registration in brain mapping

    Science.gov (United States)

    Toga, A.W.; Thompson, P.M.

    2008-01-01

    Image registration is a key step in a great variety of biomedical imaging applications. It provides the ability to geometrically align one dataset with another, and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or across time. Registration algorithms also enable the pooling and comparison of experimental findings across laboratories, the construction of population-based brain atlases, and the creation of systems to detect group patterns in structural and functional imaging data. We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain. PMID:19890483

  16. EEG Controlled Wheelchair

    Directory of Open Access Journals (Sweden)

    Swee Sim Kok

    2016-01-01

    Full Text Available This paper describes the development of a brainwave controlled wheelchair. The main objective of this project is to construct a wheelchair which can be directly controlled by the brain without requires any physical feedback as controlling input from the user. The method employed in this project is the Brain-computer Interface (BCI, which enables direct communication between the brain and the electrical wheelchair. The best method for recording the brain’s activity is electroencephalogram (EEG. EEG signal is also known as brainwaves signal. The device that used for capturing the EEG signal is the Emotiv EPOC headset. This headset is able to transmit the EEG signal wirelessly via Bluetooth to the PC (personal computer. By using the PC software, the EEG signals are processed and converted into mental command. According to the mental command (e.g. forward, left... obtained, the output electrical signal is sent out to the electrical wheelchair to perform the desired movement. Thus, in this project, a computer software is developed for translating the EEG signal into mental commands and transmitting out the controlling signal wirelessly to the electrical wheelchair.

  17. A map of octopaminergic neurons in the Drosophila brain.

    Science.gov (United States)

    Busch, Sebastian; Selcho, Mareike; Ito, Kei; Tanimoto, Hiromu

    2009-04-20

    The biogenic amine octopamine modulates diverse behaviors in invertebrates. At the single neuron level, the mode of action is well understood in the peripheral nervous system owing to its simple structure and accessibility. For elucidating the role of individual octopaminergic neurons in the modulation of complex behaviors, a detailed analysis of the connectivity in the central nervous system is required. Here we present a comprehensive anatomical map of candidate octopaminergic neurons in the adult Drosophila brain: including the supra- and subesophageal ganglia. Application of the Flp-out technique enabled visualization of 27 types of individual octopaminergic neurons. Based on their morphology and distribution of genetic markers, we found that most octopaminergic neurons project to multiple brain structures with a clear separation of dendritic and presynaptic regions. Whereas their major dendrites are confined to specific brain regions, each cell type targets different, yet defined, neuropils distributed throughout the central nervous system. This would allow them to constitute combinatorial modules assigned to the modulation of distinct neuronal processes. The map may provide an anatomical framework for the functional constitution of the octopaminergic system. It also serves as a model for the single-cell organization of a particular neurotransmitter in the brain. 2009 Wiley-Liss, Inc.

  18. A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals.

    Science.gov (United States)

    Bamatraf, Saeed; Hussain, Muhammad; Aboalsamh, Hatim; Qazi, Emad-Ul-Haq; Malik, Amir Saeed; Amin, Hafeez Ullah; Mathkour, Hassan; Muhammad, Ghulam; Imran, Hafiz Muhammad

    2016-01-01

    We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states. For data collection, half of sixty-eight healthy individuals watched the learning material in 2D format whereas the rest watched the same material in 3D format. After learning task, memory recall tasks were performed after 30 minutes (STM) and two months (LTM), and EEG signals were recorded. In case of STM, 97.5% prediction accuracy was achieved for 3D and 96.6% for 2D and, in case of LTM, it was 100% for both 2D and 3D. The statistical analysis of the results suggested that for learning and memory recall both 2D and 3D materials do not have much difference in case of STM and LTM.

  19. An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand.

    Science.gov (United States)

    Soekadar, Surjo R; Witkowski, Matthias; Vitiello, Nicola; Birbaumer, Niels

    2015-06-01

    The loss of hand function can result in severe physical and psychosocial impairment. Thus, compensation of a lost hand function using assistive robotics that can be operated in daily life is very desirable. However, versatile, intuitive, and reliable control of assistive robotics is still an unsolved challenge. Here, we introduce a novel brain/neural-computer interaction (BNCI) system that integrates electroencephalography (EEG) and electrooculography (EOG) to improve control of assistive robotics in daily life environments. To evaluate the applicability and performance of this hybrid approach, five healthy volunteers (HV) (four men, average age 26.5 ± 3.8 years) and a 34-year-old patient with complete finger paralysis due to a brachial plexus injury (BPI) used EEG (condition 1) and EEG/EOG (condition 2) to control grasping motions of a hand exoskeleton. All participants were able to control the BNCI system (BNCI control performance HV: 70.24 ± 16.71%, BPI: 65.93 ± 24.27%), but inclusion of EOG significantly improved performance across all participants (HV: 80.65 ± 11.28, BPI: 76.03 ± 18.32%). This suggests that hybrid BNCI systems can achieve substantially better control over assistive devices, e.g., a hand exoskeleton, than systems using brain signals alone and thus may increase applicability of brain-controlled assistive devices in daily life environments.

  20. Connectome analysis for pre-operative brain mapping in neurosurgery

    Science.gov (United States)

    Hart, Michael G.; Price, Stephen J.; Suckling, John

    2016-01-01

    Abstract Object: Brain mapping has entered a new era focusing on complex network connectivity. Central to this is the search for the connectome or the brains ‘wiring diagram’. Graph theory analysis of the connectome allows understanding of the importance of regions to network function, and the consequences of their impairment or excision. Our goal was to apply connectome analysis in patients with brain tumours to characterise overall network topology and individual patterns of connectivity alterations. Methods: Resting-state functional MRI data were acquired using multi-echo, echo planar imaging pre-operatively from five participants each with a right temporal–parietal–occipital glioblastoma. Complex networks analysis was initiated by parcellating the brain into anatomically regions amongst which connections were identified by retaining the most significant correlations between the respective wavelet decomposed time-series. Results: Key characteristics of complex networks described in healthy controls were preserved in these patients, including ubiquitous small world organization. An exponentially truncated power law fit to the degree distribution predicted findings of general network robustness to injury but with a core of hubs exhibiting disproportionate vulnerability. Tumours produced a consistent reduction in local and long-range connectivity with distinct patterns of connection loss depending on lesion location. Conclusions: Connectome analysis is a feasible and novel approach to brain mapping in individual patients with brain tumours. Applications to pre-surgical planning include identifying regions critical to network function that should be preserved and visualising connections at risk from tumour resection. In the future one could use such data to model functional plasticity and recovery of cognitive deficits. PMID:27447756

  1. Connectome analysis for pre-operative brain mapping in neurosurgery.

    Science.gov (United States)

    Hart, Michael G; Price, Stephen J; Suckling, John

    2016-10-01

    Brain mapping has entered a new era focusing on complex network connectivity. Central to this is the search for the connectome or the brains 'wiring diagram'. Graph theory analysis of the connectome allows understanding of the importance of regions to network function, and the consequences of their impairment or excision. Our goal was to apply connectome analysis in patients with brain tumours to characterise overall network topology and individual patterns of connectivity alterations. Resting-state functional MRI data were acquired using multi-echo, echo planar imaging pre-operatively from five participants each with a right temporal-parietal-occipital glioblastoma. Complex networks analysis was initiated by parcellating the brain into anatomically regions amongst which connections were identified by retaining the most significant correlations between the respective wavelet decomposed time-series. Key characteristics of complex networks described in healthy controls were preserved in these patients, including ubiquitous small world organization. An exponentially truncated power law fit to the degree distribution predicted findings of general network robustness to injury but with a core of hubs exhibiting disproportionate vulnerability. Tumours produced a consistent reduction in local and long-range connectivity with distinct patterns of connection loss depending on lesion location. Connectome analysis is a feasible and novel approach to brain mapping in individual patients with brain tumours. Applications to pre-surgical planning include identifying regions critical to network function that should be preserved and visualising connections at risk from tumour resection. In the future one could use such data to model functional plasticity and recovery of cognitive deficits.

  2. Evaluation of MRI sequences for quantitative T1 brain mapping

    Science.gov (United States)

    Tsialios, P.; Thrippleton, M.; Glatz, A.; Pernet, C.

    2017-11-01

    T1 mapping constitutes a quantitative MRI technique finding significant application in brain imaging. It allows evaluation of contrast uptake, blood perfusion, volume, providing a more specific biomarker of disease progression compared to conventional T1-weighted images. While there are many techniques for T1-mapping there is a wide range of reported T1-values in tissues, raising the issue of protocols reproducibility and standardization. The gold standard for obtaining T1-maps is based on acquiring IR-SE sequence. Widely used alternative sequences are IR-SE-EPI, VFA (DESPOT), DESPOT-HIFI and MP2RAGE that speed up scanning and fitting procedures. A custom MRI phantom was used to assess the reproducibility and accuracy of the different methods. All scans were performed using a 3T Siemens Prisma scanner. The acquired data processed using two different codes. The main difference was observed for VFA (DESPOT) which grossly overestimated T1 relaxation time by 214 ms [126 270] compared to the IR-SE sequence. MP2RAGE and DESPOT-HIFI sequences gave slightly shorter time than IR-SE (~20 to 30ms) and can be considered as alternative and time-efficient methods for acquiring accurate T1 maps of the human brain, while IR-SE-EPI gave identical result, at a cost of a lower image quality.

  3. Evolution of the Brain Computing Interface (BCI and Proposed Electroencephalography (EEG Signals Based Authentication Model

    Directory of Open Access Journals (Sweden)

    Ramzan Qaseem

    2018-01-01

    Full Text Available With current advancements in the field of Brain Computer interface it is required to study how it will affect the other technologies currently in use. In this paper, the authors motivate the need of Brain Computing Interface in the era of IoT (Internet of Things, and analyze how BCI in the presence of IoT could have serious privacy breach if not protected by new kind of more secure protocols. Security breach and hacking has been around for a long time but now we are sensitive towards data as our lives depend on it. When everything is interconnected through IoT and considering that we control all interconnected things by means of our brain using BCI (Brain Computer Interface, the meaning of security breach becomes much more sensitive than in the past. This paper describes the old security methods being used for authentication and how they can be compromised. Considering the sensitivity of data in the era of IoT, a new form of authentication is required, which should incorporate BCI rather than usual authentication techniques.

  4. An EEG-Based Biometric System Using Eigenvector Centrality in Resting State Brain Networks

    NARCIS (Netherlands)

    Fraschini, M.; Hillebrand, A.; Demuru, M.; Didaci, L.; Marcialis, G.L.

    2015-01-01

    Recently, there has been a growing interest in the use of brain activity for biometric systems. However, so far these studies have focused mainly on basic features of the Electroencephalography. In this study we propose an approach based on phase synchronization, to investigate personal distinctive

  5. Transdermal Physostigmine—Absence of Effect on Topographic Brain Mapping

    Directory of Open Access Journals (Sweden)

    M. Y. Neufeld

    1993-01-01

    Full Text Available Nine patients with primary degenerative dementia (PDD participated in an open trial of transdermal physostigmine (TPh. In order to evaluate the neurophysiologic effects of TPh, EEG data were recorded and compared at baseline and following 2 months of continuous treatment. There was no significant effect of TPh on EEG spectra in patients with PDD.

  6. Clinical and Biochemical Outcomes Following EEG Neurofeedback Training in Traumatic Brain Injury in the Context of Spontaneous Recovery.

    Science.gov (United States)

    Bennett, Cathlyn N; Gupta, Rajnish K; Prabhakar, Puttachandra; Christopher, Rita; Sampath, Somanna; Thennarasu, K; Rajeswaran, Jamuna

    2017-12-01

    It has been found that reduction of posttraumatic stress symptoms is positively associated with the reduction of postconcussive symptoms. Cortisol is commonly used as a biomarker of stress. Understanding the role of posttraumatic stress and cortisol in symptom reduction has implication for neuropsychological rehabilitation particularly in the context of spontaneous recovery. The aim of the research was to study the effectiveness of EEG neurofeedback training on clinical symptoms, perceived stress, and cortisol in traumatic brain injury (TBI) patients in the context of spontaneous recovery. The design was an experimental longitudinal design with the pre-post comparison. The sample comprised 60 patients with the diagnosis of TBI-30 patients in the neurofeedback training (NFT) group and 30 patients in the treatment as usual group (TAU) group. Half of the patients were recruited within 6 months of injury to study the role of spontaneous recovery and the other half were recruited in the 12 to 18 months postinjury phase. Alpha-theta training was given to the NFT group over 20 sessions. Pre and post comparisons were made on clinical symptom rating, perceived stress, and serum cortisol levels. The results indicate significant differences in symptom reporting and perceived stress between the NFT and TAU groups. Significant differences were also seen in cortisol levels with implications for the acute recovery phase. Alpha-theta NFT has a beneficial effect on symptom reduction as well as perceived stress. It also has a beneficial effect on levels of serum cortisol, corroborating these findings.

  7. Brain states recognition during visual perception by means of artificial neural network in the different EEG frequency ranges

    Science.gov (United States)

    Musatov, V. Yu.; Runnova, A. E.; Andreev, A. V.; Zhuravlev, M. O.

    2018-04-01

    In the present paper, the possibility of classification by artificial neural networks of a certain architecture of ambiguous images is investigated using the example of the Necker cube from the experimentally obtained EEG recording data of several operators. The possibilities of artificial neural network classification of ambiguous images are investigated in the different frequency ranges of EEG recording signals.

  8. A Mapping Between Structural and Functional Brain Networks.

    Science.gov (United States)

    Meier, Jil; Tewarie, Prejaas; Hillebrand, Arjan; Douw, Linda; van Dijk, Bob W; Stufflebeam, Steven M; Van Mieghem, Piet

    2016-05-01

    The relationship between structural and functional brain networks is still highly debated. Most previous studies have used a single functional imaging modality to analyze this relationship. In this work, we use multimodal data, from functional MRI, magnetoencephalography, and diffusion tensor imaging, and assume that there exists a mapping between the connectivity matrices of the resting-state functional and structural networks. We investigate this mapping employing group averaged as well as individual data. We indeed find a significantly high goodness of fit level for this structure-function mapping. Our analysis suggests that a functional connection is shaped by all walks up to the diameter in the structural network in both modality cases. When analyzing the inverse mapping, from function to structure, longer walks in the functional network also seem to possess minor influence on the structural connection strengths. Even though similar overall properties for the structure-function mapping are found for different functional modalities, our results indicate that the structure-function relationship is modality dependent.

  9. Effects of CPAP-therapy on brain electrical activity in obstructive sleep apneic patients: a combined EEG study using LORETA and Omega complexity : reversible alterations of brain activity in OSAS.

    Science.gov (United States)

    Toth, Marton; Faludi, Bela; Kondakor, Istvan

    2012-10-01

    Effects of initiation of continuous positive airway pressure (CPAP) therapy on EEG background activity were investigated in patients with obstructive sleep apnea syndrome (OSAS, N = 25) to test possible reversibility of alterations of brain electrical activity caused by chronic hypoxia. Normal control group (N = 14) was also examined. Two EEG examinations were done in each groups: at night and in the next morning. Global and regional (left vs. right, anterior vs. posterior) measures of spatial complexity (Omega complexity) were used to characterize the degree of spatial synchrony of EEG. Low resolution electromagnetic tomography (LORETA) was used to localize generators of EEG activity in separate frequency bands. Before CPAP-treatment, a significantly lower Omega complexity was found globally and over the right hemisphere. Due to CPAP-treatment, these significant differences vanished. Significantly decreased Omega complexity was found in the anterior region after treatment. LORETA showed a decreased activity in all of the beta bands after therapy in the right hippocampus, premotor and temporo-parietal cortex, and bilaterally in the precuneus, paracentral and posterior cingulate cortex. No significant changes were seen in control group. Comparing controls and patients before sleep, an increased alpha2 band activity was seen bilaterally in the precuneus, paracentral and posterior cingulate cortex, while in the morning an increased beta3 band activity in the left precentral and bilateral premotor cortex and a decreased delta band activity in the right temporo-parietal cortex and insula were observed. These findings indicate that effect of sleep on EEG background activity is different in OSAS patients and normal controls. In OSAS patients, significant changes lead to a more normal EEG after a night under CPAP-treatment. Compensatory alterations of brain electrical activity in regions associated with influencing sympathetic outflow, visuospatial abilities, long

  10. Wada-test, functional magnetic resonance imaging and direct electrical stimulation - brain mapping methods

    International Nuclear Information System (INIS)

    Minkin, K.; Tanova, R.; Busarski, A.; Penkov, M.; Penev, L.; Hadjidekov, V.

    2009-01-01

    Modern neurosurgery requires accurate preoperative and intraoperative localization of brain pathologies but also of brain functions. The presence of individual variations in healthy subjects and the shift of brain functions in brain diseases provoke the introduction of various methods for brain mapping. The aim of this paper was to analyze the most widespread methods for brain mapping: Wada-test, functional magnetic resonance imaging (fMRI) and intraoperative direct electrical stimulation (DES). This study included 4 patients with preoperative brain mapping using Wada-test and fMRI. Intraoperative mapping with DES during awake craniotomy was performed in one case. The histopathological diagnosis was low-grade glioma in 2 cases, cortical dysplasia (1 patient) and arteriovenous malformation (1 patient). The brain mapping permits total lesion resection in three of four patients. There was no new postoperative deficit despite surgery near or within functional brain areas. Brain plasticity provoking shift of eloquent areas from their usual locations was observed in two cases. The brain mapping methods allow surgery in eloquent brain areas recognized in the past as 'forbidden areas'. Each method has advantages and disadvantages. The precise location of brain functions and pathologies frequently requires combination of different brain mapping methods. (authors)

  11. How motor, cognitive and musical expertise shapes the brain: Focus on fMRI and EEG resting-state functional connectivity

    DEFF Research Database (Denmark)

    Cantou, Pauline; Platel, Hervé; Desgranges, Béatrice

    2017-01-01

    about functional cerebral reorganization due to expertise at the whole-brain level and might facilitate comparison across studies. Resting-state functional MRI and EEG makes it possible to explore the functional traces of expertise in the brain by measuring temporal correlations of blood oxygen level......, to determine whether there is a domain-specific neural signature of expertise. After highlighting expertise-related changes within resting-state networks for each domain, we discuss their specificity to the trained activity and the methodological considerations concerning different conditions and analyses used......-dependent (BOLD) and spontaneous neural activity fluctuations at rest. Since these correlations are thought to reflect a prior history co-activation of brain regions, we propose reviewing studies that focused on the effects of expertise in the motor, cognitive and musical domains on brain plasticity at rest...

  12. Causality within the Epileptic Network: An EEG-fMRI Study Validated by Intracranial EEG.

    Science.gov (United States)

    Vaudano, Anna Elisabetta; Avanzini, Pietro; Tassi, Laura; Ruggieri, Andrea; Cantalupo, Gaetano; Benuzzi, Francesca; Nichelli, Paolo; Lemieux, Louis; Meletti, Stefano

    2013-01-01

    Accurate localization of the Seizure Onset Zone (SOZ) is crucial in patients with drug-resistance focal epilepsy. EEG with fMRI recording (EEG-fMRI) has been proposed as a complementary non-invasive tool, which can give useful additional information in the pre-surgical work-up. However, fMRI maps related to interictal epileptiform activities (IED) often show multiple regions of signal change, or "networks," rather than highly focal ones. Effective connectivity approaches like Dynamic Causal Modeling (DCM) applied to fMRI data potentially offers a framework to address which brain regions drives the generation of seizures and IED within an epileptic network. Here, we present a first attempt to validate DCM on EEG-fMRI data in one patient affected by frontal lobe epilepsy. Pre-surgical EEG-fMRI demonstrated two distinct clusters of blood oxygenation level dependent (BOLD) signal increases linked to IED, one located in the left frontal pole and the other in the ipsilateral dorso-lateral frontal cortex. DCM of the IED-related BOLD signal favored a model corresponding to the left dorso-lateral frontal cortex as driver of changes in the fronto-polar region. The validity of DCM was supported by: (a) the results of two different non-invasive analysis obtained on the same dataset: EEG source imaging (ESI), and "psycho-physiological interaction" analysis; (b) the failure of a first surgical intervention limited to the fronto-polar region; (c) the results of the intracranial EEG monitoring performed after the first surgical intervention confirming a SOZ located over the dorso-lateral frontal cortex. These results add evidence that EEG-fMRI together with advanced methods of BOLD signal analysis is a promising tool that can give relevant information within the epilepsy surgery diagnostic work-up.

  13. Causality within the epileptic network: an EEG-fMRI study validated by intracranial EEG.

    Directory of Open Access Journals (Sweden)

    Anna Elisabetta eVaudano

    2013-11-01

    Full Text Available Accurate localization of the Seizure Onset Zone (SOZ is crucial in patients with drug-resistance focal epilepsy. EEG with fMRI recording (EEG-fMRI has been proposed as a complementary non-invasive tool, which can give useful additional information in the pre-surgical workup. However, fMRI maps related to interictal epileptiform activities (IED often show multiple regions of signal change, or networks, rather than highly focal ones. Effective connectivity approaches like Dynamic Causal Modelling (DCM applied to fMRI data potentially offers a framework to address which brain regions drives the generation of seizures and IED within an epileptic network. Here we present a first attempt to validate DCM on EEG-fMRI data in one patient affected by frontal lobe epilepsy. Pre-surgical EEG-fMRI demonstrated two distinct clusters of BOLD signal increases linked to IED, one located in the left frontal pole and the other in the ipsilateral dorso-lateral frontal cortex. DCM of the IED-related BOLD signal favoured a model corresponding to the left dorsolateral frontal cortex as driver of changes in the fronto-polar region. The validity of DCM was supported by: (a the results of two different non-invasive analysis obtained on the same dataset: EEG source imaging (ESI, and psychophysiological interaction analysis (PPI; (b the failure of a first surgical intervention limited to the fronto-polar region; (c the results of the intracranial EEG monitoring performed after the first surgical intervention confirming a SOZ located over the dorso-lateral frontal cortex. These results add evidence that EEG-fMRI together with advanced methods of BOLD signal analysis is a promising tool that can give relevant information within the epilepsy surgery diagnostic work-up.

  14. Eye-gaze independent EEG-based brain-computer interfaces for communication

    Science.gov (United States)

    Riccio, A.; Mattia, D.; Simione, L.; Olivetti, M.; Cincotti, F.

    2012-08-01

    The present review systematically examines the literature reporting gaze independent interaction modalities in non-invasive brain-computer interfaces (BCIs) for communication. BCIs measure signals related to specific brain activity and translate them into device control signals. This technology can be used to provide users with severe motor disability (e.g. late stage amyotrophic lateral sclerosis (ALS); acquired brain injury) with an assistive device that does not rely on muscular contraction. Most of the studies on BCIs explored mental tasks and paradigms using visual modality. Considering that in ALS patients the oculomotor control can deteriorate and also other potential users could have impaired visual function, tactile and auditory modalities have been investigated over the past years to seek alternative BCI systems which are independent from vision. In addition, various attentional mechanisms, such as covert attention and feature-directed attention, have been investigated to develop gaze independent visual-based BCI paradigms. Three areas of research were considered in the present review: (i) auditory BCIs, (ii) tactile BCIs and (iii) independent visual BCIs. Out of a total of 130 search results, 34 articles were selected on the basis of pre-defined exclusion criteria. Thirteen articles dealt with independent visual BCIs, 15 reported on auditory BCIs and the last six on tactile BCIs, respectively. From the review of the available literature, it can be concluded that a crucial point is represented by the trade-off between BCI systems/paradigms with high accuracy and speed, but highly demanding in terms of attention and memory load, and systems requiring lower cognitive effort but with a limited amount of communicable information. These issues should be considered as priorities to be explored in future studies to meet users’ requirements in a real-life scenario.

  15. Brain mapping of epileptic activity in a case of idiopathic occipital lobe epilepsy (Panayiotopoulos syndrome).

    Science.gov (United States)

    Leal, Alberto J R; Nunes, Sofia; Martins, António; Secca, Mário Forjaz; Jordão, Constança

    2007-06-01

    The Panayiotopoulos type of occipital lobe epilepsy has generated great interest, but the particular brain areas involved in the peculiar seizure manifestations have not been established. We studied a patient with the syndrome, using high-resolution EEG and simultaneous EEG and functional magnetic resonance imaging (fMRI). Resolution of the scalp EEG was improved using a realistic spline Laplacian algorithm, and produced a complex distribution of current sinks and sources over the occipital lobe. The spike-related blood oxygen level dependent (BOLD) effect was multifocal, with clusters in lateral and inferior occipital lobe and lateral and anterior temporal lobe. We also performed regional dipole seeding in BOLD clusters to determine their relative contribution to generation of scalp spikes. The integrated model of the neurophysiologic and vascular data strongly suggests that the epileptic activity originates in the lateral occipital area, spreading to the occipital pole and lateral temporal lobe.

  16. Mapping brain activity with flexible graphene micro-transistors

    Science.gov (United States)

    Blaschke, Benno M.; Tort-Colet, Núria; Guimerà-Brunet, Anton; Weinert, Julia; Rousseau, Lionel; Heimann, Axel; Drieschner, Simon; Kempski, Oliver; Villa, Rosa; Sanchez-Vives, Maria V.; Garrido, Jose A.

    2017-06-01

    Establishing a reliable communication interface between the brain and electronic devices is of paramount importance for exploiting the full potential of neural prostheses. Current microelectrode technologies for recording electrical activity, however, evidence important shortcomings, e.g. challenging high density integration. Solution-gated field-effect transistors (SGFETs), on the other hand, could overcome these shortcomings if a suitable transistor material were available. Graphene is particularly attractive due to its biocompatibility, chemical stability, flexibility, low intrinsic electronic noise and high charge carrier mobilities. Here, we report on the use of an array of flexible graphene SGFETs for recording spontaneous slow waves, as well as visually evoked and also pre-epileptic activity in vivo in rats. The flexible array of graphene SGFETs allows mapping brain electrical activity with excellent signal-to-noise ratio (SNR), suggesting that this technology could lay the foundation for a future generation of in vivo recording implants.

  17. The issue of multiple univariate comparisons in the context of neuroelectric brain mapping: an application in a neuromarketing experiment.

    Science.gov (United States)

    Vecchiato, G; De Vico Fallani, F; Astolfi, L; Toppi, J; Cincotti, F; Mattia, D; Salinari, S; Babiloni, F

    2010-08-30

    This paper presents some considerations about the use of adequate statistical techniques in the framework of the neuroelectromagnetic brain mapping. With the use of advanced EEG/MEG recording setup involving hundred of sensors, the issue of the protection against the type I errors that could occur during the execution of hundred of univariate statistical tests, has gained interest. In the present experiment, we investigated the EEG signals from a mannequin acting as an experimental subject. Data have been collected while performing a neuromarketing experiment and analyzed with state of the art computational tools adopted in specialized literature. Results showed that electric data from the mannequin's head presents statistical significant differences in power spectra during the visualization of a commercial advertising when compared to the power spectra gathered during a documentary, when no adjustments were made on the alpha level of the multiple univariate tests performed. The use of the Bonferroni or Bonferroni-Holm adjustments returned correctly no differences between the signals gathered from the mannequin in the two experimental conditions. An partial sample of recently published literature on different neuroscience journals suggested that at least the 30% of the papers do not use statistical protection for the type I errors. While the occurrence of type I errors could be easily managed with appropriate statistical techniques, the use of such techniques is still not so largely adopted in the literature. Copyright (c) 2010 Elsevier B.V. All rights reserved.

  18. Mapping of electrophysiological response to transcranial infrared laser stimulation on the human brain in vivo measured by electroencephalography (Conference Presentation)

    Science.gov (United States)

    Wang, Xinlong; Reddy, Divya Dhandapani; Gonzalez-Lima, F.; Liu, Hanli

    2017-02-01

    Transcranial infrared laser stimulation (TILS) is a non-destructive and non-thermal photobiomodulation therapy or process on the human brain; TILS uses infrared light from lasers or LEDs and has gained increased recognition for its beneficial effects on a variety of neurological and psychological conditions. While the mechanism of TILS has been assumed to stem from cytochrome-c-oxidase (CCO), which is the last enzyme in the electron transportation chain and is the primary photoacceptor, no literature is found to report electrophysiological response to TILS. In this study, a 64-channel electroencephalography (EEG) system was employed to monitor electrophysiological activities from 15 healthy human participants before, during and after TILS. A placebo experimental protocol was also applied for rigorous comparison. After recording a 3-minute baseline, we applied a 1064-nm laser with a power of 3.5W on the right forehead of each human participant for 8 minutes, followed by a 5-minute recovery period. In 64-channel EEG data analysis, we utilized several methods (root mean square, principal component analysis followed by independent component analysis, permutation conditional mutual information, and time-frequency wavelet analysis) to reveal differences in electrophysiological response to TILS between the stimulated versus placebo group. The analyzed results were further investigated using general linear model and paired t-test to reveal statistically meaningful responses induced by TILS. Moreover, this study will provide spatial mapping of human electrophysiological and possibly neural network responses to TILS for first time, indicating the potential of EEG to be an effective method for monitoring neurological improvement induced by TILS.

  19. In Vivo Tumour Mapping Using Electrocorticography Alterations During Awake Brain Surgery: A Pilot Study.

    Science.gov (United States)

    Boussen, Salah; Velly, Lionel; Benar, Christian; Metellus, Philippe; Bruder, Nicolas; Trébuchon, Agnès

    2016-09-01

    During awake brain surgery for tumour resection, in situ EEG recording (ECoG) is used to identify eloquent areas surrounding the tumour. We used the ECoG setup to record the electrical activity of cortical and subcortical tumours and then performed frequency and connectivity analyses in order to identify ECoG impairments and map tumours. We selected 16 patients with cortical (8) and subcortical (8) tumours undergoing awake brain surgery. For each patient, we computed the spectral content of tumoural and healthy areas in each frequency band. We computed connectivity of each electrode using connectivity markers (linear and non-linear correlations, phase-locking and coherence). We performed comparisons between healthy and tumour electrodes. The ECoG alterations were used to implement automated classification of the electrodes using clustering or neural network algorithms. ECoG alterations were used to image cortical tumours.Cortical tumours were found to profoundly alter all frequency contents (normalized and absolute power), with an increase in the δ activity and a decreases for the other bands (P < 0.05). Cortical tumour electrodes showed high level of connectivity compared to surrounding electrodes (all markers, P < 0.05). For subcortical tumours, a relative decrease in the γ1 band and in the alpha band in absolute amplitude (P < 0.05) were the only abnormalities. The neural network algorithm classification had a good performance: 93.6 % of the electrodes were classified adequately on a test subject. We found significant spectral and connectivity ECoG changes for cortical tumours, which allowed tumour recognition. Artificial neural algorithm pattern recognition seems promising for electrode classification in awake tumour surgery.

  20. Z-score linear discriminant analysis for EEG based brain-computer interfaces.

    Directory of Open Access Journals (Sweden)

    Rui Zhang

    Full Text Available Linear discriminant analysis (LDA is one of the most popular classification algorithms for brain-computer interfaces (BCI. LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA, which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.

  1. Neuropeptide Mapping of Dimmed Cells of Adult Drosophila Brain

    Science.gov (United States)

    Diesner, Max; Predel, Reinhard; Neupert, Susanne

    2018-05-01

    Neuropeptides are structurally highly diverse messenger molecules that act as regulators of many physiological processes such as development, metabolism, reproduction or behavior in general. Differentiation of neuropeptidergic cells often corresponds with the presence of the transcription factor DIMMED. In the central nervous system of the fruit fly Drosophila melanogaster, DIMMED commonly occurs in neuroendocrine neurons that release peptides as neurohormones but also in interneurons with complex branching patterns. Fly strains with green fluorescence protein (GFP)-expressing dimmed cells make it possible to systematically analyze the processed neuropeptides in these cells. In this study, we mapped individual GFP-expressing neurons of adult D. melanogaster from the dimmed ( c929)>GFP line. Using single cell mass spectrometry, we analyzed 10 types of dimmed neurons from the brain/gnathal ganglion. These cells included neuroendocrine cells with projection into the retrocerebral complex but also a number of large interneurons. Resulting mass spectra not only provided comprehensive data regarding mature products from 13 neuropeptide precursors but also evidence for the cellular co-localization of neuropeptides from different neuropeptide genes. The results can be implemented in a neuroanatomical map of the D. melanogaster brain. [Figure not available: see fulltext.

  2. Mapping human brain networks with cortico-cortical evoked potentials

    Science.gov (United States)

    Keller, Corey J.; Honey, Christopher J.; Mégevand, Pierre; Entz, Laszlo; Ulbert, Istvan; Mehta, Ashesh D.

    2014-01-01

    The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex. PMID:25180306

  3. Neuropeptide Mapping of Dimmed Cells of Adult Drosophila Brain

    Science.gov (United States)

    Diesner, Max; Predel, Reinhard; Neupert, Susanne

    2018-01-01

    Neuropeptides are structurally highly diverse messenger molecules that act as regulators of many physiological processes such as development, metabolism, reproduction or behavior in general. Differentiation of neuropeptidergic cells often corresponds with the presence of the transcription factor DIMMED. In the central nervous system of the fruit fly Drosophila melanogaster, DIMMED commonly occurs in neuroendocrine neurons that release peptides as neurohormones but also in interneurons with complex branching patterns. Fly strains with green fluorescence protein (GFP)-expressing dimmed cells make it possible to systematically analyze the processed neuropeptides in these cells. In this study, we mapped individual GFP-expressing neurons of adult D. melanogaster from the dimmed (c929)>GFP line. Using single cell mass spectrometry, we analyzed 10 types of dimmed neurons from the brain/gnathal ganglion. These cells included neuroendocrine cells with projection into the retrocerebral complex but also a number of large interneurons. Resulting mass spectra not only provided comprehensive data regarding mature products from 13 neuropeptide precursors but also evidence for the cellular co-localization of neuropeptides from different neuropeptide genes. The results can be implemented in a neuroanatomical map of the D. melanogaster brain. [Figure not available: see fulltext.

  4. Relation of EEG alpha background to cognitive fuction, brain atrophy, and cerebral metabolism in Down's syndrome. Age-specific changes

    International Nuclear Information System (INIS)

    Devinsky, O.; Sato, S.; Conwit, R.A.; Schapiro, M.B.

    1990-01-01

    We studied 19 young adults (19 to 37 years old) and 9 older patients (42 to 66 years old) with Down's syndrome (DS) and a control group of 13 healthy adults (22 to 38 years old) to investigate the relation of electroencephalographic (EEG) alpha background to cognitive function and cerebral metabolism. Four of the older patients with DS had a history of mental deterioration, disorientation, and memory loss and were demented. Patients and control subjects had EEGs, psychometric testing, quantitative computed tomography, and positron emission tomography with fludeoxyglucose F 18. A blinded reader classified the EEGs into two groups--those with normal alpha background or those with abnormal background. All the control subjects, the 13 young adult patients with DS, and the 5 older patients with DS had normal EEG backgrounds. In comparison with the age-matched patients with DS with normal alpha background, older patients with DS with decreased alpha background had dementia, fewer visuospatial skills, decreased attention span, larger third ventricles, and a global decrease in cerebral glucose utilization with parietal hypometabolism. In the young patients with DS, the EEG background did not correlate with psychometric or positron emission tomographic findings, but the third ventricles were significantly larger in those with abnormal EEG background. The young patients with DS, with or without normal EEG background, had positron emission tomographic findings similar to those of the control subjects. The mechanism underlying the abnormal EEG background may be the neuropathologic changes of Alzheimer's disease in older patients with DS and may be cerebral immaturity in younger patients with DS

  5. sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.

    Science.gov (United States)

    Jrad, N; Congedo, M; Phlypo, R; Rousseau, S; Flamary, R; Yger, F; Rakotomamonjy, A

    2011-10-01

    In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

  6. Brain microstructure mapping using quantitative and diffusion MRI

    International Nuclear Information System (INIS)

    Lebois, Alice

    2014-01-01

    This thesis is focused on the human brain microstructure mapping using quantitative and diffusion MRI. The T1/T2 quantitative imaging relies on sequences dedicated to the mapping of T1 and T2 relaxation times. Their variations within the tissue are linked to the presence of different water compartments defined by a specific organization of the tissue at the cell scale. Measuring these parameters can help, therefore, to better characterize the brain microstructure. The dMRI, on the other hand, explores the brownian motion of water molecules in the brain tissue, where the water molecules' movement is constrained by natural barriers, such as cell membranes. Thus, the information on their displacement carried by the dMRI signal gives access to the underlying cyto-architecture. Combination of these two modalities is, therefore, a promising way to probe the brain tissue microstructure. The main goal of the present thesis is to set up the methodology to study the microstructure of the white matter of the human brain in vivo. The first part includes the acquisition of a unique MRI database of 79 healthy subjects (the Archi/CONNECT), which includes anatomical high resolution data, relaxometry data, diffusion-weighted data at high spatio-angular resolution and functional data. This database has allowed us to build the first atlas of the anatomical connectivity of the healthy brain through the automatic segmentation of the major white matter bundles, providing an appropriate anatomical reference for the white matter to study individually the quantitative parameters along each fascicle, characterizing its microstructure organization. Emphasis was placed on the construction of the first atlas of the T1/T2 profiles along the major white matter pathways. The profiles of the T1 and T2 relaxation times were then correlated to the quantitative profiles computed from the diffusion MRI data (fractional anisotropy, radial and longitudinal diffusivities, apparent diffusion coefficient

  7. Mapping neuroplastic potential in brain-damaged patients.

    Science.gov (United States)

    Herbet, Guillaume; Maheu, Maxime; Costi, Emanuele; Lafargue, Gilles; Duffau, Hugues

    2016-03-01

    It is increasingly acknowledged that the brain is highly plastic. However, the anatomic factors governing the potential for neuroplasticity have hardly been investigated. To bridge this knowledge gap, we generated a probabilistic atlas of functional plasticity derived from both anatomic magnetic resonance imaging results and intraoperative mapping data on 231 patients having undergone surgery for diffuse, low-grade glioma. The atlas includes detailed level of confidence information and is supplemented with a series of comprehensive, connectivity-based cluster analyses. Our results show that cortical plasticity is generally high in the cortex (except in primary unimodal areas and in a small set of neural hubs) and rather low in connective tracts (especially associative and projection tracts). The atlas sheds new light on the topological organization of critical neural systems and may also be useful in predicting the likelihood of recovery (as a function of lesion topology) in various neuropathological conditions-a crucial factor in improving the care of brain-damaged patients. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  8. Mapping brain development during childhood, adolescence and young adulthood

    Science.gov (United States)

    Guo, Xiaojuan; Jin, Zhen; Chen, Kewei; Peng, Danling; Li, Yao

    2009-02-01

    Using optimized voxel-based morphometry (VBM), this study systematically investigated the differences and similarities of brain structural changes during the early three developmental periods of human lives: childhood, adolescence and young adulthood. These brain changes were discussed in relationship to the corresponding cognitive function development during these three periods. Magnetic Resonance Imaging (MRI) data from 158 Chinese healthy children, adolescents and young adults, aged 7.26 to 22.80 years old, were included in this study. Using the customized brain template together with the gray matter/white matter/cerebrospinal fluid prior probability maps, we found that there were more age-related positive changes in the frontal lobe, less in hippocampus and amygdala during childhood, but more in bilateral hippocampus and amygdala and left fusiform gyrus during adolescence and young adulthood. There were more age-related negative changes near to central sulcus during childhood, but these changes extended to the frontal and parietal lobes, mainly in the parietal lobe, during adolescence and young adulthood, and more in the prefrontal lobe during young adulthood. So gray matter volume in the parietal lobe significantly decreased from childhood and continued to decrease till young adulthood. These findings may aid in understanding the age-related differences in cognitive function.

  9. Classification of EEG-P300 Signals Extracted from Brain Activities in BCI Systems Using ν-SVM and BLDA Algorithms

    Directory of Open Access Journals (Sweden)

    Ali MOMENNEZHAD

    2014-06-01

    Full Text Available In this paper, a linear predictive coding (LPC model is used to improve classification accuracy, convergent speed to maximum accuracy, and maximum bitrates in brain computer interface (BCI system based on extracting EEG-P300 signals. First, EEG signal is filtered in order to eliminate high frequency noise. Then, the parameters of filtered EEG signal are extracted using LPC model. Finally, the samples are reconstructed by LPC coefficients and two classifiers, a Bayesian Linear discriminant analysis (BLDA, and b the υ-support vector machine (υ-SVM are applied in order to classify. The proposed algorithm performance is compared with fisher linear discriminant analysis (FLDA. Results show that the efficiency of our algorithm in improving classification accuracy and convergent speed to maximum accuracy are much better. As example at the proposed algorithms, respectively BLDA with LPC model and υ-SVM with LPC model with8 electrode configuration for subject S1 the total classification accuracy is improved as 9.4% and 1.7%. And also, subject 7 at BLDA and υ-SVM with LPC model algorithms (LPC+BLDA and LPC+ υ-SVM after block 11th converged to maximum accuracy but Fisher Linear Discriminant Analysis (FLDA algorithm did not converge to maximum accuracy (with the same configuration. So, it can be used as a promising tool in designing BCI systems.

  10. Systems Neuroscience of Psychosis: Mapping Schizophrenia Symptoms onto Brain Systems.

    Science.gov (United States)

    Strik, Werner; Stegmayer, Katharina; Walther, Sebastian; Dierks, Thomas

    2017-01-01

    Schizophrenia research has been in a deadlock for many decades. Despite important advances in clinical treatment, there are still major concerns regarding long-term psychosocial reintegration and disease management, biological heterogeneity, unsatisfactory predictors of individual course and treatment strategies, and a confusing variety of controversial theories about its etiology and pathophysiological mechanisms. In the present perspective on schizophrenia research, we first discuss a methodological pitfall in contemporary schizophrenia research inherent in the attempt to link mental phenomena with the brain: we claim that the time-honored phenomenological method of defining mental symptoms should not be contaminated with the naturalistic approach of modern neuroscience. We then describe our Systems Neuroscience of Psychosis (SyNoPsis) project, which aims to overcome this intrinsic problem of psychiatric research. Considering schizophrenia primarily as a disorder of interindividual communication, we developed a neurobiologically informed semiotics of psychotic disorders, as well as an operational clinical rating scale. The novel psychopathology allows disentangling the clinical manifestations of schizophrenia into behavioral domains matching the functions of three well-described higher-order corticobasal brain systems involved in interindividual human communication, namely, the limbic, associative, and motor loops, including their corticocortical sensorimotor connections. The results of several empirical studies support the hypothesis that the proposed three-dimensional symptom structure, segregated into the affective, the language, and the motor domain, can be specifically mapped onto structural and functional abnormalities of the respective brain systems. New pathophysiological hypotheses derived from this brain system-oriented approach have helped to develop and improve novel treatment strategies with noninvasive brain stimulation and practicable clinical

  11. Simultaneous head tissue conductivity and EEG source location estimation.

    Science.gov (United States)

    Akalin Acar, Zeynep; Acar, Can E; Makeig, Scott

    2016-01-01

    Accurate electroencephalographic (EEG) source localization requires an electrical head model incorporating accurate geometries and conductivity values for the major head tissues. While consistent conductivity values have been reported for scalp, brain, and cerebrospinal fluid, measured brain-to-skull conductivity ratio (BSCR) estimates have varied between 8 and 80, likely reflecting both inter-subject and measurement method differences. In simulations, mis-estimation of skull conductivity can produce source localization errors as large as 3cm. Here, we describe an iterative gradient-based approach to Simultaneous tissue Conductivity And source Location Estimation (SCALE). The scalp projection maps used by SCALE are obtained from near-dipolar effective EEG sources found by adequate independent component analysis (ICA) decomposition of sufficient high-density EEG data. We applied SCALE to simulated scalp projections of 15cm(2)-scale cortical patch sources in an MR image-based electrical head model with simulated BSCR of 30. Initialized either with a BSCR of 80 or 20, SCALE estimated BSCR as 32.6. In Adaptive Mixture ICA (AMICA) decompositions of (45-min, 128-channel) EEG data from two young adults we identified sets of 13 independent components having near-dipolar scalp maps compatible with a single cortical source patch. Again initialized with either BSCR 80 or 25, SCALE gave BSCR estimates of 34 and 54 for the two subjects respectively. The ability to accurately estimate skull conductivity non-invasively from any well-recorded EEG data in combination with a stable and non-invasively acquired MR imaging-derived electrical head model could remove a critical barrier to using EEG as a sub-cm(2)-scale accurate 3-D functional cortical imaging modality. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. Functional Disorganization of Small-World Brain Networks in mild Alzheimer’s Disease and amnestic Mild Cognitive Impairment: An EEG Study using Relative Wavelet Entropy (RWE

    Directory of Open Access Journals (Sweden)

    Christos A. Frantzidis

    2014-08-01

    Full Text Available Previous neuroscientific findings have linked Alzheimer’s disease (AD with less efficient information processing and brain network disorganization. However, pathological alterations of the brain networks during the preclinical phase of amnestic Mild Cognitive Impairment (aMCI remain largely unknown. The present study aimed at comparing patterns of the detection of functional disorganization in MCI relative to Mild Dementia (MD. Participants consisted of 23 cognitively healthy adults, 17 aMCI and 24 mild AD patients who underwent electroencephalographic (EEG data acquisition during a resting-state condition. Synchronization analysis through the Orthogonal Discrete Wavelet Transform (ODWT, and directional brain network analysis were applied on the EEG data. This computational model was performed for networks that have the same number of edges (N=500, 600, 700, 800 edges across all participants and groups (fixed density values. All groups exhibited a small-world (SW brain architecture. However, we found a significant reduction in the SW brain architecture in both aMCI and MD patients relative to the group of Healthy controls. This functional disorganization was also correlated with the participant’s generic cognitive status. The deterioration of the network’s organization was caused mainly by deficient local information processing as quantified by the mean cluster coefficient value. Functional hubs were identified through the normalized betweenness centrality metric. Analysis of the local characteristics showed relative hub preservation even with statistically significant reduced strength. Compensatory phenomena were also evident through the formation of additional hubs on left frontal and parietal regions. Our results indicate a declined functional network organization even during the prodromal phase. Degeneration is evident even in the preclinical phase and coexists with transient network reorganization due to compensation.

  13. In vivo mapping of brain myo-inositol.

    Science.gov (United States)

    Haris, Mohammad; Cai, Kejia; Singh, Anup; Hariharan, Hari; Reddy, Ravinder

    2011-02-01

    Myo-Inositol (MI) is one of the most abundant metabolites in the human brain located mainly in glial cells and functions as an osmolyte. The concentration of MI is altered in many brain disorders including Alzheimer's disease and brain tumors. Currently available magnetic resonance spectroscopy (MRS) methods for measuring MI are limited to low spatial resolution. Here, we demonstrate that the hydroxyl protons on MI exhibit chemical exchange with bulk water and saturation of these protons leads to reduction in bulk water signal through a mechanism known as chemical exchange saturation transfer (CEST). The hydroxyl proton exchange rate (k=600 s(-1)) is determined to be in the slow to intermediate exchange regime on the NMR time scale (chemical shift (∆ω)>k), suggesting that the CEST effect of MI (MICEST) can be imaged at high fields such as 7 T (∆ω=1.2×10(3)rad/s) and 9.4 T (∆ω=1.6×10(3) rad/s). Using optimized imaging parameters, concentration dependent broad CEST asymmetry between ~0.2 and 1.5 ppm with a peak at ~0.6 ppm from bulk water was observed. Further, it is demonstrated that MICEST detection is feasible in the human brain at ultra high fields (7 T) without exceeding the allowed limits on radiofrequency specific absorption rate. Results from healthy human volunteers (N=5) showed significantly higher (p=0.03) MICEST effect from white matter (5.2±0.5%) compared to gray matter (4.3±0.5%). The mean coefficient of variations for intra-subject MICEST contrast in WM and GM were 0.49 and 0.58 respectively. Potential overlap of CEST signals from other brain metabolites with the observed MICEST map is discussed. This noninvasive approach potentially opens the way to image MI in vivo and to monitor its alteration in many disease conditions. Copyright © 2010 Elsevier Inc. All rights reserved.

  14. Evaluation of seizure propagation on ictal brain SPECT using statistical parametric mapping in temporal lobe epilepsy

    International Nuclear Information System (INIS)

    Jeon, Tae Joo; Lee, Jong Doo; Kim, Hee Joung; Lee, Byung In; Kim, Ok Joon; Kim, Min Jung; Jeon, Jeong Dong

    1999-01-01

    Ictal brain SPECT has a high diagnostic sensitivity exceeding 90 % in the localization of seizure focus, however, it often shows increased uptake within the extratemporal areas due to early propagation of seizure discharge. This study aimed to evaluate seizure propagation on ictal brian SPECT in patients with temporal lobe epilepsy (TLE) by statistical parametric mapping (SPM). Twenty-one patients (age 27.14 5.79 y) with temporal lobe epilepsy (right in 8, left in 13) who had successful seizure outcome after surgery and nine normal control were included. The data of ictal and interictal brain SPECT of the patients and baseline SPECT of normal control group were analyzed using automatic image registration and SPM96 softwares. The statistical analysis was performed to compare the mean SPECT image of normal group with individual ictal SPECT, and each mean image of the interictal groups of the right or left TLE with individual ictal scans. The t statistic SPM [t] was transformed to SPM [Z] with a threshold of 1.64. The statistical results were displayed and rendered on the reference 3 dimensional MRI images with P value of 0.05 and uncorrected extent threshold p value of 0.5 for SPM [Z]. SPM data demonstrated increased uptake within the epileptic lesion in 19 patients (90.4 %), among them, localized increased uptake confined to the epileptogenic lesion was seen in only 4 (19%) but 15 patients (71.4%) showed hyperperfusion within propagation sites. Bi-temporal hyperperfusion was observed in 11 out of 19 patients (57.9%, 5 in the right and 6 in the left); higher uptake within the lesion than contralateral side in 9, similar activity in 1 and higher uptake within contralateral lobe in one. Extra-temporal hyperperfusion was observed in 8 (2 in the right, 3 in the left, 3 in bilateral); unilateral hyperperfusion within the epileptogenic temporal lobe and extra-temporal area in 4, bi-temporal with extra-temporal hyperperfusion in remaining 4. Ictal brain SPECT is highly

  15. Mapping oxygen concentration in the awake mouse brain

    Science.gov (United States)

    Lyons, Declan G; Parpaleix, Alexandre; Roche, Morgane; Charpak, Serge

    2016-01-01

    Although critical for brain function, the physiological values of cerebral oxygen concentration have remained elusive because high-resolution measurements have only been performed during anesthesia, which affects two major parameters modulating tissue oxygenation: neuronal activity and blood flow. Using measurements of capillary erythrocyte-associated transients, fluctuations of oxygen partial pressure (Po2) associated with individual erythrocytes, to infer Po2 in the nearby neuropil, we report the first non-invasive micron-scale mapping of cerebral Po2 in awake, resting mice. Interstitial Po2 has similar values in the olfactory bulb glomerular layer and the somatosensory cortex, whereas there are large capillary hematocrit and erythrocyte flux differences. Awake tissue Po2 is about half that under isoflurane anesthesia, and within the cortex, vascular and interstitial Po2 values display layer-specific differences which dramatically contrast with those recorded under anesthesia. Our findings emphasize the importance of measuring energy parameters non-invasively in physiological conditions to precisely quantify and model brain metabolism. DOI: http://dx.doi.org/10.7554/eLife.12024.001 PMID:26836304

  16. Design and optimization of an EEG-based brain machine interface (BMI to an upper-limb exoskeleton for stroke survivors

    Directory of Open Access Journals (Sweden)

    Nikunj Arunkumar Bhagat

    2016-03-01

    Full Text Available This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG-based brain machine interface (BMI. Intent was inferred from movement related cortical potentials (MRCPs measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II, to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: 1 an adaptive time window was used for extracting features during BMI calibration; 2 training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and 3 BMI predictions were gated by residual electromyography (EMG activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR = 62.7 +/- 21.4 % on day 4 and 67.1 +/- 14.6 % on day 5. The overall false positive rate (FPR across subjects was 27.74 +/- 37.46 % on day 4 and 27.5 +/- 35.64 % on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10 %. On average, motor intent was detected -367 +/- 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration.

  17. Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors

    Science.gov (United States)

    Bhagat, Nikunj A.; Venkatakrishnan, Anusha; Abibullaev, Berdakh; Artz, Edward J.; Yozbatiran, Nuray; Blank, Amy A.; French, James; Karmonik, Christof; Grossman, Robert G.; O'Malley, Marcia K.; Francisco, Gerard E.; Contreras-Vidal, Jose L.

    2016-01-01

    This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG)-based brain machine interface (BMI). Intent was inferred from movement related cortical potentials (MRCPs) measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II), to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: (1) an adaptive time window was used for extracting features during BMI calibration; (2) training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and (3) BMI predictions were gated by residual electromyography (EMG) activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR) = 62.7 ± 21.4% on day 4 and 67.1 ± 14.6% on day 5. The overall false positive rate (FPR) across subjects was 27.74 ± 37.46% on day 4 and 27.5 ± 35.64% on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10%). On average, motor intent was detected −367 ± 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration. PMID:27065787

  18. Suitable reference tissues for quantitative susceptibility mapping of the brain.

    Science.gov (United States)

    Straub, Sina; Schneider, Till M; Emmerich, Julian; Freitag, Martin T; Ziener, Christian H; Schlemmer, Heinz-Peter; Ladd, Mark E; Laun, Frederik B

    2017-07-01

    Since quantitative susceptibility mapping (QSM) quantifies magnetic susceptibility relative to a reference value, a suitable reference tissue has to be available to compare different subjects and stages of disease. To find such a suitable reference tissue for QSM of the brain, melanoma patients with and without brain metastases were measured. Twelve reference regions were chosen and assessed for stability of susceptibility values with respect to multiple intra-individual and inter-individual measurements, age, and stage of disease. Cerebrospinal fluid (CSF), the internal capsule and one region in the splenium of the corpus callosum are the regions with the smallest standard deviations of the mean susceptibility value. The mean susceptibility is 0.010 ± 0.014 ppm for CSF in the atrium of the lateral ventricles (csf post ), -0.060 ± 0.019 ppm for the posterior limb of the internal capsule (ci2), and -0.008 ± 0.019 ppm for the splenium of the corpus callosum. csf post and ci2 show nearly no dependence on age or stage of disease, whereas some other regions, e.g., the red nucleus, show moderate dependence on age or disease. The internal capsule and CSF appear to be the most suitable reference regions for QSM of the brain in the melanoma patients studied. Both showed virtually no dependence on age or disease and small variations among patients. Magn Reson Med 78:204-214, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

  19. Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface.

    Science.gov (United States)

    Siuly; Li, Yan; Paul Wen, Peng

    2014-03-01

    Motor imagery (MI) tasks classification provides an important basis for designing brain-computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10-20 system) as a reference channel for the cross-correlation (CC) technique and applies three diverse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  20. Psychogenic seizures and frontal disconnection: EEG synchronisation study.

    Science.gov (United States)

    Knyazeva, Maria G; Jalili, Mahdi; Frackowiak, Richard S; Rossetti, Andrea O

    2011-05-01

    Psychogenic non-epileptic seizures (PNES) are paroxysmal events that, in contrast to epileptic seizures, are related to psychological causes without the presence of epileptiform EEG changes. Recent models suggest a multifactorial basis for PNES. A potentially paramount, but currently poorly understood factor is the interplay between psychiatric features and a specific vulnerability of the brain leading to a clinical picture that resembles epilepsy. Hypothesising that functional cerebral network abnormalities may predispose to the clinical phenotype, the authors undertook a characterisation of the functional connectivity in PNES patients. The authors analysed the whole-head surface topography of multivariate phase synchronisation (MPS) in interictal high-density EEG of 13 PNES patients as compared with 13 age- and sex-matched controls. MPS mapping reduces the wealth of dynamic data obtained from high-density EEG to easily readable synchronisation maps, which provide an unbiased overview of any changes in functional connectivity associated with distributed cortical abnormalities. The authors computed MPS maps for both Laplacian and common-average-reference EEGs. In a between-group comparison, only patchy, non-uniform changes in MPS survived conservative statistical testing. However, against the background of these unimpressive group results, the authors found widespread inverse correlations between individual PNES frequency and MPS within the prefrontal and parietal cortices. PNES appears to be associated with decreased prefrontal and parietal synchronisation, possibly reflecting dysfunction of networks within these regions.

  1. Task-evoked brain functional magnetic susceptibility mapping by independent component analysis (χICA).

    Science.gov (United States)

    Chen, Zikuan; Calhoun, Vince D

    2016-03-01

    Conventionally, independent component analysis (ICA) is performed on an fMRI magnitude dataset to analyze brain functional mapping (AICA). By solving the inverse problem of fMRI, we can reconstruct the brain magnetic susceptibility (χ) functional states. Upon the reconstructed χ dataspace, we propose an ICA-based brain functional χ mapping method (χICA) to extract task-evoked brain functional map. A complex division algorithm is applied to a timeseries of fMRI phase images to extract temporal phase changes (relative to an OFF-state snapshot). A computed inverse MRI (CIMRI) model is used to reconstruct a 4D brain χ response dataset. χICA is implemented by applying a spatial InfoMax ICA algorithm to the reconstructed 4D χ dataspace. With finger-tapping experiments on a 7T system, the χICA-extracted χ-depicted functional map is similar to the SPM-inferred functional χ map by a spatial correlation of 0.67 ± 0.05. In comparison, the AICA-extracted magnitude-depicted map is correlated with the SPM magnitude map by 0.81 ± 0.05. The understanding of the inferiority of χICA to AICA for task-evoked functional map is an ongoing research topic. For task-evoked brain functional mapping, we compare the data-driven ICA method with the task-correlated SPM method. In particular, we compare χICA with AICA for extracting task-correlated timecourses and functional maps. χICA can extract a χ-depicted task-evoked brain functional map from a reconstructed χ dataspace without the knowledge about brain hemodynamic responses. The χICA-extracted brain functional χ map reveals a bidirectional BOLD response pattern that is unavailable (or different) from AICA. Copyright © 2016 Elsevier B.V. All rights reserved.

  2. INTELLIGENT EEG ANALYSIS

    Directory of Open Access Journals (Sweden)

    M. Murugesan

    2011-04-01

    Full Text Available Brain is the wonderful organ of human body. It is the agent of information collection and transformation. The neural activity of the human brain starts between the 17th and 23rd week of prenatal development. It is believed that from this early stage and throughout life electrical signals are generated by the brain function but also the status of the whole body. Understanding of neuronal functions and neurophysiologic properties of the brain function together with the mechanisms underlying the generation of signals and their recording is, however, vital for those who deal with these signals for detection, diagnosis, and treatment of brain disorders and the related diseases. This research paper concentrated only on brain tumor detection. Using minimum electrode location the brain tumor possibility is detected. This paper is separated into two parts: the First part deals with electrode location on the scalp and the second part deals with how the fuzzy logic rule based algorithm is applied for estimation of brain tumor from EEG. Basically 8 locations are identified. After acquiring the pure EEG signal Fuzzy Logic Rule is applied to predict the possibility of brain tumor.

  3. Design of an EEG-based brain-computer interface (BCI) from standard components running in real-time under Windows.

    Science.gov (United States)

    Guger, C; Schlögl, A; Walterspacher, D; Pfurtscheller, G

    1999-01-01

    An EEG-based brain-computer interface (BCI) is a direct connection between the human brain and the computer. Such a communication system is needed by patients with severe motor impairments (e.g. late stage of Amyotrophic Lateral Sclerosis) and has to operate in real-time. This paper describes the selection of the appropriate components to construct such a BCI and focuses also on the selection of a suitable programming language and operating system. The multichannel system runs under Windows 95, equipped with a real-time Kernel expansion to obtain reasonable real-time operations on a standard PC. Matlab controls the data acquisition and the presentation of the experimental paradigm, while Simulink is used to calculate the recursive least square (RLS) algorithm that describes the current state of the EEG in real-time. First results of the new low-cost BCI show that the accuracy of differentiating imagination of left and right hand movement is around 95%.

  4. Brain Injury Lesion Imaging Using Preconditioned Quantitative Susceptibility Mapping without Skull Stripping.

    Science.gov (United States)

    Soman, S; Liu, Z; Kim, G; Nemec, U; Holdsworth, S J; Main, K; Lee, B; Kolakowsky-Hayner, S; Selim, M; Furst, A J; Massaband, P; Yesavage, J; Adamson, M M; Spincemallie, P; Moseley, M; Wang, Y

    2018-04-01

    Identifying cerebral microhemorrhage burden can aid in the diagnosis and management of traumatic brain injury, stroke, hypertension, and cerebral amyloid angiopathy. MR imaging susceptibility-based methods are more sensitive than CT for detecting cerebral microhemorrhage, but methods other than quantitative susceptibility mapping provide results that vary with field strength and TE, require additional phase maps to distinguish blood from calcification, and depict cerebral microhemorrhages as bloom artifacts. Quantitative susceptibility mapping provides universal quantification of tissue magnetic property without these constraints but traditionally requires a mask generated by skull-stripping, which can pose challenges at tissue interphases. We evaluated the preconditioned quantitative susceptibility mapping MR imaging method, which does not require skull-stripping, for improved depiction of brain parenchyma and pathology. Fifty-six subjects underwent brain MR imaging with a 3D multiecho gradient recalled echo acquisition. Mask-based quantitative susceptibility mapping images were created using a commonly used mask-based quantitative susceptibility mapping method, and preconditioned quantitative susceptibility images were made using precondition-based total field inversion. All images were reviewed by a neuroradiologist and a radiology resident. Ten subjects (18%), all with traumatic brain injury, demonstrated blood products on 3D gradient recalled echo imaging. All lesions were visible on preconditioned quantitative susceptibility mapping, while 6 were not visible on mask-based quantitative susceptibility mapping. Thirty-one subjects (55%) demonstrated brain parenchyma and/or lesions that were visible on preconditioned quantitative susceptibility mapping but not on mask-based quantitative susceptibility mapping. Six subjects (11%) demonstrated pons artifacts on preconditioned quantitative susceptibility mapping and mask-based quantitative susceptibility mapping

  5. Functional brain mapping using H215O positron emission tomography (I): statistical parametric mapping method

    International Nuclear Information System (INIS)

    Lee, Dong Soo; Lee, Jae Sung; Kim, Kyeong Min; Chung, June Key; Lee, Myung Chul

    1998-01-01

    We investigated the statistical methods to compose the functional brain map of human working memory and the principal factors that have an effect on the methods for localization. Repeated PET scans with successive four tasks, which consist of one control and three different activation tasks, were performed on six right-handed normal volunteers for 2 minutes after bolus injections of 925 MBq H 2 15 O at the intervals of 30 minutes. Image data were analyzed using SPM96 (Statistical Parametric Mapping) implemented with Matlab (Mathworks Inc., U.S.A.). Images from the same subject were spatially registered and were normalized using linear and nonlinear transformation methods. Significant difference between control and each activation state was estimated at every voxel based on the general linear model. Differences of global counts were removed using analysis of covariance (ANCOVA) with global activity as covariate. Using the mean and variance for each condition which was adjusted using ANCOVA, t-statistics was performed on every voxel. To interpret the results more easily, t-values were transformed to the standard Gaussian distribution (Z-score). All the subjects carried out the activation and control tests successfully. Average rate of correct answers was 95%. The numbers of activated blobs were 4 for verbal memory I, 9 for verbal memory II, 9 for visual memory, and 6 for conjunctive activation of these three tasks. The verbal working memory activates predominantly left-sided structures, and the visual memory activates the right hemisphere. We conclude that rCBF PET imaging and statistical parametric mapping method were useful in the localization of the brain regions for verbal and visual working memory

  6. moviEEG: An animation toolbox for visualization of intracranial electroencephalography synchronization dynamics.

    Science.gov (United States)

    Wong, Simeon M; Ibrahim, George M; Ochi, Ayako; Otsubo, Hiroshi; Rutka, James T; Snead, O Carter; Doesburg, Sam M

    2016-06-01

    We introduce and describe the functions of moviEEG (Multiple Overlay Visualizations for Intracranial ElectroEncephaloGraphy), a novel MATLAB-based toolbox for spatiotemporal mapping of network synchronization dynamics in intracranial electroencephalography (iEEG) data. The toolbox integrates visualizations of inter-electrode phase-locking relationships in peri-ictal epileptogenic networks with signal spectral properties and graph-theoretical network measures overlaid upon operating room images of the electrode grid. Functional connectivity between every electrode pair is evaluated over a sliding window indexed by phase synchrony. Two case studies are presented to provide preliminary evidence for the application of the toolbox to guide network-based mapping of epileptogenic cortex and to distinguish these regions from eloquent brain networks. In both cases, epileptogenic cortex was visually distinct. We introduce moviEEG, a novel toolbox for animation of oscillatory network dynamics in iEEG data, and provide two case studies showing preliminary evidence for utility of the toolbox in delineating the epileptogenic zone. Despite evidence that atypical network synchronization has shown to be altered in epileptogenic brain regions, network based techniques have yet to be incorporated into clinical pre-surgical mapping. moviEEG provides a set of functions to enable easy visualization with network based techniques. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  7. Characterization of functional brain activity and connectivity using EEG and fMRI in patients with sickle cell disease.

    Science.gov (United States)

    Case, Michelle; Zhang, Huishi; Mundahl, John; Datta, Yvonne; Nelson, Stephen; Gupta, Kalpna; He, Bin

    2017-01-01

    Sickle cell disease (SCD) is a red blood cell disorder that causes many complications including life-long pain. Treatment of pain remains challenging due to a poor understanding of the mechanisms and limitations to characterize and quantify pain. In the present study, we examined simultaneously recording functional MRI (fMRI) and electroencephalogram (EEG) to better understand neural connectivity as a consequence of chronic pain in SCD patients. We performed independent component analysis and seed-based connectivity on fMRI data. Spontaneous power and microstate analysis was performed on EEG-fMRI data. ICA analysis showed that patients lacked activity in the default mode network (DMN) and executive control network compared to controls. EEG-fMRI data revealed that the insula cortex's role in salience increases with age in patients. EEG microstate analysis showed patients had increased activity in pain processing regions. The cerebellum in patients showed a stronger connection to the periaqueductal gray matter (involved in pain inhibition), and negative connections to pain processing areas. These results suggest that patients have reduced activity of DMN and increased activity in pain processing regions during rest. The present findings suggest resting state connectivity differences between patients and controls can be used as novel biomarkers of SCD pain.

  8. Near-infrared spectroscopy (NIRS - electroencephalography (EEG based brain-state dependent electrotherapy (BSDE: A computational approach based on excitation-inhibition balance hypothesis

    Directory of Open Access Journals (Sweden)

    Snigdha Dagar

    2016-08-01

    Full Text Available Stroke is the leading cause of severe chronic disability and the second cause of death worldwide with 15 million new cases and 50 million stroke survivors. The post stroke chronic disability may be ameliorated with early neuro rehabilitation where non-invasive brain stimulation (NIBS techniques can be used as an adjuvant treatment to hasten the effects. However, the heterogeneity in the lesioned brain will require individualized NIBS intervention where innovative neuroimaging technologies of portable electroencephalography (EEG and functional-near-infrared spectroscopy (fNIRS can be leveraged for Brain State Dependent Electrotherapy (BSDE. In this hypothesis and theory article, we propose a computational approach based on excitation-inhibition (E-I balance hypothesis to objectively quantify the post stroke individual brain state using online fNIRS-EEG joint imaging. One of the key events that occurs following Stroke is the imbalance in local excitation-inhibition (that is the ratio of Glutamate/GABA which may be targeted with NIBS using a computational pipeline that includes individual forward models to predict current flow patterns through the lesioned brain or brain target region. The current flow will polarize the neurons which can be captured with excitation-inhibition based brain models. Furthermore, E-I balance hypothesis can be used to find the consequences of cellular polarization on neuronal information processing which can then be implicated in changes in function. We first review evidence that shows how this local imbalance between excitation-inhibition leading to functional dysfunction can be restored in targeted sites with NIBS (Motor Cortex, Somatosensory Cortex resulting in large scale plastic reorganization over the cortex, and probably facilitating recovery of functions. Secondly, we show evidence how BSDE based on inhibition–excitation balance hypothesis may target a specific brain site or network as an adjuvant treatment

  9. Deep brain stimulation, brain maps and personalized medicine: lessons from the human genome project.

    Science.gov (United States)

    Fins, Joseph J; Shapiro, Zachary E

    2014-01-01

    Although the appellation of personalized medicine is generally attributed to advanced therapeutics in molecular medicine, deep brain stimulation (DBS) can also be so categorized. Like its medical counterpart, DBS is a highly personalized intervention that needs to be tailored to a patient's individual anatomy. And because of this, DBS like more conventional personalized medicine, can be highly specific where the object of care is an N = 1. But that is where the similarities end. Besides their differing medical and surgical provenances, these two varieties of personalized medicine have had strikingly different impacts. The molecular variant, though of a more recent vintage has thrived and is experiencing explosive growth, while DBS still struggles to find a sustainable therapeutic niche. Despite its promise, and success as a vetted treatment for drug resistant Parkinson's Disease, DBS has lagged in broadening its development, often encountering regulatory hurdles and financial barriers necessary to mount an adequate number of quality trials. In this paper we will consider why DBS-or better yet neuromodulation-has encountered these challenges and contrast this experience with the more successful advance of personalized medicine. We will suggest that personalized medicine and DBS's differential performance can be explained as a matter of timing and complexity. We believe that DBS has struggled because it has been a journey of scientific exploration conducted without a map. In contrast to molecular personalized medicine which followed the mapping of the human genome and the Human Genome Project, DBS preceded plans for the mapping of the human brain. We believe that this sequence has given personalized medicine a distinct advantage and that the fullest potential of DBS will be realized both as a cartographical or electrophysiological probe and as a modality of personalized medicine.

  10. The Utility of EEG in Attention Deficit Hyperactivity Disorder: A Replication Study.

    Science.gov (United States)

    Swatzyna, Ronald J; Tarnow, Jay D; Roark, Alexandra; Mardick, Jacob

    2017-07-01

    The routine use of stimulants in pediatrics has increased dramatically over the past 3 decades and the long-term consequences have yet to be fully studied. Since 1978 there have been 7 articles identifying electroencephalogram (EEG) abnormalities, particularly epileptiform discharges in children with attention deficit hyperactivity disorder (ADHD). Many have studied the prevalence of these discharges in this population with varying results. An article published in 2011 suggests that EEG technology should be considered prior to prescribing stimulants to children diagnosed with ADHD due to a high prevalence of epileptiform discharges. The 2011 study found a higher prevalence (26%) of epileptiform discharges when using 23-hour and sleep-deprived EEGs in comparison with other methods of activation (hyperventilation or photostimulation) and conventional EEG. We sought to replicate the 2011 results using conventional EEG with the added qEEG technologies of automatic spike detection and low-resolution electromagnetic tomography analysis (LORETA) brain mapping. Our results showed 32% prevalence of epileptiform discharges, which suggests that an EEG should be considered prior to prescribing stimulant medications.

  11. Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone.

    Science.gov (United States)

    Schindler, Kaspar; Rummel, Christian; Andrzejak, Ralph G; Goodfellow, Marc; Zubler, Frédéric; Abela, Eugenio; Wiest, Roland; Pollo, Claudio; Steimer, Andreas; Gast, Heidemarie

    2016-09-01

    To show that time-irreversible EEG signals recorded with intracranial electrodes during seizures can serve as markers of the epileptogenic zone. We use the recently developed method of mapping time series into directed horizontal graphs (dHVG). Each node of the dHVG represents a time point in the original intracranial EEG (iEEG) signal. Statistically significant differences between the distributions of the nodes' number of input and output connections are used to detect time-irreversible iEEG signals. In 31 of 32 seizure recordings we found time-irreversible iEEG signals. The maximally time-irreversible signals always occurred during seizures, with highest probability in the middle of the first seizure half. These signals spanned a large range of frequencies and amplitudes but were all characterized by saw-tooth like shaped components. Brain regions removed from patients who became post-surgically seizure-free generated significantly larger time-irreversibilities than regions removed from patients who still had seizures after surgery. Our results corroborate that ictal time-irreversible iEEG signals can indeed serve as markers of the epileptogenic zone and can be efficiently detected and quantified in a time-resolved manner by dHVG based methods. Ictal time-irreversible EEG signals can help to improve pre-surgical evaluation in patients suffering from pharmaco-resistant epilepsies. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  12. A Game Player Expertise Level Classification System Using Electroencephalography (EEG

    Directory of Open Access Journals (Sweden)

    Syed Muhammad Anwar

    2017-12-01

    Full Text Available The success and wider adaptability of smart phones has given a new dimension to the gaming industry. Due to the wide spectrum of video games, the success of a particular game depends on how efficiently it is able to capture the end users’ attention. This leads to the need to analyse the cognitive aspects of the end user, that is the game player, during game play. A direct window to see how an end user responds to a stimuli is to look at their brain activity. In this study, electroencephalography (EEG is used to record human brain activity during game play. A commercially available EEG headset is used for this purpose giving fourteen channels of recorded EEG brain activity. The aim is to classify a player as expert or novice using the brain activity as the player indulges in the game play. Three different machine learning classifiers have been used to train and test the system. Among the classifiers, naive Bayes has outperformed others with an accuracy of 88 % , when data from all fourteen EEG channels are used. Furthermore, the activity observed on electrodes is statistically analysed and mapped for brain visualizations. The analysis has shown that out of the available fourteen channels, only four channels in the frontal and occipital brain regions show significant activity. Features of these four channels are then used, and the performance parameters of the four-channel classification are compared to the results of the fourteen-channel classification. It has been observed that support vector machine and the naive Bayes give good classification accuracy and processing time, well suited for real-time applications.

  13. Mapping abnormal subcortical brain morphometry in an elderly HIV+ cohort.

    Science.gov (United States)

    Wade, Benjamin S C; Valcour, Victor G; Wendelken-Riegelhaupt, Lauren; Esmaeili-Firidouni, Pardis; Joshi, Shantanu H; Gutman, Boris A; Thompson, Paul M

    2015-01-01

    Over 50% of HIV + individuals exhibit neurocognitive impairment and subcortical atrophy, but the profile of brain abnormalities associated with HIV is still poorly understood. Using surface-based shape analyses, we mapped the 3D profile of subcortical morphometry in 63 elderly HIV + participants and 31 uninfected controls. The thalamus, caudate, putamen, pallidum, hippocampus, amygdala, brainstem, accumbens, callosum and ventricles were segmented from high-resolution MRIs. To investigate shape-based morphometry, we analyzed the Jacobian determinant (JD) and radial distances (RD) defined on each region's surfaces. We also investigated effects of nadir CD4 + T-cell counts, viral load, time since diagnosis (TSD) and cognition on subcortical morphology. Lastly, we explored whether HIV + participants were distinguishable from unaffected controls in a machine learning context. All shape and volume features were included in a random forest (RF) model. The model was validated with 2-fold cross-validation. Volumes of HIV + participants' bilateral thalamus, left pallidum, left putamen and callosum were significantly reduced while ventricular spaces were enlarged. Significant shape variation was associated with HIV status, TSD and the Wechsler adult intelligence scale. HIV + people had diffuse atrophy, particularly in the caudate, putamen, hippocampus and thalamus. Unexpectedly, extended TSD was associated with increased thickness of the anterior right pallidum. In the classification of HIV + participants vs. controls, our RF model attained an area under the curve of 72%.

  14. Mapping abnormal subcortical brain morphometry in an elderly HIV+ cohort

    Directory of Open Access Journals (Sweden)

    Benjamin S.C. Wade

    2015-01-01

    Full Text Available Over 50% of HIV+ individuals exhibit neurocognitive impairment and subcortical atrophy, but the profile of brain abnormalities associated with HIV is still poorly understood. Using surface-based shape analyses, we mapped the 3D profile of subcortical morphometry in 63 elderly HIV+ participants and 31 uninfected controls. The thalamus, caudate, putamen, pallidum, hippocampus, amygdala, brainstem, accumbens, callosum and ventricles were segmented from high-resolution MRIs. To investigate shape-based morphometry, we analyzed the Jacobian determinant (JD and radial distances (RD defined on each region's surfaces. We also investigated effects of nadir CD4+ T-cell counts, viral load, time since diagnosis (TSD and cognition on subcortical morphology. Lastly, we explored whether HIV+ participants were distinguishable from unaffected controls in a machine learning context. All shape and volume features were included in a random forest (RF model. The model was validated with 2-fold cross-validation. Volumes of HIV+ participants' bilateral thalamus, left pallidum, left putamen and callosum were significantly reduced while ventricular spaces were enlarged. Significant shape variation was associated with HIV status, TSD and the Wechsler adult intelligence scale. HIV+ people had diffuse atrophy, particularly in the caudate, putamen, hippocampus and thalamus. Unexpectedly, extended TSD was associated with increased thickness of the anterior right pallidum. In the classification of HIV+ participants vs. controls, our RF model attained an area under the curve of 72%.

  15. On seeing the trees and the forest: single-signal and multisignal analysis of periictal intracranial EEG.

    Science.gov (United States)

    Schindler, Kaspar; Gast, Heidemarie; Goodfellow, Marc; Rummel, Christian

    2012-09-01

    Epileptic seizures are associated with a dysregulation of electrical brain activity on many different spatial scales. To better understand the dynamics of epileptic seizures, that is, how the seizures initiate, propagate, and terminate, it is important to consider changes of electrical brain activity on different spatial scales. Herein we set out to analyze periictal electrical brain activity on comparatively small and large spatial scales by assessing changes in single intracranial electroencephalography (EEG) signals and of averaged interdependences of pairs of EEG signals. Single and multiple EEG signals are analyzed by combining methods from symbolic dynamics and information theory. This computationally efficient approach is chosen because at its core it consists of analyzing the occurrence of patterns and bears analogy to classical visual EEG reading. Symbolization is achieved by first mapping the EEG signals into bit strings, that is, long sequences of zeros and ones, depending solely on whether their amplitudes increase or decrease. Bit strings reflect relational aspects between consecutive values of the original EEG signals, but not the values themselves. For each bit string the relative frequencies of the different constituent short bit patterns are then determined and used to compute two information theoretical measures: (1) redundancy (R) of single bit strings characterizes electrical brain activity on a comparatively small spatial scale represented by a single EEG signal and (2) averaged pair-wise mutual information with all other bit strings (M), which allows tracking of larger-scale EEG dynamics. We analyzed 20 periictal intracranial EEG recordings from five patients with pharmacoresistant temporal lobe epilepsy. At seizure onset, R first strongly increased and then decreased toward seizure termination, whereas M gradually increased throughout the seizure. Bit strings with maximal R were always derived from EEG signals recorded from the visually

  16. Presentation of 60 Cases of Infantile Spasms Based on Etiology, Clinical Manifestation EEG and Brain CT Scan in Mofid Children Hospital

    Directory of Open Access Journals (Sweden)

    Mohammad Mehdi Taghdiri

    2002-06-01

    Full Text Available Objective: Among different epileptic syndrome infantile spasm is one of the most malignant forms which cause irrepairable brain damage in the child. Consequently the longer this type of epilepsy lasts the more harmful results will follow. The majority of children with infantile spasm are younger than one year age and only 5 percent of affected children are in the age group above one year. Materials & Methods: This descriptive study was done on 60 (36 male and 24 female infants 2-24 months age with clinical examination, observation, interview and questionnaire  in pediatric neurology department of Mofid children hospital during two years. Results: From 60 patients (36 male and 24 female, 48 case (80% symptomatic and 12 case (20% cryptogenic and idiopathic. Based on clinical manifestation 35 case (58% were flexor type. 6 case (10% extensor and 19 cases (32% mixed. In EEG hypsarrhythmia in all patients was seen. Brain CT scan in 11 cases showed brain atrophy and in remainder was normal. Conclusion: In our study etiologically symptomatic and clinically flexor type was more common. Hysparrhythmia in all patients was seen and brain CT scan in 80% of patients was normal.

  17. EEG alpha power as an intermediate measure between brain-derived neurotrophic factor Val66Met and depression severity in patients with major depressive disorder.

    Science.gov (United States)

    Zoon, Harriët F A; Veth, C P M; Arns, Martijn; Drinkenburg, W H I M; Talloen, Willem; Peeters, Pieter J; Kenemans, J L

    2013-06-01

    Major depressive disorder has a large impact on patients and society and is projected to be the second greatest global burden of disease by 2020. The brain-derived neurotrophic factor (BDNF) gene is considered to be one of the important factors in the etiology of major depressive disorder. In a recent study, alpha power was found to mediate between BDNF Met and subclinical depressed mood. The current study looked at a population of patients with major depressive disorder (N = 107) to examine the association between the BDNF Val66Met polymorphism, resting state EEG alpha power, and depression severity. For this purpose, repeated-measures analysis of variance, partial correlation, and multiple linear models were used. Results indicated a negative association between parietal-occipital alpha power in the eyes open resting state and depression severity. In addition, Met/Met patients showed lower global absolute alpha power in the eyes closed condition compared with Val-carriers. These findings are in accordance with the previously uncovered pathway between BDNF Val66Met, resting state EEG alpha power, and depression severity. Additional research is needed for the clarification of this tentative pathway and its implication in personalized treatment of major depressive disorder.

  18. EEG analyses with SOBI.

    Energy Technology Data Exchange (ETDEWEB)

    Glickman, Matthew R.; Tang, Akaysha (University of New Mexico, Albuquerque, NM)

    2009-02-01

    The motivating vision behind Sandia's MENTOR/PAL LDRD project has been that of systems which use real-time psychophysiological data to support and enhance human performance, both individually and of groups. Relevant and significant psychophysiological data being a necessary prerequisite to such systems, this LDRD has focused on identifying and refining such signals. The project has focused in particular on EEG (electroencephalogram) data as a promising candidate signal because it (potentially) provides a broad window on brain activity with relatively low cost and logistical constraints. We report here on two analyses performed on EEG data collected in this project using the SOBI (Second Order Blind Identification) algorithm to identify two independent sources of brain activity: one in the frontal lobe and one in the occipital. The first study looks at directional influences between the two components, while the second study looks at inferring gender based upon the frontal component.

  19. Integration of EEG source imaging and fMRI during continuous viewing of natural movies.

    Science.gov (United States)

    Whittingstall, Kevin; Bartels, Andreas; Singh, Vanessa; Kwon, Soyoung; Logothetis, Nikos K

    2010-10-01

    estimate of the current density at every time point. We then carried out a correlation between the time series of visual contrast changes in the movie with that of EEG voxels. We found the most significant correlations in visual area V1, just as seen in previous fMRI studies (Bartels A, Zeki, S, Logothetis NK. Natural vision reveals regional specialization to local motion and to contrast-invariant, global flow in the human brain. Cereb Cortex 2008;18(3):705-717), but on the time scale of milliseconds rather than of seconds. To obtain an estimate of how the EEG signal relates to the BOLD signal, we calculated the IRF between the BOLD signal and the estimated current density in area V1. We found that this IRF was very similar to that observed using combined intracortical recordings and fMRI experiments in nonhuman primates. Taken together, these findings open a new approach to noninvasive mapping of the brain. It allows, firstly, the localization of feature-selective brain areas during natural viewing conditions with the temporal resolution of EEG. Secondly, it provides a tool to assess EEG/BOLD transfer functions during processing of more natural stimuli. This is especially useful in combined EEG/fMRI experiments, where one can now potentially study neural-hemodynamic relationships across the whole brain volume in a noninvasive manner. Copyright © 2010 Elsevier Inc. All rights reserved.

  20. Variation in anticonvulsant selection and EEG monitoring following severe traumatic brain injury in children – Understanding resource availability in sites participating in a comparative effectiveness study

    Science.gov (United States)

    Kurz, Jonathan E.; Poloyac, Samuel M.; Abend, Nicholas S.; Fabio, Anthony; Bell, Michael J.; Wainwright, Mark S.

    2016-01-01

    Objective Early post-traumatic seizures (PTS) may contribute to worsened outcomes after traumatic brain injury (TBI). Evidence to guide the evaluation and management of early PTS in children is limited. We undertook a survey of current practices of continuous electroencephalographic monitoring (cEEG), seizure prophylaxis and the management of early PTS to provide essential information for trial design and the development of PTS management pathways. Design Surveys were sent to site principal investigators at all 43 sites participating in the ADAPT (Approaches and Decisions in Acute Pediatric TBI) trial at the time of the survey. Surveys consisted of 12 questions addressing strategies to (i) implement cEEG monitoring, (ii) PTS prophylaxis, (iii) treat acute PTS, (iv) treat status epilepticus (SE) and refractory status epilepticus (RSE) and (v) monitor anti-seizure drug levels. Setting Institutions comprised a mixture of free-standing children’s hospitals and university medical centers across the United States and Europe. Measurements and Main Results cEEG monitoring was available in the pediatric intensive care unit in the overwhelming majority of clinical sites (98%); however, the plans to operationalize such monitoring for children varied considerably. A similar majority of sites report that administration of prophylactic anti-seizure medications is anticipated in children (93%), yet a minority reports that a specified protocol for treatment of PTS is in place (43%). Reported medication choices varied substantially between sites, but the majority of sites reported pentobarbital for RSE (81%). Presence of an treatment protocols for seizure prophylaxis, early PTS, post-traumatic SE and RSE was associated with decreased reported medications (all p pediatric severe TBI. The substantial variation in cEEG implementation, choice of seizure prophylaxis medications, and management of early PTS across institutions was reported, signifying areas of clinical uncertainty that

  1. Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG

    Science.gov (United States)

    Bleichner, Martin G.; Debener, Stefan

    2017-01-01

    Electroencephalography (EEG) is an important clinical tool and frequently used to study the brain-behavior relationship in humans noninvasively. Traditionally, EEG signals are recorded by positioning electrodes on the scalp and keeping them in place with glue, rubber bands, or elastic caps. This setup provides good coverage of the head, but is impractical for EEG acquisition in natural daily-life situations. Here, we propose the transparent EEG concept. Transparent EEG aims for motion tolerant, highly portable, unobtrusive, and near invisible data acquisition with minimum disturbance of a user's daily activities. In recent years several ear-centered EEG solutions that are compatible with the transparent EEG concept have been presented. We discuss work showing that miniature electrodes placed in and around the human ear are a feasible solution, as they are sensitive enough to pick up electrical signals stemming from various brain and non-brain sources. We also describe the cEEGrid flex-printed sensor array, which enables unobtrusive multi-channel EEG acquisition from around the ear. In a number of validation studies we found that the cEEGrid enables the recording of meaningful continuous EEG, event-related potentials and neural oscillations. Here, we explain the rationale underlying the cEEGrid ear-EEG solution, present possible use cases and identify open issues that need to be solved on the way toward transparent EEG. PMID:28439233

  2. Independent component analysis of gait-related movement artifact recorded using EEG electrodes during treadmill walking.

    Directory of Open Access Journals (Sweden)

    Kristine Lynne Snyder

    2015-12-01

    Full Text Available There has been a recent surge in the use of electroencephalography (EEG as a tool for mobile brain imaging due to its portability and fine time resolution. When EEG is combined with independent component analysis (ICA and source localization techniques, it can model electrocortical activity as arising from temporally independent signals located in spatially distinct cortical areas. However, for mobile tasks, it is not clear how movement artifacts influence ICA and source localization. We devised a novel method to collect pure movement artifact data (devoid of any electrophysiological signals with a 256-channel EEG system. We first blocked true electrocortical activity using a silicone swim cap. Over the silicone layer, we placed a simulated scalp with electrical properties similar to real human scalp. We collected EEG movement artifact signals from ten healthy, young subjects wearing this setup as they walked on a treadmill at speeds from 0.4-1.6 m/s. We performed ICA and dipole fitting on the EEG movement artifact data to quantify how accurately these methods would identify the artifact signals as non-neural. ICA and dipole fitting accurately localized 99% of the independent components in non-neural locations or lacked dipolar characteristics. The remaining 1% of sources had locations within the brain volume and low residual variances, but had topographical maps, power spectra, time courses, and event related spectral perturbations typical of non-neural sources. Caution should be exercised when interpreting ICA for data that includes semi-periodic artifacts including artifact arising from human walking. Alternative methods are needed for the identification and separation of movement artifact in mobile EEG signals, especially methods that can be performed in real time. Separating true brain signals from motion artifact could clear the way for EEG brain computer interfaces for assistance during mobile activities, such as walking.

  3. Preterm EEG: a multimodal neurophysiological protocol.

    Science.gov (United States)

    Stjerna, Susanna; Voipio, Juha; Metsäranta, Marjo; Kaila, Kai; Vanhatalo, Sampsa

    2012-02-18

    Since its introduction in early 1950s, electroencephalography (EEG) has been widely used in the neonatal intensive care units (NICU) for assessment and monitoring of brain function in preterm and term babies. Most common indications are the diagnosis of epileptic seizures, assessment of brain maturity, and recovery from hypoxic-ischemic events. EEG recording techniques and the understanding of neonatal EEG signals have dramatically improved, but these advances have been slow to penetrate through the clinical traditions. The aim of this presentation is to bring theory and practice of advanced EEG recording available for neonatal units. In the theoretical part, we will present animations to illustrate how a preterm brain gives rise to spontaneous and evoked EEG activities, both of which are unique to this developmental phase, as well as crucial for a proper brain maturation. Recent animal work has shown that the structural brain development is clearly reflected in early EEG activity. Most important structures in this regard are the growing long range connections and the transient cortical structure, subplate. Sensory stimuli in a preterm baby will generate responses that are seen at a single trial level, and they have underpinnings in the subplate-cortex interaction. This brings neonatal EEG readily into a multimodal study, where EEG is not only recording cortical function, but it also tests subplate function via different sensory modalities. Finally, introduction of clinically suitable dense array EEG caps, as well as amplifiers capable of recording low frequencies, have disclosed multitude of brain activities that have as yet been overlooked. In the practical part of this video, we show how a multimodal, dense array EEG study is performed in neonatal intensive care unit from a preterm baby in the incubator. The video demonstrates preparation of the baby and incubator, application of the EEG cap, and performance of the sensory stimulations.

  4. Quantification of brain images using Korean standard templates and structural and cytoarchitectonic probabilistic maps

    International Nuclear Information System (INIS)

    Lee, Jae Sung; Lee, Dong Soo; Kim, Yu Kyeong

    2004-01-01

    Population based structural and functional maps of the brain provide effective tools for the analysis and interpretation of complex and individually variable brain data. Brain MRI and PET standard templates and statistical probabilistic maps based on image data of Korean normal volunteers have been developed and probabilistic maps based on cytoarchitectonic data have been introduced. A quantification method using these data was developed for the objective assessment of regional intensity in the brain images. Age, gender and ethnic specific anatomical and functional brain templates based on MR and PET images of Korean normal volunteers were developed. Korean structural probabilistic maps for 89 brain regions and cytoarchitectonic probabilistic maps for 13 Brodmann areas were transformed onto the standard templates. Brain FDG PET and SPGR MR images of normal volunteers were spatially normalized onto the template of each modality and gender. Regional uptake of radiotracers in PET and gray matter concentration in MR images were then quantified by averaging (or summing) regional intensities weighted using the probabilistic maps of brain regions. Regionally specific effects of aging on glucose metabolism in cingulate cortex were also examined. Quantification program could generate quantification results for single spatially normalized images per 20 seconds. Glucose metabolism change in cingulate gyrus was regionally specific: ratios of glucose metabolism in the rostral anterior cingulate vs. posterior cingulate and the caudal anterior cingulate vs. posterior cingulate were significantly decreased as the age increased. 'Rostral anterior' / 'posterior' was decreased by 3.1% per decade of age (p -11 , r=0.81) and 'caudal anterior' / 'posterior' was decreased by 1.7% (p -8 , r=0.72). Ethnic specific standard templates and probabilistic maps and quantification program developed in this study will be useful for the analysis of brain image of Korean people since the difference

  5. Deep learning with convolutional neural networks for EEG decoding and visualization.

    Science.gov (United States)

    Schirrmeister, Robin Tibor; Springenberg, Jost Tobias; Fiederer, Lukas Dominique Josef; Glasstetter, Martin; Eggensperger, Katharina; Tangermann, Michael; Hutter, Frank; Burgard, Wolfram; Ball, Tonio

    2017-11-01

    Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping. Hum Brain Mapp 38:5391-5420, 2017. © 2017 Wiley Periodicals, Inc. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  6. Mapping plasticity: sex/gender and the changing brain

    NARCIS (Netherlands)

    Kleinherenbrink, A.

    2014-01-01

    There is a consensus in the neuroscientific literature that brains are either male or female, and that ‘brain sex’ is a fixed, immutable trait. Feminist critics have challenged this idea, raising questions, for example, about brain plasticity (the role of sociocultural factors in the emergence and

  7. Expanding the (kaleido)scope: exploring current literature trends for translating electroencephalography (EEG) based brain-computer interfaces for motor rehabilitation in children

    Science.gov (United States)

    Kinney-Lang, E.; Auyeung, B.; Escudero, J.

    2016-12-01

    Rehabilitation applications using brain-computer interfaces (BCI) have recently shown encouraging results for motor recovery. Effective BCI neurorehabilitation has been shown to exploit neuroplastic properties of the brain through mental imagery tasks. However, these applications and results are currently restricted to adults. A systematic search reveals there is essentially no literature describing motor rehabilitative BCI applications that use electroencephalograms (EEG) in children, despite advances in such applications with adults. Further inspection highlights limited literature pursuing research in the field, especially outside of neurofeedback paradigms. Then the question naturally arises, do current literature trends indicate that EEG based BCI motor rehabilitation applications could be translated to children? To provide further evidence beyond the available literature for this particular topic, we present an exploratory survey examining some of the indirect literature related to motor rehabilitation BCI in children. Our goal is to establish if evidence in the related literature supports research on this topic and if the related studies can help explain the dearth of current research in this area. The investigation found positive literature trends in the indirect studies which support translating these BCI applications to children and provide insight into potential pitfalls perhaps responsible for the limited literature. Careful consideration of these pitfalls in conjunction with support from the literature emphasize that fully realized motor rehabilitation BCI applications for children are feasible and would be beneficial. • BCI intervention has improved motor recovery in adult patients and offer supplementary rehabilitation options to patients. • A systematic literature search revealed that essentially no research has been conducted bringing motor rehabilitation BCI applications to children, despite advances in BCI. • Indirect studies discovered

  8. Expanding the (kaleido)scope: exploring current literature trends for translating electroencephalography (EEG) based brain-computer interfaces for motor rehabilitation in children.

    Science.gov (United States)

    Kinney-Lang, E; Auyeung, B; Escudero, J

    2016-12-01

    Rehabilitation applications using brain-computer interfaces (BCI) have recently shown encouraging results for motor recovery. Effective BCI neurorehabilitation has been shown to exploit neuroplastic properties of the brain through mental imagery tasks. However, these applications and results are currently restricted to adults. A systematic search reveals there is essentially no literature describing motor rehabilitative BCI applications that use electroencephalograms (EEG) in children, despite advances in such applications with adults. Further inspection highlights limited literature pursuing research in the field, especially outside of neurofeedback paradigms. Then the question naturally arises, do current literature trends indicate that EEG based BCI motor rehabilitation applications could be translated to children? To provide further evidence beyond the available literature for this particular topic, we present an exploratory survey examining some of the indirect literature related to motor rehabilitation BCI in children. Our goal is to establish if evidence in the related literature supports research on this topic and if the related studies can help explain the dearth of current research in this area. The investigation found positive literature trends in the indirect studies which support translating these BCI applications to children and provide insight into potential pitfalls perhaps responsible for the limited literature. Careful consideration of these pitfalls in conjunction with support from the literature emphasize that fully realized motor rehabilitation BCI applications for children are feasible and would be beneficial. •  BCI intervention has improved motor recovery in adult patients and offer supplementary rehabilitation options to patients. •  A systematic literature search revealed that essentially no research has been conducted bringing motor rehabilitation BCI applications to children, despite advances in BCI. •  Indirect studies

  9. Gender effect in human brain responses to bottom-up and top-down attention using the EEG 3D-Vector Field Tomography.

    Science.gov (United States)

    Kosmidou, Vasiliki E; Adam, Aikaterini; Papadaniil, Chrysa D; Tsolaki, Magda; Hadjileontiadis, Leontios J; Kompatsiaris, Ioannis

    2015-01-01

    The effect of gender in rapidly allocating attention to objects, features or locations, as reflected in brain activity, is examined in this study. A visual-attention task, consisting of bottom-up (visual pop-out) and top-down (visual search) conditions during stimuli of four triangles, i.e., a target and three distractors, was engaged. In pop-out condition, both color and orientation of the distractors differed from target, while in search condition they differed only in orientation. During the task, high-density EEG (256 channels) data were recorded and analyzed by means of behavioral, event-related potentials, i.e., the P300 component and brain source localization analysis using 3D-Vector Field Tomography (3D-VFT). Twenty subjects (half female; 32±4.7 years old) participated in the experiments, performing 60 trials for each condition. Behavioral analysis revealed that both female and male outperformed in the pop-out condition compared to the search one, with respect to accuracy and reaction time, whereas no gender-related statistical significant differences were found. Nevertheless, in the search condition, higher P300 amplitudes were detected for females compared to males (p left inferior frontal and superior temporal gyri, whereas in males it was found in the right inferior frontal and superior temporal gyri. Overall, the experimental results show that visual attention depends on contributions from different brain lateralization linked to gender, posing important implications in studying developmental disorders, characterized by gender differences.

  10. Early Prefrontal Brain Responses to the Hedonic Quality of Emotional Words – A Simultaneous EEG and MEG Study

    Science.gov (United States)

    Keuper, Kati; Zwitserlood, Pienie; Rehbein, Maimu A.; Eden, Annuschka S.; Laeger, Inga; Junghöfer, Markus; Zwanzger, Peter; Dobel, Christian

    2013-01-01

    The hedonic meaning of words affects word recognition, as shown by behavioral, functional imaging, and event-related potential (ERP) studies. However, the spatiotemporal dynamics and cognitive functions behind are elusive, partly due to methodological limitations of previous studies. Here, we account for these difficulties by computing combined electro-magnetoencephalographic (EEG/MEG) source localization techniques. Participants covertly read emotionally high-arousing positive and negative nouns, while EEG and MEG were recorded simultaneously. Combined EEG/MEG current-density reconstructions for the P1 (80–120 ms), P2 (150–190 ms) and EPN component (200–300 ms) were computed using realistic individual head models, with a cortical constraint. Relative to negative words, the P1 to positive words predominantly involved language-related structures (left middle temporal and inferior frontal regions), and posterior structures related to directed attention (occipital and parietal regions). Effects shifted to the right hemisphere in the P2 component. By contrast, negative words received more activation in the P1 time-range only, recruiting prefrontal regions, including the anterior cingulate cortex (ACC). Effects in the EPN were not statistically significant. These findings show that different neuronal networks are active when positive versus negative words are processed. We account for these effects in terms of an “emotional tagging” of word forms during language acquisition. These tags then give rise to different processing strategies, including enhanced lexical processing of positive words and a very fast language-independent alert response to negative words. The valence-specific recruitment of different networks might underlie fast adaptive responses to both approach- and withdrawal-related stimuli, be they acquired or biological. PMID:23940642

  11. Early prefrontal brain responses to the Hedonic quality of emotional words--a simultaneous EEG and MEG study.

    Science.gov (United States)

    Keuper, Kati; Zwitserlood, Pienie; Rehbein, Maimu A; Eden, Annuschka S; Laeger, Inga; Junghöfer, Markus; Zwanzger, Peter; Dobel, Christian

    2013-01-01

    The hedonic meaning of words affects word recognition, as shown by behavioral, functional imaging, and event-related potential (ERP) studies. However, the spatiotemporal dynamics and cognitive functions behind are elusive, partly due to methodological limitations of previous studies. Here, we account for these difficulties by computing combined electro-magnetoencephalographic (EEG/MEG) source localization techniques. Participants covertly read emotionally high-arousing positive and negative nouns, while EEG and MEG were recorded simultaneously. Combined EEG/MEG current-density reconstructions for the P1 (80-120 ms), P2 (150-190 ms) and EPN component (200-300 ms) were computed using realistic individual head models, with a cortical constraint. Relative to negative words, the P1 to positive words predominantly involved language-related structures (left middle temporal and inferior frontal regions), and posterior structures related to directed attention (occipital and parietal regions). Effects shifted to the right hemisphere in the P2 component. By contrast, negative words received more activation in the P1 time-range only, recruiting prefrontal regions, including the anterior cingulate cortex (ACC). Effects in the EPN were not statistically significant. These findings show that different neuronal networks are active when positive versus negative words are processed. We account for these effects in terms of an "emotional tagging" of word forms during language acquisition. These tags then give rise to different processing strategies, including enhanced lexical processing of positive words and a very fast language-independent alert response to negative words. The valence-specific recruitment of different networks might underlie fast adaptive responses to both approach- and withdrawal-related stimuli, be they acquired or biological.

  12. Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment.

    Science.gov (United States)

    Capecci, Elisa; Kasabov, Nikola; Wang, Grace Y

    2015-08-01

    The paper presents a methodology for the analysis of functional changes in brain activity across different conditions and different groups of subjects. This analysis is based on the recently proposed NeuCube spiking neural network (SNN) framework and more specifically on the analysis of the connectivity of a NeuCube model trained with electroencephalography (EEG) data. The case study data used to illustrate this method is EEG data collected from three groups-subjects with opiate addiction, patients undertaking methadone maintenance treatment, and non-drug users/healthy control group. The proposed method classifies more accurately the EEG data than traditional statistical and artificial intelligence (AI) methods and can be used to predict response to treatment and dose-related drug effect. But more importantly, the method can be used to compare functional brain activities of different subjects and the changes of these activities as a result of treatment, which is a step towards a better understanding of both the EEG data and the brain processes that generated it. The method can also be used for a wide range of applications, such as a better understanding of disease progression or aging. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. Mapping of brain activity by automated volume analysis of immediate early genes

    Science.gov (United States)

    Renier, Nicolas; Adams, Eliza L.; Kirst, Christoph; Wu, Zhuhao; Azevedo, Ricardo; Kohl, Johannes; Autry, Anita E.; Kadiri, Lolahon; Venkataraju, Kannan Umadevi; Zhou, Yu; Wang, Victoria X.; Tang, Cheuk Y.; Olsen, Olav; Dulac, Catherine; Osten, Pavel; Tessier-Lavigne, Marc

    2016-01-01

    Summary Understanding how neural information is processed in physiological and pathological states would benefit from precise detection, localization and quantification of the activity of all neurons across the entire brain, which has not to date been achieved in the mammalian brain. We introduce a pipeline for high speed acquisition of brain activity at cellular resolution through profiling immediate early gene expression using immunostaining and light-sheet fluorescence imaging, followed by automated mapping and analysis of activity by an open-source software program we term ClearMap. We validate the pipeline first by analysis of brain regions activated in response to Haloperidol. Next, we report new cortical regions downstream of whisker-evoked sensory processing during active exploration. Lastly, we combine activity mapping with axon tracing to uncover new brain regions differentially activated during parenting behavior. This pipeline is widely applicable to different experimental paradigms, including animal species for which transgenic activity reporters are not readily available. PMID:27238021

  14. Single-trial EEG-informed fMRI analysis of emotional decision problems in hot executive function.

    Science.gov (United States)

    Guo, Qian; Zhou, Tiantong; Li, Wenjie; Dong, Li; Wang, Suhong; Zou, Ling

    2017-07-01

    Executive function refers to conscious control in psychological process which relates to thinking and action. Emotional decision is a part of hot executive function and contains emotion and logic elements. As a kind of important social adaptation ability, more and more attention has been paid in recent years. Gambling task can be well performed in the study of emotional decision. As fMRI researches focused on gambling task show not completely consistent brain activation regions, this study adopted EEG-fMRI fusion technology to reveal brain neural activity related with feedback stimuli. In this study, an EEG-informed fMRI analysis was applied to process simultaneous EEG-fMRI data. First, relative power-spectrum analysis and K-means clustering method were performed separately to extract EEG-fMRI features. Then, Generalized linear models were structured using fMRI data and using different EEG features as regressors. The results showed that in the win versus loss stimuli, the activated regions almost covered the caudate, the ventral striatum (VS), the orbital frontal cortex (OFC), and the cingulate. Wide activation areas associated with reward and punishment were revealed by the EEG-fMRI integration analysis than the conventional fMRI results, such as the posterior cingulate and the OFC. The VS and the medial prefrontal cortex (mPFC) were found when EEG power features were performed as regressors of GLM compared with results entering the amplitudes of feedback-related negativity (FRN) as regressors. Furthermore, the brain region activation intensity was the strongest when theta-band power was used as a regressor compared with the other two fusion results. The EEG-based fMRI analysis can more accurately depict the whole-brain activation map and analyze emotional decision problems.

  15. EEG in connection with coma.

    Science.gov (United States)

    Wilson, John A; Nordal, Helge J

    2013-01-08

    Coma is a dynamic condition that may have various causes. Important changes may take place rapidly, often with consequences for treatment. The purpose of this article is to provide a brief overview of EEG patterns in comas with various causes, and indicate how EEG contributes in an assessment of the prognosis for coma patients. The article is based on many years of clinical and research-based experience of EEG used for patients in coma. A self-built reference database was supplemented by searches for relevant articles in PubMed. EEG reveals immediate changes in coma, and can provide early information on cause and prognosis. It is the only diagnostic tool for detecting a non-convulsive epileptic status. Locked-in- syndrome may be overseen without EEG. Repeated EEG scans increase diagnostic certainty and make it possible to monitor the development of coma. EEG reflects brain function continuously and therefore holds a key place in the assessment and treatment of coma.

  16. Zebrafish brain mapping--standardized spaces, length scales, and the power of N and n.

    Science.gov (United States)

    Hunter, Paul R; Hendry, Aenea C; Lowe, Andrew S

    2015-06-01

    Mapping anatomical and functional parameters of the zebrafish brain is moving apace. Research communities undertaking such studies are becoming ever larger and more diverse. The unique features, tools, and technologies associated with zebrafish are propelling them as the 21st century model organism for brain mapping. Uniquely positioned as a vertebrate model system, the zebrafish enables imaging of anatomy and function at different length scales from intraneuronal compartments to sparsely distributed whole brain patterns. With a variety of diverse and established statistical modeling and analytic methods available from the wider brain mapping communities, the richness of zebrafish neuroimaging data is being realized. The statistical power of population observations (N) within and across many samples (n) projected onto a standardized space will provide vast databases for data-driven biological approaches. This article reviews key brain mapping initiatives at different levels of scale that highlight the potential of zebrafish brain mapping. By way of introduction to the next wave of brain mappers, an accessible introduction to the key concepts and caveats associated with neuroimaging are outlined and discussed. © 2014 Wiley Periodicals, Inc.

  17. Mapping Language Function in the Brain: A Review of the Recent Literature.

    Science.gov (United States)

    Crafton, Robert E.; Kido, Elissa

    2000-01-01

    Considers the potential importance of brain study for composition instruction, briefly describes functional imaging techniques, and reviews the findings of recent brain-mapping studies investigating the neurocognitive systems involved in language function. Presents a review of the recent literature and considers the possible implications of this…

  18. Differences in Information Mapping Strategies in Left and Right Brain Learners.

    Science.gov (United States)

    Hauck, LaVerne S., Jr.

    The Information Mapping technique was used to present a learning packet, and its usefulness in helping right-brain cerebrally dominant students to achieve the same level of subject mastery as their left-brain counterparts was examined. Reading level, grade point average, and gender were also analyzed. Torrance's "Your Style of Learning and…

  19. Mapping how local perturbations influence systems-level brain dynamics.

    Science.gov (United States)

    Gollo, Leonardo L; Roberts, James A; Cocchi, Luca

    2017-10-15

    The human brain exhibits a distinct spatiotemporal organization that supports brain function and can be manipulated via local brain stimulation. Such perturbations to local cortical dynamics are globally integrated by distinct neural systems. However, it remains unclear how local changes in neural activity affect large-scale system dynamics. Here, we briefly review empirical and computational studies addressing how localized perturbations affect brain activity. We then systematically analyze a model of large-scale brain dynamics, assessing how localized changes in brain activity at the different sites affect whole-brain dynamics. We find that local stimulation induces changes in brain activity that can be summarized by relatively smooth tuning curves, which relate a region's effectiveness as a stimulation site to its position within the cortical hierarchy. Our results also support the notion that brain hubs, operating in a slower regime, are more resilient to focal perturbations and critically contribute to maintain stability in global brain dynamics. In contrast, perturbations of peripheral regions, characterized by faster activity, have greater impact on functional connectivity. As a parallel with this region-level result, we also find that peripheral systems such as the visual and sensorimotor networks were more affected by local perturbations than high-level systems such as the cingulo-opercular network. Our findings highlight the importance of a periphery-to-core hierarchy to determine the effect of local stimulation on the brain network. This study also provides novel resources to orient empirical work aiming at manipulating functional connectivity using non-invasive brain stimulation. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Quantitative susceptibility mapping of human brain at 3T: a multisite reproducibility study.

    Science.gov (United States)

    Lin, P-Y; Chao, T-C; Wu, M-L

    2015-03-01

    Quantitative susceptibility mapping of the human brain has demonstrated strong potential in examining iron deposition, which may help in investigating possible brain pathology. This study assesses the reproducibility of quantitative susceptibility mapping across different imaging sites. In this study, the susceptibility values of 5 regions of interest in the human brain were measured on 9 healthy subjects following calibration by using phantom experiments. Each of the subjects was imaged 5 times on 1 scanner with the same procedure repeated on 3 different 3T systems so that both within-site and cross-site quantitative susceptibility mapping precision levels could be assessed. Two quantitative susceptibility mapping algorithms, similar in principle, one by using iterative regularization (iterative quantitative susceptibility mapping) and the other with analytic optimal solutions (deterministic quantitative susceptibility mapping), were implemented, and their performances were compared. Results show that while deterministic quantitative susceptibility mapping had nearly 700 times faster computation speed, residual streaking artifacts seem to be more prominent compared with iterative quantitative susceptibility mapping. With quantitative susceptibility mapping, the putamen, globus pallidus, and caudate nucleus showed smaller imprecision on the order of 0.005 ppm, whereas the red nucleus and substantia nigra, closer to the skull base, had a somewhat larger imprecision of approximately 0.01 ppm. Cross-site errors were not significantly larger than within-site errors. Possible sources of estimation errors are discussed. The reproducibility of quantitative susceptibility mapping in the human brain in vivo is regionally dependent, and the precision levels achieved with quantitative susceptibility mapping should allow longitudinal and multisite studies such as aging-related changes in brain tissue magnetic susceptibility. © 2015 by American Journal of Neuroradiology.

  1. Tactile exploration of virtual objects for blind and sighted people: the role of beta 1 EEG band in sensory substitution and supramodal mental mapping.

    Science.gov (United States)

    Campus, C; Brayda, L; De Carli, F; Chellali, R; Famà, F; Bruzzo, C; Lucagrossi, L; Rodriguez, G

    2012-05-01

    The neural correlates of exploration and cognitive mapping in blindness remain elusive. The role of visuo-spatial pathways in blind vs. sighted subjects is still under debate. In this preliminary study, we investigate, as a possible estimation of the activity in the visuo-spatial pathways, the EEG patterns of blind and blindfolded-sighted subjects during the active tactile construction of cognitive maps from virtual objects compared with rest and passive tactile stimulation. Ten blind and ten matched, blindfolded-sighted subjects participated in the study. Events were defined as moments when the finger was only stimulated (passive stimulation) or the contour of a virtual object was touched (during active exploration). Event-related spectral power and coherence perturbations were evaluated within the beta 1 band (14-18 Hz). They were then related to a subjective cognitive-load estimation required by the explorations [namely, perceived levels of difficulty (PLD)]. We found complementary cues for sensory substitution and spatial processing in both groups: both blind and sighted subjects showed, while exploring, late power decreases and early power increases, potentially associated with motor programming and touch, respectively. The latter involved occipital areas only for blind subjects (long-term plasticity) and only during active exploration, thus supporting tactile-to-visual sensory substitution. In both groups, coherences emerged among the fronto-central, centro-parietal, and occipito-temporal derivations associated with visuo-spatial processing. This seems in accordance with mental map construction involving spatial processing, sensory-motor processing, and working memory. The observed involvement of the occipital regions suggests that a substitution process also occurs in sighted subjects. Only during explorations did coherence correlate positively with PLD for both groups and in derivations, which can be related to visuo-spatial processing, supporting the

  2. Tactile exploration of virtual objects for blind and sighted people: the role of beta 1 EEG band in sensory substitution and supramodal mental mapping

    Science.gov (United States)

    Brayda, L.; De Carli, F.; Chellali, R.; Famà, F.; Bruzzo, C.; Lucagrossi, L.; Rodriguez, G.

    2012-01-01

    The neural correlates of exploration and cognitive mapping in blindness remain elusive. The role of visuo-spatial pathways in blind vs. sighted subjects is still under debate. In this preliminary study, we investigate, as a possible estimation of the activity in the visuo-spatial pathways, the EEG patterns of blind and blindfolded-sighted subjects during the active tactile construction of cognitive maps from virtual objects compared with rest and passive tactile stimulation. Ten blind and ten matched, blindfolded-sighted subjects participated in the study. Events were defined as moments when the finger was only stimulated (passive stimulation) or the contour of a virtual object was touched (during active exploration). Event-related spectral power and coherence perturbations were evaluated within the beta 1 band (14–18 Hz). They were then related to a subjective cognitive-load estimation required by the explorations [namely, perceived levels of difficulty (PLD)]. We found complementary cues for sensory substitution and spatial processing in both groups: both blind and sighted subjects showed, while exploring, late power decreases and early power increases, potentially associated with motor programming and touch, respectively. The latter involved occipital areas only for blind subjects (long-term plasticity) and only during active exploration, thus supporting tactile-to-visual sensory substitution. In both groups, coherences emerged among the fronto-central, centro-parietal, and occipito-temporal derivations associated with visuo-spatial processing. This seems in accordance with mental map construction involving spatial processing, sensory-motor processing, and working memory. The observed involvement of the occipital regions suggests that a substitution process also occurs in sighted subjects. Only during explorations did coherence correlate positively with PLD for both groups and in derivations, which can be related to visuo-spatial processing, supporting the

  3. Deciphering the genomic architecture of the stickleback brain with a novel multilocus gene-mapping approach.

    Science.gov (United States)

    Li, Zitong; Guo, Baocheng; Yang, Jing; Herczeg, Gábor; Gonda, Abigél; Balázs, Gergely; Shikano, Takahito; Calboli, Federico C F; Merilä, Juha

    2017-03-01

    Quantitative traits important to organismal function and fitness, such as brain size, are presumably controlled by many small-effect loci. Deciphering the genetic architecture of such traits with traditional quantitative trait locus (QTL) mapping methods is challenging. Here, we investigated the genetic architecture of brain size (and the size of five different brain parts) in nine-spined sticklebacks (Pungitius pungitius) with the aid of novel multilocus QTL-mapping approaches based on a de-biased LASSO method. Apart from having more statistical power to detect QTL and reduced rate of false positives than conventional QTL-mapping approaches, the developed methods can handle large marker panels and provide estimates of genomic heritability. Single-locus analyses of an F 2 interpopulation cross with 239 individuals and 15 198, fully informative single nucleotide polymorphisms (SNPs) uncovered 79 QTL associated with variation in stickleback brain size traits. Many of these loci were in strong linkage disequilibrium (LD) with each other, and consequently, a multilocus mapping of individual SNPs, accounting for LD structure in the data, recovered only four significant QTL. However, a multilocus mapping of SNPs grouped by linkage group (LG) identified 14 LGs (1-6 depending on the trait) that influence variation in brain traits. For instance, 17.6% of the variation in relative brain size was explainable by cumulative effects of SNPs distributed over six LGs, whereas 42% of the variation was accounted for by all 21 LGs. Hence, the results suggest that variation in stickleback brain traits is influenced by many small-effect loci. Apart from suggesting moderately heritable (h 2  ≈ 0.15-0.42) multifactorial genetic architecture of brain traits, the results highlight the challenges in identifying the loci contributing to variation in quantitative traits. Nevertheless, the results demonstrate that the novel QTL-mapping approach developed here has distinctive advantages

  4. Scale-free music of the brain.

    Directory of Open Access Journals (Sweden)

    Dan Wu

    Full Text Available BACKGROUND: There is growing interest in the relation between the brain and music. The appealing similarity between brainwaves and the rhythms of music has motivated many scientists to seek a connection between them. A variety of transferring rules has been utilized to convert the brainwaves into music; and most of them are mainly based on spectra feature of EEG. METHODOLOGY/PRINCIPAL FINDINGS: In this study, audibly recognizable scale-free music was deduced from individual Electroencephalogram (EEG waveforms. The translation rules include the direct mapping from the period of an EEG waveform to the duration of a note, the logarithmic mapping of the change of average power of EEG to music intensity according to the Fechner's law, and a scale-free based mapping from the amplitude of EEG to music pitch according to the power law. To show the actual effect, we applied the deduced sonification rules to EEG segments recorded during rapid-eye movement sleep (REM and slow-wave sleep (SWS. The resulting music is vivid and different between the two mental states; the melody during REM sleep sounds fast and lively, whereas that in SWS sleep is slow and tranquil. 60 volunteers evaluated 25 music pieces, 10 from REM, 10 from SWS and 5 from white noise (WN, 74.3% experienced a happy emotion from REM and felt boring and drowsy when listening to SWS, and the average accuracy for all the music pieces identification is 86.8%(kappa = 0.800, P<0.001. We also applied the method to the EEG data from eyes closed, eyes open and epileptic EEG, and the results showed these mental states can be identified by listeners. CONCLUSIONS/SIGNIFICANCE: The sonification rules may identify the mental states of the brain, which provide a real-time strategy for monitoring brain activities and are potentially useful to neurofeedback therapy.

  5. Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping

    Science.gov (United States)

    Robinson, Jennifer; Calhoun, Vince

    2018-01-01

    Purpose To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping. Methods A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ0 +δχ, with δχ for BOLD magnetic perturbations and χ0 for background. A brain fMRI experiment produces a timeseries of complex-valued images (T2* images), whereby we extract the BOLD phase signals (denoted by δP) by a complex division. By solving an inverse problem, we reconstruct the BOLD δχ dataset from the δP dataset, and the brain χ distribution from a (unwrapped) T2* phase image. Given a 4D dataset of task BOLD fMRI, we implement brain functional mapping by temporal correlation analysis. Results Through a high-field (7T) and high-resolution (0.5mm in plane) task fMRI experiment, we demonstrated in detail the BOLD perturbation model for fMRI phase signal separation (P + δP) and reconstructing intrinsic brain magnetic source (χ and δχ). We also provided to a low-field (3T) and low-resolution (2mm) task fMRI experiment in support of single-subject fMRI study. Our experiments show that the δχ-depicted functional map reveals bidirectional BOLD χ perturbations during the task performance. Conclusions The BOLD perturbation model allows us to separate fMRI phase signal (by complex division) and to perform inverse mapping for pure BOLD δχ reconstruction for intrinsic functional χ mapping. The full brain χ reconstruction (from unwrapped fMRI phase) provides a new brain tissue image that allows to scrutinize the brain tissue idiosyncrasy for the pure BOLD δχ response through an automatic function/structure co-localization. PMID:29351339

  6. Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping.

    Science.gov (United States)

    Chen, Zikuan; Robinson, Jennifer; Calhoun, Vince

    2018-01-01

    To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping. A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ0 +δχ, with δχ for BOLD magnetic perturbations and χ0 for background. A brain fMRI experiment produces a timeseries of complex-valued images (T2* images), whereby we extract the BOLD phase signals (denoted by δP) by a complex division. By solving an inverse problem, we reconstruct the BOLD δχ dataset from the δP dataset, and the brain χ distribution from a (unwrapped) T2* phase image. Given a 4D dataset of task BOLD fMRI, we implement brain functional mapping by temporal correlation analysis. Through a high-field (7T) and high-resolution (0.5mm in plane) task fMRI experiment, we demonstrated in detail the BOLD perturbation model for fMRI phase signal separation (P + δP) and reconstructing intrinsic brain magnetic source (χ and δχ). We also provided to a low-field (3T) and low-resolution (2mm) task fMRI experiment in support of single-subject fMRI study. Our experiments show that the δχ-depicted functional map reveals bidirectional BOLD χ perturbations during the task performance. The BOLD perturbation model allows us to separate fMRI phase signal (by complex division) and to perform inverse mapping for pure BOLD δχ reconstruction for intrinsic functional χ mapping. The full brain χ reconstruction (from unwrapped fMRI phase) provides a new brain tissue image that allows to scrutinize the brain tissue idiosyncrasy for the pure BOLD δχ response through an automatic function/structure co-localization.

  7. Analyzing Electroencephalogram Signal Using EEG Lab

    Directory of Open Access Journals (Sweden)

    Mukesh BHARDWAJ

    2009-01-01

    Full Text Available The EEG is composed of electrical potentials arising from several sources. Each source (including separate neural clusters, blink artifact or pulse artifact forms a unique topography onto the scalp – ‘scalp map‘. Scalp map may be 2-D or 3-D.These maps are mixed according to the principle of linear superposition. Independent component analysis (ICA attempts to reverse the superposition by separating the EEG into mutually independent scalp maps, or components. MATLAB toolbox and graphic user interface, EEGLAB is used for processing EEG data of any number of channels. Wavelet toolbox has been used for 2-D signal analysis.

  8. Analysis of routine EEG usage in a general adult ICU.

    LENUS (Irish Health Repository)

    McHugh, J C

    2009-09-01

    Non-convulsive seizures and status epilepticus are common in brain-injured patients in intensive care units. Continuous electroencephalography (cEEG) monitoring is the most sensitive means of their detection. In centres where cEEG is unavailable, routine EEG is often utilized for diagnosis although its sensitivity is lower.

  9. Involvement of the anterior cingulate cortex in time-based prospective memory task monitoring: An EEG analysis of brain sources using Independent Component and Measure Projection Analysis.

    Directory of Open Access Journals (Sweden)

    Gabriela Cruz

    Full Text Available Time-based prospective memory (PM, remembering to do something at a particular moment in the future, is considered to depend upon self-initiated strategic monitoring, involving a retrieval mode (sustained maintenance of the intention plus target checking (intermittent time checks. The present experiment was designed to explore what brain regions and brain activity are associated with these components of strategic monitoring in time-based PM tasks.24 participants were asked to reset a clock every four minutes, while performing a foreground ongoing word categorisation task. EEG activity was recorded and data were decomposed into source-resolved activity using Independent Component Analysis. Common brain regions across participants, associated with retrieval mode and target checking, were found using Measure Projection Analysis.Participants decreased their performance on the ongoing task when concurrently performed with the time-based PM task, reflecting an active retrieval mode that relied on withdrawal of limited resources from the ongoing task. Brain activity, with its source in or near the anterior cingulate cortex (ACC, showed changes associated with an active retrieval mode including greater negative ERP deflections, decreased theta synchronization, and increased alpha suppression for events locked to the ongoing task while maintaining a time-based intention. Activity in the ACC was also associated with time-checks and found consistently across participants; however, we did not find an association with time perception processing per se.The involvement of the ACC in both aspects of time-based PM monitoring may be related to different functions that have been attributed to it: strategic control of attention during the retrieval mode (distributing attentional resources between the ongoing task and the time-based task and anticipatory/decision making processing associated with clock-checks.

  10. Quantitative Susceptibility Mapping of Human Brain Reflects Spatial Variation in Tissue Composition

    Science.gov (United States)

    Li, Wei; Wu, Bing; Liu, Chunlei

    2011-01-01

    Image phase from gradient echo MRI provides a unique contrast that reflects brain tissue composition variations, such as iron and myelin distribution. Phase imaging is emerging as a powerful tool for the investigation of functional brain anatomy and disease diagnosis. However, the quantitative value of phase is compromised by its nonlocal and orientation dependent properties. There is an increasing need for reliable quantification of magnetic susceptibility, the intrinsic property of tissue. In this study, we developed a novel and accurate susceptibility mapping method that is also phase-wrap insensitive. The proposed susceptibility mapping method utilized two complementary equations: (1) the Fourier relationship of phase and magnetic susceptibility; and (2) the first-order partial derivative of the first equation in the spatial frequency domain. In numerical simulation, this method reconstructed the susceptibility map almost free of streaking artifact. Further, the iterative implementation of this method allowed for high quality reconstruction of susceptibility maps of human brain in vivo. The reconstructed susceptibility map provided excellent contrast of iron-rich deep nuclei and white matter bundles from surrounding tissues. Further, it also revealed anisotropic magnetic susceptibility in brain white matter. Hence, the proposed susceptibility mapping method may provide a powerful tool for the study of brain physiology and pathophysiology. Further elucidation of anisotropic magnetic susceptibility in vivo may allow us to gain more insight into the white matter microarchitectures. PMID:21224002

  11. EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

    Science.gov (United States)

    Shishkin, Sergei L.; Nuzhdin, Yuri O.; Svirin, Evgeny P.; Trofimov, Alexander G.; Fedorova, Anastasia A.; Kozyrskiy, Bogdan L.; Velichkovsky, Boris M.

    2016-01-01

    We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG) marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs) were collected during game's control-on and control-off conditions. A slow negative wave with a maximum in the parietooccipital region was present in each participant's averaged FRPs in the control-on conditions and was absent or had much lower amplitude in the control-off condition. This wave was similar but not identical to stimulus-preceding negativity, a slow negative wave that can be observed during feedback expectation. Classification of intentional vs. spontaneous fixations was based on amplitude features from 13 EEG channels using 300 ms length segments free from electrooculogram contamination (200–500 ms relative to the fixation onset). For the first fixations in the fixation triplets required to make moves in the game, classified against control-off data, a committee of greedy classifiers provided 0.90 ± 0.07 specificity and 0.38 ± 0.14 sensitivity. Similar (slightly lower) results were obtained for the shrinkage Linear Discriminate Analysis (LDA) classifier. The second and third fixations in the triplets were classified at lower rate. We expect that, with improved feature sets and classifiers, a hybrid dwell-based Eye-Brain-Computer Interface (EBCI) can be built using the FRP difference between the intended and spontaneous fixations. If this direction of BCI development will be successful, such a multimodal interface may improve the fluency of interaction and can possibly become the basis for a new input device

  12. EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Sergei L. Shishkin

    2016-11-01

    Full Text Available We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs were collected during game’s control-on and control-off conditions. A slow negative wave with a maximum in the parietooccipital region was present in each participant’s averaged FRPs in the control-on conditions and was absent or had much lower amplitude in the control-off condition. This wave was similar but not identical to stimulus-preceding negativity, a slow negative wave that can be observed during feedback expectation. Classification of intentional vs. spontaneous fixations was based on amplitude features from 13 EEG channels using 300 ms length segments free from electrooculogram contamination (200..500 ms relative to the fixation onset. For the first fixations in the fixation triplets required to make moves in the game, classified against control-off data, a committee of greedy classifiers provided 0.90 ± 0.07 specificity and 0.38 ± 0.14 sensitivity. Similar (slightly lower results were obtained for the shrinkage LDA classifier. The second and third fixations in the triplets were classified at lower rate. We expect that, with improved feature sets and classifiers, a hybrid dwell-based Eye-Brain-Computer Interface (EBCI can be built using the FRP difference between the intended and spontaneous fixations. If this direction of BCI development will be successful, such a multimodal interface may improve the fluency of interaction and can possibly become the basis for a new input device for

  13. Mapping metals in Parkinson's and normal brain using rapid-scanning x-ray fluorescence

    International Nuclear Information System (INIS)

    Popescu, Bogdan F Gh; George, Martin J; McCrea, Richard P E; Devon, Richard M; George, Graham N; Hanson, Akela D; Chapman, L Dean; Nichol, Helen; Bergmann, Uwe; Garachtchenko, Alex V; Luening, Katharina; Kelly, Michael E; Harder, Sheri M; Pickering, Ingrid J

    2009-01-01

    Rapid-scanning x-ray fluorescence (RS-XRF) is a synchrotron technology that maps multiple metals in tissues by employing unique hardware and software to increase scanning speed. RS-XRF was validated by mapping and quantifying iron, zinc and copper in brain slices from Parkinson's disease (PD) and unaffected subjects. Regions and structures in the brain were readily identified by their metal complement and each metal had a unique distribution. Many zinc-rich brain regions were low in iron and vice versa. The location and amount of iron in brain regions known to be affected in PD agreed with analyses using other methods. Sample preparation is simple and standard formalin-fixed autopsy slices are suitable. RS-XRF can simultaneously and non-destructively map and quantify multiple metals and holds great promise to reveal metal pathologies associated with PD and other neurodegenerative diseases as well as diseases of metal metabolism.

  14. A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems

    Science.gov (United States)

    Tahernezhad-Javazm, Farajollah; Azimirad, Vahid; Shoaran, Maryam

    2018-04-01

    Objective. Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. Approach. The paper is divided into two main parts. In the first part, a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and evolutionary algorithms are reviewed and investigated. In the second part, these classifiers and evolutionary algorithms are assessed and compared based on two types of relatively widely used BMI systems, sensory motor rhythm-BMI and event-related potentials-BMI. Moreover, in the second part, some of the improved evolutionary algorithms as well as bi-objective algorithms are experimentally assessed and compared. Main results. In this study two databases are used, and cross-validation accuracy (CVA) and stability to data volume (SDV) are considered as the evaluation criteria for the classifiers. According to the experimental results on both databases, regarding the base classifiers, linear discriminant analysis and support vector machines with respect to CVA evaluation metric, and naive Bayes with respect to SDV demonstrated the best performances. Among the combinatorial classifiers, four classifiers, Bagg-DT (bagging decision tree), LogitBoost, and GentleBoost with respect to CVA, and Bagging-LR (bagging logistic regression) and AdaBoost (adaptive boosting) with respect to SDV had the best performances. Finally, regarding the evolutionary algorithms, single-objective invasive weed optimization (IWO) and bi-objective nondominated sorting IWO algorithms demonstrated the best performances. Significance. We present a general survey on the base and the combinatorial classification methods for EEG signals (sensory motor rhythm and event-related potentials) as well as their optimization methods

  15. User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

    Directory of Open Access Journals (Sweden)

    Aitzol Astigarraga

    2016-01-01

    Full Text Available Brain-Computer Interfaces (BCIs have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG- based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.

  16. ORIGINAL ARTICLE EEG changes and neuroimaging abnormalities ...

    African Journals Online (AJOL)

    salah

    Clinical Genetics Department, Human Genetics & Genome Research Division, ... neuroimaging changes of the brain and EEG abnormalities in correlation to the ... level and by developmental changes2. .... for IQ as a confounding factor.30.

  17. Prediction of treatment outcome in patients with obsessive-compulsive disorder with Low-Resolution Brain Electromagnetic Tomography: a prospective EEG study

    Directory of Open Access Journals (Sweden)

    Daniela eKrause

    2016-01-01

    Full Text Available The issue of predicting treatment response and identifying, in advance, which patient will profit from treating obsessive-compulsive disorder (OCD seems to be an elusive goal. This prospective study investigated brain electric activity (using Low-Resolution Brain Electromagnetic Tomography (LORETA for the purpose of predicting response to treatment. Forty-one unmedicated patients with a DSM-IV diagnosis of OCD were included. A resting 32-channel EEG was obtained from each participant before and after ten weeks of standardized treatment with sertraline and behavioral therapy. LORETA was used to localize the sources of brain electrical activity. At week ten, patients were divided into responders and non-responders (according to a reduction of symptom severity > 50% on the Y-BOCS. LORETA analysis revealed that at baseline responders showed compared to non-responders a significantly lower brain electric activity within the beta 1 (t=2.86, p<0.05, 2 (t=2.81, p<0.05 and 3 (t=2.76, p<0.05 frequency bands and ROI analysis confirmed a reduced activity in alpha 2 (t=2.06, p<0.05 in the anterior cingulate cortex (ACC. When baseline LORETA data were compared to follow-up data, the analysis showed in the responder group a significantly lower brain electrical resting activity in the beta 1 (t=3.17. p<0.05 and beta 3 (t=3.11. p<0.05 frequency bands and equally for the ROI analysis of the orbitofrontal cortex (OFC in the alpha 2 (t=2.15. p<0.05 frequency band. In the group of non-responders the opposite results were found. In addition, a positive correlation between frequency alpha 2 (rho=0.40, p=0.010, beta 3 (rho=0.42, p=0.006, delta (rho=0.33, p=0.038, theta (rho=0.34, p=0.031, alpha 1 (rho=0.38, p=0.015 and beta1 (rho=0.34, p=0.028 of the OFC and the bands delta (rho=0.33, p=0.035, alpha 1 (rho=0.36, p=0.019, alpha 2 (rho=0.34, p=0.031 and beta 3 (rho=0.38, p=0.015 of the ACC with a reduction of the Y-BOCS scores was identified.Our results suggest that

  18. Coding space-time stimulus dynamics in auditory brain maps

    Directory of Open Access Journals (Sweden)

    Yunyan eWang

    2014-04-01

    Full Text Available Sensory maps are often distorted representations of the environment, where ethologically-important ranges are magnified. The implication of a biased representation extends beyond increased acuity for having more neurons dedicated to a certain range. Because neurons are functionally interconnected, non-uniform representations influence the processing of high-order features that rely on comparison across areas of the map. Among these features are time-dependent changes of the auditory scene generated by moving objects. How sensory representation affects high order processing can be approached in the map of auditory space of the owl’s midbrain, where locations in the front are over-represented. In this map, neurons are selective not only to location but also to location over time. The tuning to space over time leads to direction selectivity, which is also topographically organized. Across the population, neurons tuned to peripheral space are more selective to sounds moving into the front. The distribution of direction selectivity can be explained by spatial and temporal integration on the non-uniform map of space. Thus, the representation of space can induce biased computation of a second-order stimulus feature. This phenomenon is likely observed in other sensory maps and may be relevant for behavior.

  19. EEG frequency PCA in EEG-ERP dynamics.

    Science.gov (United States)

    Barry, Robert J; De Blasio, Frances M

    2018-05-01

    Principal components analysis (PCA) has long been used to decompose the ERP into components, and these mathematical entities are increasingly accepted as meaningful and useful representatives of the electrophysiological components constituting the ERP. A similar expansion appears to be beginning in regard to decomposition of the EEG amplitude spectrum into frequency components via frequency PCA. However, to date, there has been no exploration of the brain's dynamic EEG-ERP linkages using PCA decomposition to assess components in each measure. Here, we recorded intrinsic EEG in both eyes-closed and eyes-open resting conditions, followed by an equiprobable go/no-go task. Frequency PCA of the EEG, including the nontask resting and within-task prestimulus periods, found seven frequency components within the delta to beta range. These differentially predicted PCA-derived go and no-go N1 and P3 ERP components. This demonstration suggests that it may be beneficial in future brain dynamics studies to implement PCA for the derivation of data-driven components from both the ERP and EEG. © 2017 Society for Psychophysiological Research.

  20. Dynamics of chaotic maps for modelling the multifractal spectrum of human brain Diffusion Tensor Images

    International Nuclear Information System (INIS)

    Provata, A.; Katsaloulis, P.; Verganelakis, D.A.

    2012-01-01

    Highlights: ► Calculation of human brain multifractal spectra. ► Calculations are based on Diffusion Tensor MRI Images. ► Spectra are modelled by coupled Ikeda map dynamics. ► Coupled lattice Ikeda maps model well only positive multifractal spectra. ► Appropriately modified coupled lattice Ikeda maps give correct spectra. - Abstract: The multifractal spectra of 3d Diffusion Tensor Images (DTI) obtained by magnetic resonance imaging of the human brain are studied. They are shown to deviate substantially from artificial brain images with the same white matter intensity. All spectra, obtained from 12 healthy subjects, show common characteristics indicating non-trivial moments of the intensity. To model the spectra the dynamics of the chaotic Ikeda map are used. The DTI multifractal spectra for positive q are best approximated by 3d coupled Ikeda maps in the fully developed chaotic regime. The coupling constants are as small as α = 0.01. These results reflect not only the white tissue non-trivial architectural complexity in the human brain, but also demonstrate the presence and importance of coupling between neuron axons. The architectural complexity is also mirrored by the deviations in the negative q-spectra, where the rare events dominate. To obtain a good agreement in the DTI negative q-spectrum of the brain with the Ikeda dynamics, it is enough to slightly modify the most rare events of the coupled Ikeda distributions. The representation of Diffusion Tensor Images with coupled Ikeda maps is not unique: similar conclusions are drawn when other chaotic maps (Tent, Logistic or Henon maps) are employed in the modelling of the neuron axons network.

  1. Correlation of BOLD Signal with Linear and Nonlinear Patterns of EEG in Resting State EEG-Informed fMRI

    Directory of Open Access Journals (Sweden)

    Galina V. Portnova

    2018-01-01

    Full Text Available Concurrent EEG and fMRI acquisitions in resting state showed a correlation between EEG power in various bands and spontaneous BOLD fluctuations. However, there is a lack of data on how changes in the complexity of brain dynamics derived from EEG reflect variations in the BOLD signal. The purpose of our study was to correlate both spectral patterns, as linear features of EEG rhythms, and nonlinear EEG dynamic complexity with neuronal activity obtained by fMRI. We examined the relationships between EEG patterns and brain activation obtained by simultaneous EEG-fMRI during the resting state condition in 25 healthy right-handed adult volunteers. Using EEG-derived regressors, we demonstrated a substantial correlation of BOLD signal changes with linear and nonlinear features of EEG. We found the most significant positive correlation of fMRI signal with delta spectral power. Beta and alpha spectral features had no reliable effect on BOLD fluctuation. However, dynamic changes of alpha peak frequency exhibited a significant association with BOLD signal increase in right-hemisphere areas. Additionally, EEG dynamic complexity as measured by the HFD of the 2–20 Hz EEG frequency range significantly correlated with the activation of cortical and subcortical limbic system areas. Our results indicate that both spectral features of EEG frequency bands and nonlinear dynamic properties of spontaneous EEG are strongly associated with fluctuations of the BOLD signal during the resting state condition.

  2. EEG-fMRI Study of Alpha-Stimulation Neurobiofeedback Training Course.

    Science.gov (United States)

    Kozlova, L I; Shtark, M B; Mel'nikov, M E; Verevkin, E G; Savelov, A A; Petrovskii, E D

    2016-09-01

    fMRI-EEG dynamics of brain activity in volunteers was studied during the course of EEG alpha-stimulation training (20 sessions). Twenty-three healthy men (20-35 years) were subjected to 3-fold mapping in a feedback loop (EEG alpha-rhythm biofeedback with acoustic reinforcement). This procedure was performed at the beginning, middle, and end of the course. During the first neurofeedback training session, deactivation (pBrodmann area 39, and cerebellum. Activation (pareas of both hemispheres, and Brodmann area 32. During final (third) neurofeedback training session, we observed strong deactivation (pBrodmann areas 6, 9, 7, 31, 8, 13, and 22). Changes in the alpha wave power were most pronounced in the primary and secondary somatosensory cortex of the left hemisphere (Brodmann areas 2L and 5L).

  3. Quantification of brain images using Korean standard templates and structural and cytoarchitectonic probabilistic maps

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jae Sung; Lee, Dong Soo; Kim, Yu Kyeong [College of Medicine, Seoul National Univ., Seoul (Korea, Republic of)] [and others

    2004-06-01

    Population based structural and functional maps of the brain provide effective tools for the analysis and interpretation of complex and individually variable brain data. Brain MRI and PET standard templates and statistical probabilistic maps based on image data of Korean normal volunteers have been developed and probabilistic maps based on cytoarchitectonic data have been introduced. A quantification method using these data was developed for the objective assessment of regional intensity in the brain images. Age, gender and ethnic specific anatomical and functional brain templates based on MR and PET images of Korean normal volunteers were developed. Korean structural probabilistic maps for 89 brain regions and cytoarchitectonic probabilistic maps for 13 Brodmann areas were transformed onto the standard templates. Brain FDG PET and SPGR MR images of normal volunteers were spatially normalized onto the template of each modality and gender. Regional uptake of radiotracers in PET and gray matter concentration in MR images were then quantified by averaging (or summing) regional intensities weighted using the probabilistic maps of brain regions. Regionally specific effects of aging on glucose metabolism in cingulate cortex were also examined. Quantification program could generate quantification results for single spatially normalized images per 20 seconds. Glucose metabolism change in cingulate gyrus was regionally specific: ratios of glucose metabolism in the rostral anterior cingulate vs. posterior cingulate and the caudal anterior cingulate vs. posterior cingulate were significantly decreased as the age increased. 'Rostral anterior' / 'posterior' was decreased by 3.1% per decade of age (p<10{sup -11}, r=0.81) and 'caudal anterior' / 'posterior' was decreased by 1.7% (p<10{sup -8}, r=0.72). Ethnic specific standard templates and probabilistic maps and quantification program developed in this study will be useful for the analysis

  4. Anatomically standardized statistical mapping of 123I-IMP SPECT in brain tumors

    International Nuclear Information System (INIS)

    Shibata, Yasushi; Akimoto, Manabu; Matsushita, Akira; Yamamoto, Tetsuya; Takano, Shingo; Matsumura, Akira

    2010-01-01

    123 I-iodoamphetamine Single Photon Emission Computed Tomography (IMP SPECT) is used to evaluate cerebral blood flow. However, application of IMP SPECT to patients with brain tumors has been rarely reported. Primary central nervous system lymphoma (PCNSL) is a rare tumor that shows delayed IMP uptake. The relatively low spatial resolution of SPECT is a clinical problem in diagnosing brain tumors. We examined anatomically standardized statistical mapping of IMP SPECT in patients with brain lesions. This study included 49 IMP SPECT images for 49 patients with brain lesions: 20 PCNSL, 1 Burkitt's lymphoma, 14 glioma, 4 other tumor, 7 inflammatory disease and 3 without any pathological diagnosis but a clinical diagnosis of PCNSL. After intravenous injection of 222 MBq of 123 I-IMP, early (15 minutes) and delayed (4 hours) images were acquired using a multi-detector SPECT machine. All SPECT data were transferred to a newly developed software program iNeurostat+ (Nihon Medi-physics). SPECT data were anatomically standardized on normal brain images. Regions of increased uptake of IMP were statistically mapped on the tomographic images of normal brain. Eighteen patients showed high uptake in the delayed IMP SPECT images (16 PCNSL, 2 unknown). Other tumor or diseases did not show high uptake of delayed IMP SPECT, so there were no false positives. Four patients with pathologically proven PCNSL showed no uptake in original IMP SPECT. These tumors were too small to detect in IMP SPECT. However, statistical mapping revealed IMP uptake in 18 of 20 pathologically verified PCNSL patients. A heterogeneous IMP uptake was seen in homogenous tumors in MRI. For patients with a hot IMP uptake, statistical mapping showed clearer uptake. IMP SPECT is a sensitive test to diagnose of PCNSL, although it produced false negative results for small posterior fossa tumor. Anatomically standardized statistical mapping is therefore considered to be a useful method for improving the diagnostic

  5. Multicenter R2* mapping in the healthy brain

    DEFF Research Database (Denmark)

    Ropele, Stefan; Wattjes, Mike P; Langkammer, Christian

    2014-01-01

    structures. METHODS: R2* mapping was performed in 81 healthy subjects in seven centers using different 3 T systems. R2* was calculated from a dual-echo gradient echo sequence and was assessed in several deep gray matter structures. The inter-scanner and inter-subject variability of R2* was calculated...

  6. One century of brain mapping using Brodmann areas.

    Science.gov (United States)

    Strotzer, Michael

    2009-08-01

    100 years after their publication, Brodmann's maps of the cerebral cortex are universally used to locate neuropsychological functions. On the occasion of this jubilee the life and work of Korbinian Brodmann are reported. The core functions of each single Brodmann area are described and Brodmann's views on neuropsychological processes are depicted.

  7. Statistical probabilistic mapping in the individual brain space: decreased metabolism in epilepsy with FDG PET

    International Nuclear Information System (INIS)

    Oh, Jung Su; Lee, Jae Sung; Kim, Yu Kyeong; Chung, June Key; Lee, Myung Chul; Lee, Dong Soo

    2005-01-01

    In the statistical probabilistic mapping, commonly, differences between two or more groups of subjects are statistically analyzed following spatial normalization. However, to our best knowledge, there is few study which performed the statistical mapping in the individual brain space rather than in the stereotaxic brain space, i.e., template space. Therefore, in the current study, a new method for mapping the statistical results in the template space onto individual brain space has been developed. Four young subjects with epilepsy and their age-matched thirty normal healthy subjects were recruited. Both FDG PET and T1 structural MRI was scanned in these groups. Statistical analysis on the decreased FDG metabolism in epilepsy was performed on the SPM with two sample t-test (p < 0.001, intensity threshold 100). To map the statistical results onto individual space, inverse deformation was performed as follows. With SPM deformation toolbox, DCT (discrete cosine transform) basis-encoded deformation fields between individual T1 images and T1 MNI template were obtained. Afterward, inverse of those fields, i.e., inverse deformation fields were obtained. Since both PET and T1 images have been already normalized in the same MNI space, inversely deformed results in PET is on the individual brain MRI space. By applying inverse deformation field on the statistical results of the PET, the statistical map of decreased metabolism in individual spaces were obtained. With statistical results in the template space, localization of decreased metabolism was in the inferior temporal lobe, which was slightly inferior to the hippocampus. The statistical results in the individual space were commonly located in the hippocampus, where the activation should be decreased according to a priori knowledge of neuroscience. With our newly developed statistical mapping on the individual spaces, the localization of the brain functional mapping became more appropriate in the sense of neuroscience

  8. Statistical probabilistic mapping in the individual brain space: decreased metabolism in epilepsy with FDG PET

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Jung Su; Lee, Jae Sung; Kim, Yu Kyeong; Chung, June Key; Lee, Myung Chul; Lee, Dong Soo [Seoul National University Hospital, Seoul (Korea, Republic of)

    2005-07-01

    In the statistical probabilistic mapping, commonly, differences between two or more groups of subjects are statistically analyzed following spatial normalization. However, to our best knowledge, there is few study which performed the statistical mapping in the individual brain space rather than in the stereotaxic brain space, i.e., template space. Therefore, in the current study, a new method for mapping the statistical results in the template space onto individual brain space has been developed. Four young subjects with epilepsy and their age-matched thirty normal healthy subjects were recruited. Both FDG PET and T1 structural MRI was scanned in these groups. Statistical analysis on the decreased FDG metabolism in epilepsy was performed on the SPM with two sample t-test (p < 0.001, intensity threshold 100). To map the statistical results onto individual space, inverse deformation was performed as follows. With SPM deformation toolbox, DCT (discrete cosine transform) basis-encoded deformation fields between individual T1 images and T1 MNI template were obtained. Afterward, inverse of those fields, i.e., inverse deformation fields were obtained. Since both PET and T1 images have been already normalized in the same MNI space, inversely deformed results in PET is on the individual brain MRI space. By applying inverse deformation field on the statistical results of the PET, the statistical map of decreased metabolism in individual spaces were obtained. With statistical results in the template space, localization of decreased metabolism was in the inferior temporal lobe, which was slightly inferior to the hippocampus. The statistical results in the individual space were commonly located in the hippocampus, where the activation should be decreased according to a priori knowledge of neuroscience. With our newly developed statistical mapping on the individual spaces, the localization of the brain functional mapping became more appropriate in the sense of neuroscience.

  9. Brain signal analysis using EEG and Entropy to study the effect of physical and mental tasks on cognitive performance

    Directory of Open Access Journals (Sweden)

    Dineshen Chuckravanen

    2015-07-01

    Full Text Available Some theoretical control models posit that the fatigue which is developed during physical activity is not always peripheral and it is the brain which causes this feeling of fatigue. This fatigue develops due to a decrease of metabolic resources to and from the brain that modulates physical performance. Therefore, this research was conducted to find out if there was finite level ofmetabolic energy resources in the brain, by performing both mental and physical activities to exhaustion. It was found that there was an overflow of information during the exercise-involved experiment. The circular relationship between fatigue, cognitive performance and arousal state insinuates that one should apply more effort to maintain performance levels which would require more energy resources that eventually accelerates the development of fatigue. Thus, there appeared to be a limited amount of energy resources in the brain as shown by the cognitive performance of the participants.

  10. Image-guided recording system for spatial and temporal mapping of neuronal activities in brain slice.

    Science.gov (United States)

    Choi, Geonho; Lee, Jeonghyeon; Kim, Hyeongeun; Jang, Jaemyung; Im, Changkyun; Jeon, Nooli; Jung, Woonggyu

    2018-03-01

    In this study, we introduce the novel image-guided recording system (IGRS) for efficient interpretation of neuronal activities in the brain slice. IGRS is designed to combine microelectrode array (MEA) and optical coherence tomography at the customized upright microscope. It allows to record multi-site neuronal signals and image of the volumetric brain anatomy in a single body configuration. For convenient interconnection between a brain image and neuronal signals, we developed the automatic mapping protocol that enables us to project acquired neuronal signals on a brain image. To evaluate the performance of IGRS, hippocampal signals of the brain slice were monitored, and corresponding with two-dimensional neuronal maps were successfully reconstructed. Our results indicated that IGRS and mapping protocol can provide the intuitive information regarding long-term and multi-sites neuronal signals. In particular, the temporal and spatial mapping capability of neuronal signals would be a very promising tool to observe and analyze the massive neuronal activity and connectivity in MEA-based electrophysiological studies. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Amplitude spectrum EEG signal evidence for the dissociation of motor and perceptual spatial working memory in the human brain.

    Science.gov (United States)

    Smyrnis, Nikolaos; Protopapa, Foteini; Tsoukas, Evangelos; Balogh, Allison; Siettos, Constantinos I; Evdokimidis, Ioannis

    2014-02-01

    This study investigated the question whether spatial working memory related to movement plans (motor working memory) and spatial working memory related to spatial attention and perceptual processes (perceptual spatial working memory) share the same neurophysiological substrate or there is evidence for separate motor and perceptual working memory streams of processing. Towards this aim, ten healthy human subjects performed delayed responses to visual targets presented at different spatial locations. Two tasks were attained, one in which the spatial location of the target was the goal for a pointing movement and one in which the spatial location of the target was used for a perceptual (yes or no) change detection. Each task involved two conditions: a memory condition in which the target remained visible only for the first 250 ms of the delay period and a delay condition in which the target location remained visible throughout the delay period. The amplitude spectrum analysis of the EEG revealed that the alpha (8-12 Hz) band signal was smaller, while the beta (13-30 Hz) and gamma (30-45 Hz) band signals were larger in the memory compared to the non-memory condition. The alpha band signal difference was confined to the frontal midline area; the beta band signal difference extended over the right hemisphere and midline central area, and the gamma band signal difference was confined to the right occipitoparietal area. Importantly, both in beta and gamma bands, we observed a significant increase in the movement-related compared to the perceptual-related memory-specific amplitude spectrum signal in the central midline area. This result provides clear evidence for the dissociation of motor and perceptual spatial working memory.

  12. Increased brain iron deposition is a risk factor for brain atrophy in patients with haemodialysis: a combined study of quantitative susceptibility mapping and whole brain volume analysis.

    Science.gov (United States)

    Chai, Chao; Zhang, Mengjie; Long, Miaomiao; Chu, Zhiqiang; Wang, Tong; Wang, Lijun; Guo, Yu; Yan, Shuo; Haacke, E Mark; Shen, Wen; Xia, Shuang

    2015-08-01

    To explore the correlation between increased brain iron deposition and brain atrophy in patients with haemodialysis and their correlation with clinical biomarkers and neuropsychological test. Forty two patients with haemodialysis and forty one age- and gender-matched healthy controls were recruited in this prospective study. 3D whole brain high resolution T1WI and susceptibility weighted imaging were scanned on a 3 T MRI system. The brain volume was analyzed using voxel-based morphometry (VBM) in patients and to compare with that of healthy controls. Quantitative susceptibility mapping was used to measure and compare the susceptibility of different structures between patients and healthy controls. Correlation analysis was used to investigate the relationship between the brain volume, iron deposition and neuropsychological scores. Stepwise multiple regression analysis was used to explore the effect of clinical biomarkers on the brain volumes in patients. Compared with healthy controls, patients with haemodialysis showed decreased volume of bilateral putamen and left insular lobe (All P brain iron deposition is negatively correlated with the decreased volume of bilateral putamen (P brain iron deposition and dialysis duration was risk factors for brain atrophy in patients with haemodialysis. The decreased gray matter volume of the left insular lobe was correlated with neurocognitive impairment.

  13. Atypical category processing and hemispheric asymmetries in high-functioning children with autism: revealed through high-density EEG mapping.

    Science.gov (United States)

    Fiebelkorn, Ian C; Foxe, John J; McCourt, Mark E; Dumas, Kristina N; Molholm, Sophie

    2013-05-01

    Behavioral evidence for an impaired ability to group objects based on similar physical or semantic properties in autism spectrum disorders (ASD) has been mixed. Here, we recorded brain activity from high-functioning children with ASD as they completed a visual-target detection task. We then assessed the extent to which object-based selective attention automatically generalized from targets to non-target exemplars from the same well-known object class (e.g., dogs). Our results provide clear electrophysiological evidence that children with ASD (N=17, aged 8-13 years) process the similarity between targets (e.g., a specific dog) and same-category non-targets (SCNT) (e.g., another dog) to a lesser extent than do their typically developing (TD) peers (N=21). A closer examination of the data revealed striking hemispheric asymmetries that were specific to the ASD group. These findings align with mounting evidence in the autism literature of anatomic underconnectivity between the cerebral hemispheres. Years of research in individuals with TD have demonstrated that the left hemisphere (LH) is specialized toward processing local (or featural) stimulus properties and the right hemisphere (RH) toward processing global (or configural) stimulus properties. We therefore propose a model where a lack of communication between the hemispheres in ASD, combined with typical hemispheric specialization, is a root cause for impaired categorization and the oft-observed bias to process local over global stimulus properties. Copyright © 2012 Elsevier Ltd. All rights reserved.

  14. Time-efficient, high-resolution, whole brain three-dimensional macromolecular proton fraction mapping.

    Science.gov (United States)

    Yarnykh, Vasily L

    2016-05-01

    Macromolecular proton fraction (MPF) mapping is a quantitative MRI method that reconstructs parametric maps of a relative amount of macromolecular protons causing the magnetization transfer (MT) effect and provides a biomarker of myelination in neural tissues. This study aimed to develop a high-resolution whole brain MPF mapping technique using a minimal number of source images for scan time reduction. The described technique was based on replacement of an actually acquired reference image without MT saturation by a synthetic one reconstructed from R1 and proton density maps, thus requiring only three source images. This approach enabled whole brain three-dimensional MPF mapping with isotropic 1.25 × 1.25 × 1.25 mm(3) voxel size and a scan time of 20 min. The synthetic reference method was validated against standard MPF mapping with acquired reference images based on data from eight healthy subjects. Mean MPF values in segmented white and gray matter appeared in close agreement with no significant bias and small within-subject coefficients of variation (maps demonstrated sharp white-gray matter contrast and clear visualization of anatomical details, including gray matter structures with high iron content. The proposed synthetic reference method improves resolution of MPF mapping and combines accurate MPF measurements with unique neuroanatomical contrast features. © 2015 Wiley Periodicals, Inc.

  15. Correlation between intra- and extracranial background EEG

    DEFF Research Database (Denmark)

    Duun-Henriksen, Jonas; Kjaer, Troels W.; Madsen, Rasmus E.

    2012-01-01

    Scalp EEG is the most widely used modality to record the electrical signals of the brain. It is well known that the volume conduction of these brain waves through the brain, cerebrospinal fluid, skull and scalp reduces the spatial resolution and the signal amplitude. So far the volume conduction...... has primarily been investigated by realistic head models or interictal spike analysis. We have set up a novel and more realistic experiment that made it possible to compare the information in the intra- and extracranial EEG. We found that intracranial EEG channels contained correlated patterns when...... placed less than 30 mm apart, that intra- and extracranial channels were partly correlated when placed less than 40 mm apart, and that extracranial channels probably were correlated over larger distances. The underlying cortical area that influences the extracranial EEG is found to be up to 45 cm2...

  16. Brain Mapping of Low and High Implusivity based P300 Signals

    Science.gov (United States)

    Turnip, Arjon; Dwi, Esti K.; Hidayat, Taufik; Hidayat, Teddy

    2018-04-01

    Impulsiveness is defined as action without good planning and with little consideration the consequences. Impulsive actions are typically poorly conceived, prematurely expressed, or inappropriate to the undesirable situation such as abuse of drugs. Instead of taking treatment for an addiction subject, it is better take prevention. In this paper, an implusivity detection based EEG-P300 potential is proposed. Twenty four subjects consist of three groups (addiction, methadone, and control) are involved in the experiment. Five different pictures (one picture related drug is used as a target) were randomly flashed to the subjects. The subject is asked to comfortly sit in a chair and to silently count the appearance number of the target. The high amplitude of the P300 component with shortest latency and dominant brain activity are indicated by high implusive group.

  17. Electroencephalographic (eeg coherence between visual and motor areas of the left and the right brain hemisphere while performing visuomotor task with the right and the left hand

    Directory of Open Access Journals (Sweden)

    Simon Brežan

    2007-09-01

    Full Text Available Background: Unilateral limb movements are based on the activation of contralateral primary motor cortex and the bilateral activation of premotor cortices. Performance of a visuomotor task requires a visuomotor integration between motor and visual cortical areas. The functional integration (»binding« of different brain areas, is probably mediated by the synchronous neuronal oscillatory activity, which can be determined by electroencephalographic (EEG coherence analysis. We introduced a new method of coherence analysis and compared coherence and power spectra in the left and right hemisphere for the right vs. left hand visuomotor task, hypothesizing that the increase in coherence and decrease in power spectra while performing the task would be greater in the contralateral hemisphere.Methods: We analyzed 6 healthy subjects and recorded their electroencephalogram during visuomotor task with the right or the left hand. For data analysis, a special Matlab computer programme was designed. The results were statistically analysed by a two-way analysis of variance, one-way analysis of variance and post-hoc t-tests with Bonferroni correction.Results: We demonstrated a significant increase in coherence (p < 0.05 for the visuomotor task compared to control tasks in alpha (8–13 Hz in beta 1 (13–20 Hz frequency bands between visual and motor electrodes. There were no significant differences in coherence nor power spectra depending on the hand used. The changes of coherence and power spectra between both hemispheres were symmetrical.Conclusions: In previous studies, a specific increase of coherence and decrease of power spectra for the visuomotor task was found, but we found no conclusive asymmetries when performing the task with right vs. left hand. This could be explained in a way that increases in coherence and decreases of power spectra reflect symmetrical activation and cooperation between more complex visual and motor brain areas.

  18. Three-Dimensional Computer Graphics Brain-Mapping Project

    Science.gov (United States)

    1988-03-24

    1975-76, one of these brains was hand digitized. It was then reconstructed three dimensionally, using an Evans and Sutherland Picture System 2. This...Yakovlev Collection, we use the Evans and Sutherland Picture System 2 which we have been employing for this purpose for a dozen years. Its virtue is...careful, experimentally designed new protocol (See Figure 20). Most of these heads were imaged with Computed Tomography, thanks to Clint Stiles of Picker

  19. Mapping White Matter Microstructure in the One Month Human Brain.

    Science.gov (United States)

    Dean, D C; Planalp, E M; Wooten, W; Adluru, N; Kecskemeti, S R; Frye, C; Schmidt, C K; Schmidt, N L; Styner, M A; Goldsmith, H H; Davidson, R J; Alexander, A L

    2017-08-29

    White matter microstructure, essential for efficient and coordinated transmission of neural communications, undergoes pronounced development during the first years of life, while deviations to this neurodevelopmental trajectory likely result in alterations of brain connectivity relevant to behavior. Hence, systematic evaluation of white matter microstructure in the normative brain is critical for a neuroscientific approach to both typical and atypical early behavioral development. However, few studies have examined the infant brain in detail, particularly in infants under 3 months of age. Here, we utilize quantitative techniques of diffusion tensor imaging and neurite orientation dispersion and density imaging to investigate neonatal white matter microstructure in 104 infants. An optimized multiple b-value diffusion protocol was developed to allow for successful acquisition during non-sedated sleep. Associations between white matter microstructure measures and gestation corrected age, regional asymmetries, infant sex, as well as newborn growth measures were assessed. Results highlight changes of white matter microstructure during the earliest periods of development and demonstrate differential timing of developing regions and regional asymmetries. Our results contribute to a growing body of research investigating the neurobiological changes associated with neurodevelopment and suggest that characteristics of white matter microstructure are already underway in the weeks immediately following birth.

  20. Potential brain language reorganization in a boy with refractory epilepsy; an fNIRS–EEG and fMRI comparison

    Directory of Open Access Journals (Sweden)

    Phetsamone Vannasing

    2016-01-01

    Full Text Available As part of a presurgical investigation for a resection of a tumor located in the left temporal brain region, we evaluated pre- and postsurgical language lateralization in a right-handed boy with refractory epilepsy. In this study, we compared functional near infrared spectroscopy (fNIRS results obtained while the participant performed expressive and receptive language tasks with those obtained using functional magnetic resonance imaging (fMRI. This case study illustrates the potential for NIRS to contribute favorably to the localization of language functions in children with epilepsy and cognitive or behavioral problems and its potential advantages over fMRI in presurgical assessment. Moreover, it suggests that fNIRS is sensitive in localizing an atypical language network or potential brain reorganization related to epilepsy in young patients.

  1. An Efficient Framework for EEG Analysis with Application to Hybrid Brain Computer Interfaces Based on Motor Imagery and P300

    Directory of Open Access Journals (Sweden)

    Jinyi Long

    2017-01-01

    Full Text Available The hybrid brain computer interface (BCI based on motor imagery (MI and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI.

  2. Application of statistical parametric mapping in PET and SPECT brain functional imaging

    International Nuclear Information System (INIS)

    Guo Wanhua

    2002-01-01

    Regional of interest (ROI) is the method regularly used to analyze brain functional imaging. But, due to its obvious shortcomings such as subjectivity and poor reproducibility, precise analyzing the brain function was seriously limited. Therefore, statistical parametric mapping (SPM) as an automatic analyze software was developed based on voxel or pixel to resolve this problem. Using numerous mathematical models, it can be used to statistically assess the whole brain pixel. Present review introduces its main principle, modular composition and practical application. It can be concluded, with development of neuroscience, the SPM software will be used more widely in relative field, like neurobiology, cognition and neuropharmacology

  3. Using a concept map as a tool for strategic planning: The Healthy Brain Initiative.

    Science.gov (United States)

    Anderson, Lynda A; Day, Kristine L; Vandenberg, Anna E

    2011-09-01

    Concept mapping is a tool to assist in strategic planning that allows planners to work through a sequence of phases to produce a conceptual framework. Although several studies describe how concept mapping is applied to various public health problems, the flexibility of the methods used in each phase of the process is often overlooked. If practitioners were more aware of the flexibility, more public health endeavors could benefit from using concept mapping as a tool for strategic planning. The objective of this article is to describe how the 6 concept-mapping phases originally outlined by William Trochim guided our strategic planning process and how we adjusted the specific methods in the first 2 phases to meet the specialized needs and requirements to create The Healthy Brain Initiative: A National Public Health Road Map to Maintaining Cognitive Health. In the first stage (phases 1 and 2 of concept mapping), we formed a steering committee, convened 4 work groups over a period of 3 months, and generated an initial set of 42 action items grounded in science. In the second stage (phases 3 and 4), we engaged stakeholders in sorting and rating the action items and constructed a series of concept maps. In the third and final stage (phases 5 and 6), we examined and refined the action items and generated a final concept map consisting of 44 action items. We then selected the top 10 action items, and in 2007, we published The Healthy Brain Initiative: A National Public Health Road Map to Maintaining Cognitive Health, which represents the strategic plan for The Healthy Brain Initiative.

  4. EOG Artifacts Removal in EEG Measurements for Affective Interaction

    NARCIS (Netherlands)

    Qi, Wen

    2014-01-01

    A brain-computer interface (BCI) is a direct link between the brain and a computer. Multi-modal input with BCI forms a promising solution for creating rich gaming experience. Electroencephalography (EEG) measurement is the sole necessary component for a BCI system. EEG signals have the

  5. R2* mapping for brain iron: associations with cognition in normal aging.

    Science.gov (United States)

    Ghadery, Christine; Pirpamer, Lukas; Hofer, Edith; Langkammer, Christian; Petrovic, Katja; Loitfelder, Marisa; Schwingenschuh, Petra; Seiler, Stephan; Duering, Marco; Jouvent, Eric; Schmidt, Helena; Fazekas, Franz; Mangin, Jean-Francois; Chabriat, Hugues; Dichgans, Martin; Ropele, Stefan; Schmidt, Reinhold

    2015-02-01

    Brain iron accumulates during aging and has been associated with neurodegenerative disorders including Alzheimer's disease. Magnetic resonance (MR)-based R2* mapping enables the in vivo detection of iron content in brain tissue. We investigated if during normal brain aging iron load relates to cognitive impairment in region-specific patterns in a community-dwelling cohort of 336 healthy, middle aged, and older adults from the Austrian Stroke Prevention Family Study. MR imaging and R2* mapping in the basal ganglia and neocortex were done at 3T. Comprehensive neuropsychological testing assessed memory, executive function, and psychomotor speed. We found the highest iron concentration in the globus pallidus, and pallidal and putaminal iron was significantly and inversely associated with cognitive performance in all cognitive domains, except memory. These associations were iron load dependent. Vascular brain lesions and brain volume did not mediate the relationship between iron and cognitive performance. We conclude that higher R2*-determined iron in the basal ganglia correlates with cognitive impairment during brain aging independent of concomitant brain abnormalities. The prognostic significance of this finding needs to be determined. Copyright © 2015 Elsevier Inc. All rights reserved.

  6. Investigation of the Correlation Between Neurocognitive Function with Advanced Magnetic Resonance Imaging (MRI), Electroencephalography (EEG) in Patients with Traumatic Brain Injury Exposure: Neurocognitive function and advanced MRI and EEG

    Science.gov (United States)

    2011-01-01

    child (MVA007), pneumonia (MVA017), urinary infection (MVA018), appendix (MVA026), a leg fracture (MVA008), and tonsils and fractures (MVA019). The...Toronto CR 2011-015 59 Glossary ..... DTI – diffusion tensor imaging EEG – electroencephalography FLAIR – fluid-attenuated inversion recovery

  7. In-depth mapping of the mouse brain N-glycoproteome reveals widespread N-glycosylation of diverse brain proteins.

    Science.gov (United States)

    Fang, Pan; Wang, Xin-Jian; Xue, Yu; Liu, Ming-Qi; Zeng, Wen-Feng; Zhang, Yang; Zhang, Lei; Gao, Xing; Yan, Guo-Quan; Yao, Jun; Shen, Hua-Li; Yang, Peng-Yuan

    2016-06-21

    N-glycosylation is one of the most prominent and abundant posttranslational modifications of proteins. It is estimated that over 50% of mammalian proteins undergo glycosylation. However, the analysis of N-glycoproteins has been limited by the available analytical technology. In this study, we comprehensively mapped the N-glycosylation sites in the mouse brain proteome by combining complementary methods, which included seven protease treatments, four enrichment techniques and two fractionation strategies. Altogether, 13492 N-glycopeptides containing 8386 N-glycosylation sites on 3982 proteins were identified. After evaluating the performance of the above methods, we proposed a simple and efficient workflow for large-scale N-glycosylation site mapping. The optimized workflow yielded 80% of the initially identified N-glycosylation sites with considerably less effort. Analysis of the identified N-glycoproteins revealed that many of the mouse brain proteins are N-glycosylated, including those proteins in critical pathways for nervous system development and neurological disease. Additionally, several important biomarkers of various diseases were found to be N-glycosylated. These data confirm that N-glycosylation is important in both physiological and pathological processes in the brain, and provide useful details about numerous N-glycosylation sites in brain proteins.

  8. A quantitative brain map of experimental cerebral malaria pathology.

    Directory of Open Access Journals (Sweden)

    Patrick Strangward

    2017-03-01

    Full Text Available The murine model of experimental cerebral malaria (ECM has been utilised extensively in recent years to study the pathogenesis of human cerebral malaria (HCM. However, it has been proposed that the aetiologies of ECM and HCM are distinct, and, consequently, no useful mechanistic insights into the pathogenesis of HCM can be obtained from studying the ECM model. Therefore, in order to determine the similarities and differences in the pathology of ECM and HCM, we have performed the first spatial and quantitative histopathological assessment of the ECM syndrome. We demonstrate that the accumulation of parasitised red blood cells (pRBCs in brain capillaries is a specific feature of ECM that is not observed during mild murine malaria infections. Critically, we show that individual pRBCs appear to occlude murine brain capillaries during ECM. As pRBC-mediated congestion of brain microvessels is a hallmark of HCM, this suggests that the impact of parasite accumulation on cerebral blood flow may ultimately be similar in mice and humans during ECM and HCM, respectively. Additionally, we demonstrate that cerebrovascular CD8+ T-cells appear to co-localise with accumulated pRBCs, an event that corresponds with development of widespread vascular leakage. As in HCM, we show that vascular leakage is not dependent on extensive vascular destruction. Instead, we show that vascular leakage is associated with alterations in transcellular and paracellular transport mechanisms. Finally, as in HCM, we observed axonal injury and demyelination in ECM adjacent to diverse vasculopathies. Collectively, our data therefore shows that, despite very different presentation, and apparently distinct mechanisms, of parasite accumulation, there appear to be a number of comparable features of cerebral pathology in mice and in humans during ECM and HCM, respectively. Thus, when used appropriately, the ECM model may be useful for studying specific pathological features of HCM.

  9. A quantitative brain map of experimental cerebral malaria pathology.

    Science.gov (United States)

    Strangward, Patrick; Haley, Michael J; Shaw, Tovah N; Schwartz, Jean-Marc; Greig, Rachel; Mironov, Aleksandr; de Souza, J Brian; Cruickshank, Sheena M; Craig, Alister G; Milner, Danny A; Allan, Stuart M; Couper, Kevin N

    2017-03-01

    The murine model of experimental cerebral malaria (ECM) has been utilised extensively in recent years to study the pathogenesis of human cerebral malaria (HCM). However, it has been proposed that the aetiologies of ECM and HCM are distinct, and, consequently, no useful mechanistic insights into the pathogenesis of HCM can be obtained from studying the ECM model. Therefore, in order to determine the similarities and differences in the pathology of ECM and HCM, we have performed the first spatial and quantitative histopathological assessment of the ECM syndrome. We demonstrate that the accumulation of parasitised red blood cells (pRBCs) in brain capillaries is a specific feature of ECM that is not observed during mild murine malaria infections. Critically, we show that individual pRBCs appear to occlude murine brain capillaries during ECM. As pRBC-mediated congestion of brain microvessels is a hallmark of HCM, this suggests that the impact of parasite accumulation on cerebral blood flow may ultimately be similar in mice and humans during ECM and HCM, respectively. Additionally, we demonstrate that cerebrovascular CD8+ T-cells appear to co-localise with accumulated pRBCs, an event that corresponds with development of widespread vascular leakage. As in HCM, we show that vascular leakage is not dependent on extensive vascular destruction. Instead, we show that vascular leakage is associated with alterations in transcellular and paracellular transport mechanisms. Finally, as in HCM, we observed axonal injury and demyelination in ECM adjacent to diverse vasculopathies. Collectively, our data therefore shows that, despite very different presentation, and apparently distinct mechanisms, of parasite accumulation, there appear to be a number of comparable features of cerebral pathology in mice and in humans during ECM and HCM, respectively. Thus, when used appropriately, the ECM model may be useful for studying specific pathological features of HCM.

  10. Mapping Functional Brain Development: Building a Social Brain through Interactive Specialization

    Science.gov (United States)

    Johnson, Mark H.; Grossmann, Tobias; Kadosh, Kathrin Cohen

    2009-01-01

    The authors review a viewpoint on human functional brain development, interactive specialization (IS), and its application to the emerging network of cortical regions referred to as the "social brain." They advance the IS view in 2 new ways. First, they extend IS into a domain to which it has not previously been applied--the emergence of social…

  11. Macroscopic networks in the human brain: mapping connectivity in healthy and damaged brains

    NARCIS (Netherlands)

    Nijhuis, E.H.J.

    2013-01-01

    The human brain contains a network of interconnected neurons. Recent advances in functional and structural in-vivo magnetic resonance neuroimaging (MRI) techniques have provided opportunities to model the networks of the human brain on a macroscopic scale. This dissertation investigates the

  12. Mapping social behavior-induced brain activation at cellular resolution in the mouse

    Science.gov (United States)

    Kim, Yongsoo; Venkataraju, Kannan Umadevi; Pradhan, Kith; Mende, Carolin; Taranda, Julian; Turaga, Srinivas C.; Arganda-Carreras, Ignacio; Ng, Lydia; Hawrylycz, Michael J.; Rockland, Kathleen; Seung, H. Sebastian; Osten, Pavel

    2014-01-01

    Understanding how brain activation mediates behaviors is a central goal of systems neuroscience. Here we apply an automated method for mapping brain activation in the mouse in order to probe how sex-specific social behaviors are represented in the male brain. Our method uses the immediate early gene c-fos, a marker of neuronal activation, visualized by serial two-photon tomography: the c-fos-GFP-positive neurons are computationally detected, their distribution is registered to a reference brain and a brain atlas, and their numbers are analyzed by statistical tests. Our results reveal distinct and shared female and male interaction-evoked patterns of male brain activation representing sex discrimination and social recognition. We also identify brain regions whose degree of activity correlates to specific features of social behaviors and estimate the total numbers and the densities of activated neurons per brain areas. Our study opens the door to automated screening of behavior-evoked brain activation in the mouse. PMID:25558063

  13. Proficient brain for optimal performance: the MAP model perspective.

    Science.gov (United States)

    Bertollo, Maurizio; di Fronso, Selenia; Filho, Edson; Conforto, Silvia; Schmid, Maurizio; Bortoli, Laura; Comani, Silvia; Robazza, Claudio

    2016-01-01

    Background. The main goal of the present study was to explore theta and alpha event-related desynchronization/synchronization (ERD/ERS) activity during shooting performance. We adopted the idiosyncratic framework of the multi-action plan (MAP) model to investigate different processing modes underpinning four types of performance. In particular, we were interested in examining the neural activity associated with optimal-automated (Type 1) and optimal-controlled (Type 2) performances. Methods. Ten elite shooters (6 male and 4 female) with extensive international experience participated in the study. ERD/ERS analysis was used to investigate cortical dynamics during performance. A 4 × 3 (performance types × time) repeated measures analysis of variance was performed to test the differences among the four types of performance during the three seconds preceding the shots for theta, low alpha, and high alpha frequency bands. The dependent variables were the ERD/ERS percentages in each frequency band (i.e., theta, low alpha, high alpha) for each electrode site across the scalp. This analysis was conducted on 120 shots for each participant in three different frequency bands and the individual data were then averaged. Results. We found ERS to be mainly associated with optimal-automatic performance, in agreement with the "neural efficiency hypothesis." We also observed more ERD as related to optimal-controlled performance in conditions of "neural adaptability" and proficient use of cortical resources. Discussion. These findings are congruent with the MAP conceptualization of four performance states, in which unique psychophysiological states underlie distinct performance-related experiences. From an applied point of view, our findings suggest that the MAP model can be used as a framework to develop performance enhancement strategies based on cognitive and neurofeedback techniques.

  14. Proficient brain for optimal performance: the MAP model perspective

    Directory of Open Access Journals (Sweden)

    Maurizio Bertollo

    2016-05-01

    Full Text Available Background. The main goal of the present study was to explore theta and alpha event-related desynchronization/synchronization (ERD/ERS activity during shooting performance. We adopted the idiosyncratic framework of the multi-action plan (MAP model to investigate different processing modes underpinning four types of performance. In particular, we were interested in examining the neural activity associated with optimal-automated (Type 1 and optimal-controlled (Type 2 performances. Methods. Ten elite shooters (6 male and 4 female with extensive international experience participated in the study. ERD/ERS analysis was used to investigate cortical dynamics during performance. A 4 × 3 (performance types × time repeated measures analysis of variance was performed to test the differences among the four types of performance during the three seconds preceding the shots for theta, low alpha, and high alpha frequency bands. The dependent variables were the ERD/ERS percentages in each frequency band (i.e., theta, low alpha, high alpha for each electrode site across the scalp. This analysis was conducted on 120 shots for each participant in three different frequency bands and the individual data were then averaged. Results. We found ERS to be mainly associated with optimal-automatic performance, in agreement with the “neural efficiency hypothesis.” We also observed more ERD as related to optimal-controlled performance in conditions of “neural adaptability” and proficient use of cortical resources. Discussion. These findings are congruent with the MAP conceptualization of four performance states, in which unique psychophysiological states underlie distinct performance-related experiences. From an applied point of view, our findings suggest that the MAP model can be used as a framework to develop performance enhancement strategies based on cognitive and neurofeedback techniques.

  15. Functional MR mapping of higher cognitive brain functions

    International Nuclear Information System (INIS)

    Bellemann, M.E.; Spitzer, M.; Brix, G.; Kammer, T.; Loose, R.; Schwartz, A.; Gueckel, F.

    1995-01-01

    Fifteen normal subjects were examined on a conventional 1.5-T MR system to visualize cortical activation during the performance of high-level cognitive tasks. A computer-controlled videoprojector was employed to present psychometrically optimized activation paradigms. Reaction times and error rates of the volunteers were acquired online during stimulus presentation. The time course of cortical activation was measured in a series of strongly T 2 *-weighted gradient-echo images from three or four adjacent slices. For anatomical correlation, picture elements showing a stimulus-related significant signal increase were color-coded and superimposed on T 1 -weighted spin-echo images. Analysis of the fMRI data revealed a subtle (range 2-5%), but statistically significant increase in signal intensity during the periods of induced cortical activation. Judgment of semantic relatedness of word pairs, for example, activated selectively cortical areas in left frontal and left temporal brain regions. The strength of cortex activation in the semantic task decreased significantly in the course of stimulus presentation and was paralleled by a decrease in the corresponding reaction times. With its move into the area of cognitive neuroscience, fMRI calls both for the careful design of activation schemes and for the acquisition of behavioral data. For example, brain regions involved in language processing could only be identified clearly when psychometrically matched activation paradigms were employed. The reaction time data correlated well with selective learning and thus helped to facilitate interpretation of the fMRI data sets. (orig.) [de

  16. Mapping Subcortical Brain Maturation during Adolescence: Evidence of Hemisphere-and Sex-Specific Longitudinal Changes

    Science.gov (United States)

    Dennison, Meg; Whittle, Sarah; Yücel, Murat; Vijayakumar, Nandita; Kline, Alexandria; Simmons, Julian; Allen, Nicholas B.

    2013-01-01

    Early to mid-adolescence is an important developmental period for subcortical brain maturation, but longitudinal studies of these neurodevelopmental changes are lacking. The present study acquired repeated magnetic resonance images from 60 adolescent subjects (28 female) at ages 12.5 and 16.5 years to map changes in subcortical structure volumes.…

  17. Images Are Not the (Only) Truth: Brain Mapping, Visual Knowledge, and Iconoclasm.

    Science.gov (United States)

    Beaulieu, Anne

    2002-01-01

    Debates the paradoxical nature of claims about the emerging contributions of functional brain mapping. Examines the various ways that images are deployed and rejected and highlights an approach that provides insight into the current demarcation of imaging. (Contains 68 references.) (DDR)

  18. High-throughput mapping of brain-wide activity in awake and drug-responsive vertebrates.

    Science.gov (United States)

    Lin, Xudong; Wang, Shiqi; Yu, Xudong; Liu, Zhuguo; Wang, Fei; Li, Wai Tsun; Cheng, Shuk Han; Dai, Qiuyun; Shi, Peng

    2015-02-07

    The reconstruction of neural activity across complete neural circuits, or brain activity mapping, has great potential in both fundamental and translational neuroscience research. Larval zebrafish, a vertebrate model, has recently been demonstrated to be amenable to whole brain activity mapping in behaving animals. Here we demonstrate a microfluidic array system ("Fish-Trap") that enables high-throughput mapping of brain-wide activity in awake larval zebrafish. Unlike the commonly practiced larva-processing methods using a rigid gel or a capillary tube, which are laborious and time-consuming, the hydrodynamic design of our microfluidic chip allows automatic, gel-free, and anesthetic-free processing of tens of larvae for microscopic imaging with single-cell resolution. Notably, this system provides the capability to directly couple pharmaceutical stimuli with real-time recording of neural activity in a large number of animals, and the local and global effects of pharmacoactive drugs on the nervous system can be directly visualized and evaluated by analyzing drug-induced functional perturbation within or across different brain regions. Using this technology, we tested a set of neurotoxin peptides and obtained new insights into how to exploit neurotoxin derivatives as therapeutic agents. The novel and versatile "Fish-Trap" technology can be readily unitized to study other stimulus (optical, acoustic, or physical) associated functional brain circuits using similar experimental strategies.

  19. Whole brain diffeomorphic metric mapping via integration of sulcal and gyral curves, cortical surfaces, and images

    Science.gov (United States)

    Du, Jia; Younes, Laurent; Qiu, Anqi

    2011-01-01

    This paper introduces a novel large deformation diffeomorphic metric mapping algorithm for whole brain registration where sulcal and gyral curves, cortical surfaces, and intensity images are simultaneously carried from one subject to another through a flow of diffeomorphisms. To the best of our knowledge, this is the first time that the diffeomorphic metric from one brain to another is derived in a shape space of intensity images and point sets (such as curves and surfaces) in a unified manner. We describe the Euler–Lagrange equation associated with this algorithm with respect to momentum, a linear transformation of the velocity vector field of the diffeomorphic flow. The numerical implementation for solving this variational problem, which involves large-scale kernel convolution in an irregular grid, is made feasible by introducing a class of computationally friendly kernels. We apply this algorithm to align magnetic resonance brain data. Our whole brain mapping results show that our algorithm outperforms the image-based LDDMM algorithm in terms of the mapping accuracy of gyral/sulcal curves, sulcal regions, and cortical and subcortical segmentation. Moreover, our algorithm provides better whole brain alignment than combined volumetric and surface registration (Postelnicu et al., 2009) and hierarchical attribute matching mechanism for elastic registration (HAMMER) (Shen and Davatzikos, 2002) in terms of cortical and subcortical volume segmentation. PMID:21281722

  20. Brain Oscillatory and Hemodynamic Activity in a Bimanual Coordination Task Following Transcranial Alternating Current Stimulation (tACS): A Combined EEG-fNIRS Study.

    Science.gov (United States)

    Berger, Alisa; Pixa, Nils H; Steinberg, Fabian; Doppelmayr, Michael

    2018-01-01

    Motor control is associated with synchronized oscillatory activity at alpha (8-12 Hz) and beta (12-30 Hz) frequencies in a cerebello-thalamo-cortical network. Previous studies demonstrated that transcranial alternating current stimulation (tACS) is capable of entraining ongoing oscillatory activity while also modulating motor control. However, the modulatory effects of tACS on both motor control and its underlying electro- and neurophysiological mechanisms remain ambiguous. Thus, the purpose of this study was to contribute to gathering neurophysiological knowledge regarding tACS effects by investigating the after-effects of 10 Hz tACS and 20 Hz tACS at parietal brain areas on bimanual coordination and its concurrent oscillatory and hemodynamic activity. Twenty-four right-handed healthy volunteers (12 females) aged between 18 and 30 ( M = 22.35 ± 3.62) participated in the study and performed a coordination task requiring bimanual movements. Concurrent to bimanual motor training, participants received either 10 Hz tACS, 20 Hz tACS or a sham stimulation over the parietal cortex (at P3/P4 electrode positions) for 20 min via small gel electrodes (3,14 cm 2 Ag/AgCl, amperage = 1 mA). Before and three time-points after tACS (immediately, 30 min and 1 day), bimanual coordination performance was assessed. Oscillatory activities were measured by electroencephalography (EEG) and hemodynamic changes were examined using functional near-infrared spectroscopy (fNIRS). Improvements of bimanual coordination performance were not differently between groups, thus, no tACS-specific effect on bimanual coordination performance emerged. However, physiological measures during the task revealed significant increases in parietal alpha activity immediately following 10 Hz tACS and 20 Hz tACS which were accompanied by significant decreases of Hboxy concentration in the right hemispheric motor cortex compared to the sham group. Based on the physiological responses, we conclude that t

  1. Brain Oscillatory and Hemodynamic Activity in a Bimanual Coordination Task Following Transcranial Alternating Current Stimulation (tACS: A Combined EEG-fNIRS Study

    Directory of Open Access Journals (Sweden)

    Alisa Berger

    2018-04-01

    Full Text Available Motor control is associated with synchronized oscillatory activity at alpha (8–12 Hz and beta (12–30 Hz frequencies in a cerebello-thalamo-cortical network. Previous studies demonstrated that transcranial alternating current stimulation (tACS is capable of entraining ongoing oscillatory activity while also modulating motor control. However, the modulatory effects of tACS on both motor control and its underlying electro- and neurophysiological mechanisms remain ambiguous. Thus, the purpose of this study was to contribute to gathering neurophysiological knowledge regarding tACS effects by investigating the after-effects of 10 Hz tACS and 20 Hz tACS at parietal brain areas on bimanual coordination and its concurrent oscillatory and hemodynamic activity. Twenty-four right-handed healthy volunteers (12 females aged between 18 and 30 (M = 22.35 ± 3.62 participated in the study and performed a coordination task requiring bimanual movements. Concurrent to bimanual motor training, participants received either 10 Hz tACS, 20 Hz tACS or a sham stimulation over the parietal cortex (at P3/P4 electrode positions for 20 min via small gel electrodes (3,14 cm2 Ag/AgCl, amperage = 1 mA. Before and three time-points after tACS (immediately, 30 min and 1 day, bimanual coordination performance was assessed. Oscillatory activities were measured by electroencephalography (EEG and hemodynamic changes were examined using functional near-infrared spectroscopy (fNIRS. Improvements of bimanual coordination performance were not differently between groups, thus, no tACS-specific effect on bimanual coordination performance emerged. However, physiological measures during the task revealed significant increases in parietal alpha activity immediately following 10 Hz tACS and 20 Hz tACS which were accompanied by significant decreases of Hboxy concentration in the right hemispheric motor cortex compared to the sham group. Based on the physiological responses, we conclude that

  2. Volumetric B1 (+) mapping of the brain at 7T using DREAM.

    Science.gov (United States)

    Nehrke, Kay; Versluis, Maarten J; Webb, Andrew; Börnert, Peter

    2014-01-01

    To tailor and optimize the Dual Refocusing Echo Acquisition Mode (DREAM) approach for volumetric B1 (+) mapping of the brain at 7T. A new DREAM echo timing scheme based on the virtual stimulated echo was derived to minimize potential effects of transverse relaxation. Furthermore, the DREAM B1 (+) mapping performance was investigated in simulations and experimentally in phantoms and volunteers for volumetric applications, studying and optimizing the accuracy of the sequence with respect to saturation effects, slice profile imperfections, and T1 and T2 relaxation. Volumetric brain protocols were compiled for different isotropic resolutions (5-2.5 mm) and SENSE factors, and were studied in vivo for different RF drive modes (circular/linear polarization) and the application of dielectric pads. Volumetric B1 (+) maps with good SNR at 2.5 mm isotropic resolution were acquired in about 20 s or less. The specific absorption rate was well below the safety limits for all scans. Mild flow artefacts were observed in the large vessels. Moreover, a slight contrast in the ventricle was observed in the B1 (+) maps, which could be attributed to T1 and T2 relaxation effects. DREAM enables safe, very fast, and robust volumetric B1 (+) mapping of the brain at ultrahigh fields. Copyright © 2013 Wiley Periodicals, Inc.

  3. Comparison of ADC map with trace map in the normal and infarct areas of the brains of stroke patients

    International Nuclear Information System (INIS)

    Kim, Seung Hyung; Yoon, Pyeong Ho; Jeong, Eun Kee; Oh, Young Taick; Kim, Dong Ik

    1999-01-01

    To compare ADC mapping with trace mapping in normal and infarct areas of the brains of stroke patients. Eighteen patients diagnosed on the basis of clinical and brain MRI examinations as suffering from brain infarction were included in this study (hyperacute-1, acute-4, subacute-12, chronic-1). Diffusion weighted images of three orthogonal directions of a patient's brain were obtained by means of a single shot EPI pulse sequence, using a diffusion gradient with four serial b-factors. Three ADC maps were then reconstructed by post-image processing and were summed pixel by pixel to yield a trace map. ROIs were selected in the normal areas of white matter, gray matter and CSF of one hemisphere, and other ROIs of the same size were selected at the same site of the contralateral hemisphere. ADC and trace values were measured and right/left ratios of ADC and trace values were calculated. Using these values, we then compared the ADC map with the trace map, and compared the degree of anisotropic diffusion between white matter, gray matter and CSF. Except for three, whose infarct lesions were small and lay over white and gray matter, patients were divided into two groups. Those with infarct in the white matter (n=10) were assigned to one group, and those with infarct in the gray matter (n=5) to the other. ROIs were selected in the infarct area and other ROIs of the same size were selected at the same site of the contralateral hemisphere. ADC and trace values were measured and infarct/contralateral ratios were calculated. We then compared ADC ratio with trace ratio in white matter and gray matter infarct. In normal white matter, the Dxx ratio was 0.980±0.098, the Dyy ratio 1.019±0.086, the Dzz ratio 0.999±0.111, and the trace ratio 0.995±0.031. In normal gray matter, the Dxx ratio was 1.001±0.058, the Dyy ratio 0.996±0.063, Dzz ratio 1.005±0.070, and the trace ratio 1.001±0.028. In CSF, the Dxx ratio was 1.002±0.064, the Dyy ratio 1.023±0.055, the Dzz ratio 0.999

  4. Stable long-term chronic brain mapping at the single-neuron level.

    Science.gov (United States)

    Fu, Tian-Ming; Hong, Guosong; Zhou, Tao; Schuhmann, Thomas G; Viveros, Robert D; Lieber, Charles M

    2016-10-01

    Stable in vivo mapping and modulation of the same neurons and brain circuits over extended periods is critical to both neuroscience and medicine. Current electrical implants offer single-neuron spatiotemporal resolution but are limited by such factors as relative shear motion and chronic immune responses during long-term recording. To overcome these limitations, we developed a chronic in vivo recording and stimulation platform based on flexible mesh electronics, and we demonstrated stable multiplexed local field potentials and single-unit recordings in mouse brains for at least 8 months without probe repositioning. Properties of acquired signals suggest robust tracking of the same neurons over this period. This recording and stimulation platform allowed us to evoke stable single-neuron responses to chronic electrical stimulation and to carry out longitudinal studies of brain aging in freely behaving mice. Such advantages could open up future studies in mapping and modulating changes associated with learning, aging and neurodegenerative diseases.

  5. Assessing Mild Cognitive Impairment Progression using a Spherical Brain Mapping of Magnetic Resonance Imaging.

    Science.gov (United States)

    Martinez-Murcia, Francisco Jesus; Górriz, Juan Manuel; Ramírez, Javier; Segovia, Fermín; Salas-Gonzalez, Diego; Castillo-Barnes, Diego; Ortiz, Andrés

    2018-04-04

    The early diagnosis of Alzheimer's Disease (AD), particularly in its prodromal stage, mild cognitive impairment (MCI), still remains a challenge. Many computational tools have been developed to successfully explore and predict the disease progression. In this context, the Spherical Brain Mapping (SBM) proved its ability in detecting differences between AD and aged subjects without symptoms of dementia. Being a very visual tool, its application in predicting MCI conversion to AD could be of great help to understand neurodegeneration and the disease progression. In this work, we aim at predicting the conversion of MCI affected subjects to AD more than 6 months in advance of their conversion session and understanding the progression of the disease by predicting neuropsychological test outcomes from MRI data. In order to do so, SBM is applied to a series of MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The resulting spherical brain maps show statistical and morphological information of the brain in a bidimensional plane, performing at the same time a significant feature reduction that provides a feature vector used in classification analysis. The study achieves up to 92.3% accuracy in the AD versus normal controls (CTL) detection, and up to a 77.6% in detection a of MCI conversions when trained with AD and CTL subjects. The prediction of neuropsychological test outcomes achieved R2 rates up to more than 0.5. Significant regions according to t-test and correlation analysis match reported brain areas in the literature. The results prove that Spherical Brain Mapping offers good ability to predict conversion patterns and cognitive state, at the same time that provides an additional aid for visualizing a two-dimensional abstraction map of the brain.

  6. Spatial cluster analysis of nanoscopically mapped serotonin receptors for classification of fixed brain tissue

    Science.gov (United States)

    Sams, Michael; Silye, Rene; Göhring, Janett; Muresan, Leila; Schilcher, Kurt; Jacak, Jaroslaw

    2014-01-01

    We present a cluster spatial analysis method using nanoscopic dSTORM images to determine changes in protein cluster distributions within brain tissue. Such methods are suitable to investigate human brain tissue and will help to achieve a deeper understanding of brain disease along with aiding drug development. Human brain tissue samples are usually treated postmortem via standard fixation protocols, which are established in clinical laboratories. Therefore, our localization microscopy-based method was adapted to characterize protein density and protein cluster localization in samples fixed using different protocols followed by common fluorescent immunohistochemistry techniques. The localization microscopy allows nanoscopic mapping of serotonin 5-HT1A receptor groups within a two-dimensional image of a brain tissue slice. These nanoscopically mapped proteins can be confined to clusters by applying the proposed statistical spatial analysis. Selected features of such clusters were subsequently used to characterize and classify the tissue. Samples were obtained from different types of patients, fixed with different preparation methods, and finally stored in a human tissue bank. To verify the proposed method, samples of a cryopreserved healthy brain have been compared with epitope-retrieved and paraffin-fixed tissues. Furthermore, samples of healthy brain tissues were compared with data obtained from patients suffering from mental illnesses (e.g., major depressive disorder). Our work demonstrates the applicability of localization microscopy and image analysis methods for comparison and classification of human brain tissues at a nanoscopic level. Furthermore, the presented workflow marks a unique technological advance in the characterization of protein distributions in brain tissue sections.

  7. On initial Brain Activity Mapping of episodic and semantic memory code in the hippocampus.

    Science.gov (United States)

    Tsien, Joe Z; Li, Meng; Osan, Remus; Chen, Guifen; Lin, Longian; Wang, Phillip Lei; Frey, Sabine; Frey, Julietta; Zhu, Dajiang; Liu, Tianming; Zhao, Fang; Kuang, Hui

    2013-10-01

    It has been widely recognized that the understanding of the brain code would require large-scale recording and decoding of brain activity patterns. In 2007 with support from Georgia Research Alliance, we have launched the Brain Decoding Project Initiative with the basic idea which is now similarly advocated by BRAIN project or Brain Activity Map proposal. As the planning of the BRAIN project is currently underway, we share our insights and lessons from our efforts in mapping real-time episodic memory traces in the hippocampus of freely behaving mice. We show that appropriate large-scale statistical methods are essential to decipher and measure real-time memory traces and neural dynamics. We also provide an example of how the carefully designed, sometime thinking-outside-the-box, behavioral paradigms can be highly instrumental to the unraveling of memory-coding cell assembly organizing principle in the hippocampus. Our observations to date have led us to conclude that the specific-to-general categorical and combinatorial feature-coding cell assembly mechanism represents an emergent property for enabling the neural networks to generate and organize not only episodic memory, but also semantic knowledge and imagination. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

  8. Brain-wide Maps Reveal Stereotyped Cell-Type-Based Cortical Architecture and Subcortical Sexual Dimorphism.

    Science.gov (United States)

    Kim, Yongsoo; Yang, Guangyu Robert; Pradhan, Kith; Venkataraju, Kannan Umadevi; Bota, Mihail; García Del Molino, Luis Carlos; Fitzgerald, Greg; Ram, Keerthi; He, Miao; Levine, Jesse Maurica; Mitra, Partha; Huang, Z Josh; Wang, Xiao-Jing; Osten, Pavel

    2017-10-05

    The stereotyped features of neuronal circuits are those most likely to explain the remarkable capacity of the brain to process information and govern behaviors, yet it has not been possible to comprehensively quantify neuronal distributions across animals or genders due to the size and complexity of the mammalian brain. Here we apply our quantitative brain-wide (qBrain) mapping platform to document the stereotyped distributions of mainly inhibitory cell types. We discover an unexpected cortical organizing principle: sensory-motor areas are dominated by output-modulating parvalbumin-positive interneurons, whereas association, including frontal, areas are dominated by input-modulating somatostatin-positive interneurons. Furthermore, we identify local cell type distributions with more cells in the female brain in 10 out of 11 sexually dimorphic subcortical areas, in contrast to the overall larger brains in males. The qBrain resource can be further mined to link stereotyped aspects of neuronal distributions to known and unknown functions of diverse brain regions. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Electroencephalograph (EEG) study on self-contemplating image formation

    Science.gov (United States)

    Meng, Qinglei; Hong, Elliot; Choa, Fow-Sen

    2016-05-01

    Electroencephalography (EEG) is one of the most widely used electrophysiological monitoring methods and plays a significant role in studies of human brain electrical activities. Default mode network (DMN), is a functional connection of brain regions that are activated while subjects are not in task positive state or not focused on the outside world. In this study, EEG was used for human brain signals recording while all subjects were asked to sit down quietly on a chair with eyes closed and thinking about some parts of their own body, such as left and right hands, left and right ears, lips, nose, and the images of faces that they were familiar with as well as doing some simple mathematical calculation. The time is marker when the image is formed in the subject's mind. By analyzing brain activity maps 300ms right before the time marked instant for each of the 4 wave bands, Delta, Theta, Alpha and Beta waves. We found that for most EEG datasets during this 300ms, Delta wave activity would mostly locate at the frontal lobe or the visual cortex, and the change and movement of activities are slow. Theta wave activity tended to rotate along the edge of cortex either clockwise or counterclockwise. Beta wave behaved like inquiry types of oscillations between any two regions spread over the cortex. Alpha wave activity looks like a mix of the Theta and Beta activities but more close to Theta activity. From the observation we feel that Beta and high Alpha are playing utility role for information inquiry. Theta and low Alpha are likely playing the role of binding and imagination formation in DMN operations.

  10. Dual-EEG of joint finger tapping: what can two interacting brains teach us about social interaction?

    DEFF Research Database (Denmark)

    Konvalinka, Ivana

    -frequency analysis revealed a left-motor and right-frontal suppression at 10 Hz during task execution, when carrying the task out interactively in contrast with the uncoupled computer-driven task. We used machine-learning approaches to identify the brain signals driving the interaction. The raw-power at 10 Hz during...... tapping emanating from electrodes of member one and member two were used as features. We combined data from both participants in each pair, and applied logistic regression using feature selection in order to classify the two conditions. The first seven (frontal) electrodes consistently emerged as good...... classifiers, with 85-99% accuracy. There was a tendency for one member’s frontal electrodes to drive the classifier over the other’s, which predicted the leader of the interaction in 8/9 pairs. This study reveals new neural mechanisms underlying two-person interactions. It also shows how analyzing two...

  11. Study on non-linear bistable dynamics model based EEG signal discrimination analysis method.

    Science.gov (United States)

    Ying, Xiaoguo; Lin, Han; Hui, Guohua

    2015-01-01

    Electroencephalogram (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations generating from ionic current flows within the neurons of the brain. EEG signal is looked as one of the most important factors that will be focused in the next 20 years. In this paper, EEG signal discrimination based on non-linear bistable dynamical model was proposed. EEG signals were processed by non-linear bistable dynamical model, and features of EEG signals were characterized by coherence index. Experimental results showed that the proposed method could properly extract the features of different EEG signals.

  12. Contribution of EEG in transient neurological deficits.

    Science.gov (United States)

    Lozeron, Pierre; Tcheumeni, Nadine Carole; Turki, Sahar; Amiel, Hélène; Meppiel, Elodie; Masmoudi, Sana; Roos, Caroline; Crassard, Isabelle; Plaisance, Patrick; Benbetka, Houria; Guichard, Jean-Pierre; Houdart, Emmanuel; Baudoin, Hélène; Kubis, Nathalie

    2018-01-01

    Identification of stroke mimics and 'chameleons' among transient neurological deficits (TND) is critical. Diagnostic workup consists of a brain imaging study, for a vascular disease or a brain tumour and EEG, for epileptiform discharges. The precise role of EEG in this diagnostic workup has, however, never been clearly delineated. However, this could be crucial in cases of atypical or incomplete presentation with consequences on disease management and treatment. We analysed the EEG patterns on 95 consecutive patients referred for an EEG within 7 days of a TND with diagnostic uncertainty. Patients were classified at the discharge or the 3-month follow-up visit as: 'ischemic origin', 'migraine aura', 'focal seizure', and 'other'. All patients had a brain imaging study. EEG characteristics were correlated to the TND symptoms, imaging study, and final diagnosis. Sixty four (67%) were of acute onset. Median symptom duration was 45 min. Thirty two % were 'ischemic', 14% 'migraine aura', 19% 'focal seizure', and 36% 'other' cause. EEGs were recorded with a median delay of 1.6 day after symptoms onset. Forty EEGs (42%) were abnormal. Focal slow waves were the most common finding (43%), also in the ischemic group (43%), whether patients had a typical presentation or not. Epileptiform discharges were found in three patients, one with focal seizure and two with migraine aura. Non-specific EEG focal slowing is commonly found in TND, and may last several days. We found no difference in EEG presentation between stroke mimics and stroke chameleons, and between other diagnoses.

  13. Deep brain stimulation of the nucleus basalis of Meynert attenuates early EEG components associated with defective sensory gating in patients with Alzheimer disease - a two-case study.

    Science.gov (United States)

    Dürschmid, Stefan; Reichert, Christoph; Kuhn, Jens; Freund, Hans-Joachim; Hinrichs, Hermann; Heinze, Hans-Jochen

    2017-10-20

    Alzheimer's disease (AD) is associated with deterioration of memory and cognitive function and a degeneration of neurons of the nucleus basalis of Meynert (NBM). The NBM is the major input source of acetylcholine (ACh) to the cortex. The decreasing cholinergic innervation of the cortex due to degeneration of the NBM might be the cause of loss of memory function. NBM-Deep brain stimulation (NBM-DBS) is considered to serve as a potential therapeutic option for patients with AD by supporting residual cholinergic transmission to stabilize oscillatory activity in memory-relevant circuits. However, whether DBS could improve sensory memory functions in patients with AD is not clear. Here, in a passive auditory oddball paradigm, patients with AD (N = 2) listened to repetitive background tones (standard tones) randomly interrupted by frequency deviants in two blocks with NBM-DBS OFF and then NBM-DBS ON, while age-matched healthy controls (N = 6) repeated the experiment twice. The mismatch negativity in NBM-DBS OFF significantly differed from controls in both blocks, but not under NBM-DBS, which was likely due to a pronounced P50 increase overlapping with the N1 in NBM-DBS OFF. This early complex of EEG components recovered under stimulation to a normal level as defined by responses in controls. In this temporal interval, we found in patients with NBM-DBS ON (but not with NBM-DBS OFF) and in controls a strong repetition suppression effect to standard tones - with more attenuated responses to frequently repeated standard tones. This highlights the role of NBM-DBS for sensory gating of familiar auditory information into sensory memory. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  14. [Quantitative magnetic resonance imaging of brain iron deposition: comparison between quantitative susceptibility mapping and transverse relaxation rate (R2*) mapping].

    Science.gov (United States)

    Guan, Ji-Jing; Feng, Yan-Qiu

    2018-03-20

    To evaluate the accuracy and sensitivity of quantitative susceptibility mapping (QSM) and transverse relaxation rate (R2*) mapping in the measurement of brain iron deposition. Super paramagnetic iron oxide (SPIO) phantoms and mouse models of Parkinson's disease (PD) related to iron deposition in the substantia nigra (SN) underwent 7.0 T magnetic resonance (MR) scans (Bruker, 70/16) with a multi-echo 3D gradient echo sequence, and the acquired data were processed to obtain QSM and R2*. Linear regression analysis was performed for susceptibility and R2* in the SPIO phantoms containing 5 SPIO concentrations (30, 15, 7.5, 3.75 and 1.875 µg/mL) to evaluate the accuracy of QSM and R2* in quantitative iron analysis. The sensitivities of QSM and R2* mapping in quantitative detection of brain iron deposition were assessed using mouse models of PD induced by 1-methyl-4-phenyl-1,2,3,6-tetrahy-dropyridine (MPTP) in comparison with the control mice. In SPIO phantoms, QSM provided a higher accuracy than R2* mapping and their goodness-of-fit coefficients (R 2 ) were 0.98 and 0.89, respectively. In the mouse models of PD and control mice, the susceptibility of the SN was significantly higher in the PD models (5.19∓1.58 vs 2.98∓0.88, n=5; Pbrain iron deposition than R2*, and the susceptibility derived by QSM can be a potentially useful biomarker for studying PD.

  15. Mapping the brain network of the phonological loop.

    Science.gov (United States)

    Papagno, Costanza; Comi, Alessandro; Riva, Marco; Bizzi, Alberto; Vernice, Mirta; Casarotti, Alessandra; Fava, Enrica; Bello, Lorenzo

    2017-06-01

    The cortical and subcortical neural correlates underlying item and order information in verbal short-term memory (STM) were investigated by means of digit span in 29 patients with direct electrical stimulation during awake surgery for removal of a neoplastic lesion. Stimulation of left Broca's area interfered with span, producing significantly more item than order errors, as compared to the stimulation of the supramarginal/angular gyrus, which also interfered with span but, conversely, produced more order than item errors. Similarly, stimulation of the third segment of the left superior longitudinal fasciculus (SLF-III), also known as anterior segment of the arcuate fascicle (AF), produced more order than item errors. Therefore, we obtained two crucial results: first, we were able to distinguish between content and order information storage. Second, we demonstrated that the SLF-III is involved in transferring order information from Geschwind's area to Broca's area. In a few patients, we demonstrated that also order information of nonverbal material was disrupted by left supramarginal gyrus stimulation. Order information is thus likely stored in the supramarginal gyrus, possibly independently from the nature of the material. Hum Brain Mapp 38:3011-3024, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  16. fMRI activation patterns in an analytic reasoning task: consistency with EEG source localization

    Science.gov (United States)

    Li, Bian; Vasanta, Kalyana C.; O'Boyle, Michael; Baker, Mary C.; Nutter, Brian; Mitra, Sunanda

    2010-03-01

    Functional magnetic resonance imaging (fMRI) is used to model brain activation patterns associated with various perceptual and cognitive processes as reflected by the hemodynamic (BOLD) response. While many sensory and motor tasks are associated with relatively simple activation patterns in localized regions, higher-order cognitive tasks may produce activity in many different brain areas involving complex neural circuitry. We applied a recently proposed probabilistic independent component analysis technique (PICA) to determine the true dimensionality of the fMRI data and used EEG localization to identify the common activated patterns (mapped as Brodmann areas) associated with a complex cognitive task like analytic reasoning. Our preliminary study suggests that a hybrid GLM/PICA analysis may reveal additional regions of activation (beyond simple GLM) that are consistent with electroencephalography (EEG) source localization patterns.

  17. EEG applications for sport and performance.

    Science.gov (United States)

    Thompson, Trevor; Steffert, Tony; Ros, Tomas; Leach, Joseph; Gruzelier, John

    2008-08-01

    One approach to understanding processes that underlie skilled performing has been to study electrical brain activity using electroencephalography (EEG). A notorious problem with EEG is that genuine cerebral data is often contaminated by artifacts of non-cerebral origin. Unfortunately, such artifacts tend to be exacerbated when the subject is in motion, meaning that obtaining reliable data during exercise is inherently problematic. These problems may explain the limited number of studies using EEG as a methodological tool in the sports sciences. This paper discusses how empirical studies have generally tackled the problem of movement artifact by adopting alternative paradigms which avoid recording during actual physical exertion. Moreover, the specific challenges that motion presents to obtaining reliable EEG data are discussed along with practical and computational techniques to confront these challenges. Finally, as EEG recording in sports is often underpinned by a desire to optimise performance, a brief review of EEG-biofeedback and peak performance studies is also presented. A knowledge of practical aspects of EEG recording along with the advent of new technology and increasingly sophisticated processing models offer a promising approach to minimising, if perhaps not entirely circumventing, the problem of obtaining reliable EEG data during motion.

  18. Brain single-photon emission tomography with {sup 99m}Tc-HMPAO in neuropsychiatric systemic lupus erythematosus: relations with EEG and MRI findings and clinical manifestations

    Energy Technology Data Exchange (ETDEWEB)

    Colamussi, P. [Dept. of Nuclear Medicine, Univ. of Ferrara (Italy); Giganti, M. [Dept. of Nuclear Medicine, Univ. of Ferrara (Italy); Cittanti, C. [Dept. of Nuclear Medicine, Univ. of Ferrara (Italy); Dovigo, L. [Inst. of Neurology, Univ. of Ferrara (Italy); Trotta, F. [Inst. of Neurology, Univ. of Ferrara (Italy); Tola, M.R. [Div. of Rheumatology, S. Anna Hospital, Ferrara (Italy); Tamarozzi, R. [Radiology Dept., S. Anna Hospital, Ferrara (Italy); Lucignani, G. [INB-CNR Dept. of Nuclear Medicine, H.S. Raffaele, Milan (Italy); Piffanelli, A. [Dept. of Nuclear Medicine, Univ. of Ferrara (Italy)

    1995-01-01

    In the reported study the role of single-photon emission tomography (SPET) with technetium-99m hexamethylpropylene amine oxime (HMPAO) in the evaluation of CNS involvement in SLE was assessed and the relations between SPET perfusion defects, EEG examination, magnetic resonance imaging (MRI) findings and clinical presentation were examined. Twenty SLE patients with different NP manifestations were studied. Multiple areas of hypoperfusion, especially in the territory of the middle cerebral artery, were demonstrated by SPET analysis in all 20 patients. The number of hypoperfused areas and the degree of hypoperfusion, expressed by an asymmetry index (AI), were more marked in patients with multiple NP manifestations. MRI and EEG evaluations were positive for 14 of 18 and for 12 of 20 patients, respectively. In the patients with positive SPET and MRI, 87 MRI focal lesions and 63 hypoperfused areas were found, and for 51 of these 63 at least one MRI lesion was found in the same anatomical region. SPET examination of patients with a normal EEG showed fewer hypoperfused areas and a lower degree of asymmetry compared to patients with an abnormal EEG. SPET of patients with focal EEG abnormalities showed more hypoperfused areas (difference not statistically significant) and a higher AI than did SPET of the patients with diffuse EEG abnormalities. Seven of 11 anatomical regions with focal EEG abnormalities. Seven of 11 anatomical regions with focal EEG abnormalities had co-localized hypoperfused areas and in two of these seven no detectable MRI lesions were found. The analysis of SPET and NP manifestations showed that 12 of 20 patients had at least one positive correlation, always involving the areas with the highest AI. In total, 51/88 (58%) hypoperfused areas correlated with the MRI findings and 31/88 (35%) with NP manifestations; for seven of the latter no concurrent MRI lesions were detected in the same anatomical region. (orig.)

  19. Brain single-photon emission tomography with 99mTc-HMPAO in neuropsychiatric systemic lupus erythematosus: relations with EEG and MRI findings and clinical manifestations

    International Nuclear Information System (INIS)

    Colamussi, P.; Giganti, M.; Cittanti, C.; Dovigo, L.; Trotta, F.; Tola, M.R.; Tamarozzi, R.; Lucignani, G.; Piffanelli, A.

    1995-01-01

    In the reported study the role of single-photon emission tomography (SPET) with technetium-99m hexamethylpropylene amine oxime (HMPAO) in the evaluation of CNS involvement in SLE was assessed and the relations between SPET perfusion defects, EEG examination, magnetic resonance imaging (MRI) findings and clinical presentation were examined. Twenty SLE patients with different NP manifestations were studied. Multiple areas of hypoperfusion, especially in the territory of the middle cerebral artery, were demonstrated by SPET analysis in all 20 patients. The number of hypoperfused areas and the degree of hypoperfusion, expressed by an asymmetry index (AI), were more marked in patients with multiple NP manifestations. MRI and EEG evaluations were positive for 14 of 18 and for 12 of 20 patients, respectively. In the patients with positive SPET and MRI, 87 MRI focal lesions and 63 hypoperfused areas were found, and for 51 of these 63 at least one MRI lesion was found in the same anatomical region. SPET examination of patients with a normal EEG showed fewer hypoperfused areas and a lower degree of asymmetry compared to patients with an abnormal EEG. SPET of patients with focal EEG abnormalities showed more hypoperfused areas (difference not statistically significant) and a higher AI than did SPET of the patients with diffuse EEG abnormalities. Seven of 11 anatomical regions with focal EEG abnormalities. Seven of 11 anatomical regions with focal EEG abnormalities had co-localized hypoperfused areas and in two of these seven no detectable MRI lesions were found. The analysis of SPET and NP manifestations showed that 12 of 20 patients had at least one positive correlation, always involving the areas with the highest AI. In total, 51/88 (58%) hypoperfused areas correlated with the MRI findings and 31/88 (35%) with NP manifestations; for seven of the latter no concurrent MRI lesions were detected in the same anatomical region. (orig.)

  20. PENGEMBANGAN ALAT BANTU PEMODELAN TERAPI LENGAN PASCA STROKE DENGAN MEMANFAATKAN SINYAL ELECTROENCEPHALOGRAPHY (EEG) MENGGUNAKAN EMOTIV

    OpenAIRE

    Fatmawati, Ester; Prawito, Prawito; Wijaya, Sastra Kusuma

    2016-01-01

    Design modeling has been done post-stroke therapy arm by utilizing command brain signals generated by Electroencephalography (EEG). EEG signals provides a lot of information, one of which is motor information. Every body moving describe the unique form of brain signals. In conditions paralysis, motor information on the EEG signals will still be found when someone tries to move his limbs. The basic concepts of this study are the EEG signal acquisition using the Emotiv EPOC +, controling signal...

  1. The average baboon brain: MRI templates and tissue probability maps from 89 individuals.

    Science.gov (United States)

    Love, Scott A; Marie, Damien; Roth, Muriel; Lacoste, Romain; Nazarian, Bruno; Bertello, Alice; Coulon, Olivier; Anton, Jean-Luc; Meguerditchian, Adrien

    2016-05-15

    The baboon (Papio) brain is a remarkable model for investigating the brain. The current work aimed at creating a population-average baboon (Papio anubis) brain template and its left/right hemisphere symmetric version from a large sample of T1-weighted magnetic resonance images collected from 89 individuals. Averaging the prior probability maps output during the segmentation of each individual also produced the first baboon brain tissue probability maps for gray matter, white matter and cerebrospinal fluid. The templates and the tissue probability maps were created using state-of-the-art, freely available software tools and are being made freely and publicly available: http://www.nitrc.org/projects/haiko89/ or http://lpc.univ-amu.fr/spip.php?article589. It is hoped that these images will aid neuroimaging research of the baboon by, for example, providing a modern, high quality normalization target and accompanying standardized coordinate system as well as probabilistic priors that can be used during tissue segmentation. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. Brain Activity Related to the Judgment of Face-Likeness: Correlation between EEG and Face-Like Evaluation.

    Science.gov (United States)

    Nihei, Yuji; Minami, Tetsuto; Nakauchi, Shigeki

    2018-01-01

    Faces represent important information for social communication, because social information, such as face-color, expression, and gender, is obtained from faces. Therefore, individuals' tend to find faces unconsciously, even in objects. Why is face-likeness perceived in non-face objects? Previous event-related potential (ERP) studies showed that the P1 component (early visual processing), the N170 component (face detection), and the N250 component (personal detection) reflect the neural processing of faces. Inverted faces were reported to enhance the amplitude and delay the latency of P1 and N170. To investigate face-likeness processing in the brain, we explored the face-related components of the ERP through a face-like evaluation task using natural faces, cars, insects, and Arcimboldo paintings presented upright or inverted. We found a significant correlation between the inversion effect index and face-like scores in P1 in both hemispheres and in N170 in the right hemisphere. These results suggest that judgment of face-likeness occurs in a relatively early stage of face processing.

  3. Brain Activity Related to the Judgment of Face-Likeness: Correlation between EEG and Face-Like Evaluation

    Directory of Open Access Journals (Sweden)

    Yuji Nihei

    2018-02-01

    Full Text Available Faces represent important information for social communication, because social information, such as face-color, expression, and gender, is obtained from faces. Therefore, individuals' tend to find faces unconsciously, even in objects. Why is face-likeness perceived in non-face objects? Previous event-related potential (ERP studies showed that the P1 component (early visual processing, the N170 component (face detection, and the N250 component (personal detection reflect the neural processing of faces. Inverted faces were reported to enhance the amplitude and delay the latency of P1 and N170. To investigate face-likeness processing in the brain, we explored the face-related components of the ERP through a face-like evaluation task using natural faces, cars, insects, and Arcimboldo paintings presented upright or inverted. We found a significant correlation between the inversion effect index and face-like scores in P1 in both hemispheres and in N170 in the right hemisphere. These results suggest that judgment of face-likeness occurs in a relatively early stage of face processing.

  4. Intrinsic functional brain mapping in reconstructed 4D magnetic susceptibility (χ) data space.

    Science.gov (United States)

    Chen, Zikuan; Calhoun, Vince

    2015-02-15

    By solving an inverse problem of T2*-weighted magnetic resonance imaging for a dynamic fMRI study, we reconstruct a 4D magnetic susceptibility source (χ) data space for intrinsic functional mapping. A 4D phase dataset is calculated from a 4D complex fMRI dataset. The background field and phase wrapping effect are removed by a Laplacian technique. A 3D χ source map is reconstructed from a 3D phase image by a computed inverse MRI (CIMRI) scheme. A 4D χ data space is reconstructed by repeating the 3D χ source reconstruction for each time point. A functional map is calculated by a temporal correlation between voxel signals in the 4D χ space and the timecourse of the task paradigm. With a finger-tapping experiment, we obtain two 3D functional mappings in the 4D magnitude data space and in the reconstructed 4D χ data space. We find that the χ-based functional mapping reveals co-occurrence of bidirectional responses in a 3D activation map that is different from the conventional magnitude-based mapping. The χ-based functional mapping can also be achieved by a 3D deconvolution of a phase activation map. Based on a subject experimental comparison, we show that the 4D χ tomography method could produce a similar χ activation map as obtained by the 3D deconvolution method. By removing the dipole effect and other fMRI technological contaminations, 4D χ tomography provides a 4D χ data space that allows a more direct and truthful functional mapping of a brain activity. Published by Elsevier B.V.

  5. Technical Aspects of Awake Craniotomy with Mapping for Brain Tumors in a Limited Resource Setting.

    Science.gov (United States)

    Leal, Rafael Teixeira Magalhaes; Barcellos, Bruno Mendonça; Landeiro, Jose Alberto

    2018-05-01

    Brain tumor surgery near or within eloquent regions is increasingly common and is associated with a high risk of neurologic injury. Awake craniotomy with mapping has been shown to be a valid method to preserve neurologic function and increase the extent of resection. However, the technique used varies greatly among centers. Most count on professionals such as neuropsychologists, speech therapists, neurophysiologists, or neurologists to help in intraoperative patient evaluation. We describe our technique with the sole participation of neurosurgeons and anesthesiologists. A retrospective review of 19 patients who underwent awake craniotomies for brain tumors between January 2013 and February 2017 at a tertiary university hospital was performed. We sought to identify and describe the most critical stages involved in this surgery as well as show the complications associated with our technique. Preoperative preparation, positioning, anesthesia, brain mapping, resection, and management of seizures and pain were stages deemed relevant to the accomplishment of an awake craniotomy. Sixteen percent of the patients developed new postoperative deficit. Seizures occurred in 24%. None led to awake craniotomy failure. We provide a thorough description of the technique used in awake craniotomies with mapping used in our institution, where the intraoperative patient evaluation is carried out solely by neurosurgeons and anesthesiologists. The absence of other specialized personnel and equipment does not necessarily preclude successful mapping during awake craniotomy. We hope to provide helpful information for those who wish to offer function-guided tumor resection in their own centers. Copyright © 2018 Elsevier Inc. All rights reserved.

  6. Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery.

    Science.gov (United States)

    Hart, Michael G; Ypma, Rolf J F; Romero-Garcia, Rafael; Price, Stephen J; Suckling, John

    2016-06-01

    Neuroanatomy has entered a new era, culminating in the search for the connectome, otherwise known as the brain's wiring diagram. While this approach has led to landmark discoveries in neuroscience, potential neurosurgical applications and collaborations have been lagging. In this article, the authors describe the ideas and concepts behind the connectome and its analysis with graph theory. Following this they then describe how to form a connectome using resting state functional MRI data as an example. Next they highlight selected insights into healthy brain function that have been derived from connectome analysis and illustrate how studies into normal development, cognitive function, and the effects of synthetic lesioning can be relevant to neurosurgery. Finally, they provide a précis of early applications of the connectome and related techniques to traumatic brain injury, functional neurosurgery, and neurooncology.

  7. Mapping of Synaptic-Neuronal Impairment on the Brain Surface through Fluctuation Analysis

    International Nuclear Information System (INIS)

    Musha, Toshimitsu; Kurachi, Takayoshi; Suzuki, Naohoro; Kosugi, Yukio

    2005-01-01

    Increase of demented population year by year is becoming a serious social problem to be solved urgently. The most effective way to block this increase is in its early detection by means of an inexpensive, non-invasive, sensitive, reliable and easy-to-operate diagnosis method. We have developed a method satisfying these requirements by using scalp potential fluctuations. We have collected 21ch EEG and SPECT data of 25 very mild Alzheimer's disease (AD) (MMSE=26±1.8), moderately severe AD (MMSE=15.3±6.4) and age-matched normal controls. As AD progresses, local synaptic-neuronal activity becomes abnormal, either more unstable or more inactive than in normal state. Such abnormality is detected in terms of normalized power variance (NPV) of a scalp potential recorded with a scalp electrode. The z-score is defined by z = ((NPV of a subject) - (mean NPV of normal subjects))/(standard deviation of NPV of normal subjects). Correlation of a measured z-score map with the mean z-score map for AD patients characterizes likelihood to AD, in terms of which AD is discriminated from normal with 75% of true positive and 25% false negative probability. By introducing two thresholds, we have 90% of true positive and 10% of false negative discrimination

  8. Whole-Brain Mapping of Neuronal Activity in the Learned Helplessness Model of Depression.

    Science.gov (United States)

    Kim, Yongsoo; Perova, Zinaida; Mirrione, Martine M; Pradhan, Kith; Henn, Fritz A; Shea, Stephen; Osten, Pavel; Li, Bo

    2016-01-01

    Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP - a marker of neuronal activation - in c-fosGFP transgenic mice subjected to the learned helplessness (LH) procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing "helpless" behavior had an overall brain-wide reduction in the level of neuronal activation compared with mice showing "resilient" behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole-brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. Our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses to stress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.

  9. Whole-brain mapping of neuronal activity in the learned helplessness model of depression

    Directory of Open Access Journals (Sweden)

    Yongsoo eKim

    2016-02-01

    Full Text Available Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP – a marker of neuronal activation – in c-fosGFP transgenic mice subjected to the learned helplessness (LH procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing helpless behavior had an overall brain-wide reduction in the level of neuronal activation compared with mice showing resilient behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. Our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses to stress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.

  10. Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

    Science.gov (United States)

    Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

    2016-01-01

    Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms

  11. Hyperspherical Manifold for EEG Signals of Epileptic Seizures

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    Tahir Ahmad

    2012-01-01

    Full Text Available The mathematical modelling of EEG signals of epileptic seizures presents a challenge as seizure data is erratic, often with no visible trend. Limitations in existing models indicate a need for a generalized model that can be used to analyze seizures without the need for apriori information, whilst minimizing the loss of signal data due to smoothing. This paper utilizes measure theory to design a discrete probability measure that reformats EEG data without altering its geometric structure. An analysis of EEG data from three patients experiencing epileptic seizures is made using the developed measure, resulting in successful identification of increased potential difference in portions of the brain that correspond to physical symptoms demonstrated by the patients. A mapping then is devised to transport the measure data onto the surface of a high-dimensional manifold, enabling the analysis of seizures using directional statistics and manifold theory. The subset of seizure signals on the manifold is shown to be a topological space, verifying Ahmad's approach to use topological modelling.

  12. Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

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    François-B. Vialatte

    2011-01-01

    Full Text Available Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD. However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ (3.5–7.5 Hz, α1 (7.5–9.5 Hz, α2 (9.5–12.5 Hz, and β (12.5–25 Hz. The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the θ range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.

  13. Quantitative Susceptibility Mapping Indicates a Disturbed Brain Iron Homeostasis in Neuromyelitis Optica ? A Pilot Study

    OpenAIRE

    Doring, Thomas Martin; Granado, Vanessa; Rueda, Fernanda; Deistung, Andreas; Reichenbach, Juergen R.; Tukamoto, Gustavo; Gasparetto, Emerson Leandro; Schweser, Ferdinand

    2016-01-01

    Dysregulation of brain iron homeostasis is a hallmark of many neurodegenerative diseases and can be associated with oxidative stress. The objective of this study was to investigate brain iron in patients with Neuromyelitis Optica (NMO) using quantitative susceptibility mapping (QSM), a quantitative iron-sensitive MRI technique. 12 clinically confirmed NMO patients (6 female and 6 male; age 35.4y±14.2y) and 12 age- and sex-matched healthy controls (7 female and 5 male; age 33.9±11.3y) underwen...

  14. Dysconnection topography in schizophrenia revealed with state-space analysis of EEG.

    Science.gov (United States)

    Jalili, Mahdi; Lavoie, Suzie; Deppen, Patricia; Meuli, Reto; Do, Kim Q; Cuénod, Michel; Hasler, Martin; De Feo, Oscar; Knyazeva, Maria G

    2007-10-24

    The dysconnection hypothesis has been proposed to account for pathophysiological mechanisms underlying schizophrenia. Widespread structural changes suggesting abnormal connectivity in schizophrenia have been imaged. A functional counterpart of the structural maps would be the EEG synchronization maps. However, due to the limits of currently used bivariate methods, functional correlates of dysconnection are limited to the isolated measurements of synchronization between preselected pairs of EEG signals. To reveal a whole-head synchronization topography in schizophrenia, we applied a new method of multivariate synchronization analysis called S-estimator to the resting dense-array (128 channels) EEG obtained from 14 patients and 14 controls. This method determines synchronization from the embedding dimension in a state-space domain based on the theoretical consequence of the cooperative behavior of simultaneous time series-the shrinking of the state-space embedding dimension. The S-estimator imaging revealed a specific synchronization landscape in schizophrenia patients. Its main features included bilaterally increased synchronization over temporal brain regions and decreased synchronization over the postcentral/parietal region neighboring the midline. The synchronization topography was stable over the course of several months and correlated with the severity of schizophrenia symptoms. In particular, direct correlations linked positive, negative, and general psychopathological symptoms to the hyper-synchronized temporal clusters over both hemispheres. Along with these correlations, general psychopathological symptoms inversely correlated within the hypo-synchronized postcentral midline region. While being similar to the structural maps of cortical changes in schizophrenia, the S-maps go beyond the topography limits, demonstrating a novel aspect of the abnormalities of functional cooperation: namely, regionally reduced or enhanced connectivity. The new method of

  15. Dysconnection topography in schizophrenia revealed with state-space analysis of EEG.

    Directory of Open Access Journals (Sweden)

    Mahdi Jalili

    2007-10-01

    Full Text Available The dysconnection hypothesis has been proposed to account for pathophysiological mechanisms underlying schizophrenia. Widespread structural changes suggesting abnormal connectivity in schizophrenia have been imaged. A functional counterpart of the structural maps would be the EEG synchronization maps. However, due to the limits of currently used bivariate methods, functional correlates of dysconnection are limited to the isolated measurements of synchronization between preselected pairs of EEG signals.To reveal a whole-head synchronization topography in schizophrenia, we applied a new method of multivariate synchronization analysis called S-estimator to the resting dense-array (128 channels EEG obtained from 14 patients and 14 controls. This method determines synchronization from the embedding dimension in a state-space domain based on the theoretical consequence of the cooperative behavior of simultaneous time series-the shrinking of the state-space embedding dimension. The S-estimator imaging revealed a specific synchronization landscape in schizophrenia patients. Its main features included bilaterally increased synchronization over temporal brain regions and decreased synchronization over the postcentral/parietal region neighboring the midline. The synchronization topography was stable over the course of several months and correlated with the severity of schizophrenia symptoms. In particular, direct correlations linked positive, negative, and general psychopathological symptoms to the hyper-synchronized temporal clusters over both hemispheres. Along with these correlations, general psychopathological symptoms inversely correlated within the hypo-synchronized postcentral midline region. While being similar to the structural maps of cortical changes in schizophrenia, the S-maps go beyond the topography limits, demonstrating a novel aspect of the abnormalities of functional cooperation: namely, regionally reduced or enhanced connectivity.The new

  16. Accelerated whole-brain multi-parameter mapping using blind compressed sensing.

    Science.gov (United States)

    Bhave, Sampada; Lingala, Sajan Goud; Johnson, Casey P; Magnotta, Vincent A; Jacob, Mathews

    2016-03-01

    To introduce a blind compressed sensing (BCS) framework to accelerate multi-parameter MR mapping, and demonstrate its feasibility in high-resolution, whole-brain T1ρ and T2 mapping. BCS models the evolution of magnetization at every pixel as a sparse linear combination of bases in a dictionary. Unlike compressed sensing, the dictionary and the sparse coefficients are jointly estimated from undersampled data. Large number of non-orthogonal bases in BCS accounts for more complex signals than low rank representations. The low degree of freedom of BCS, attributed to sparse coefficients, translates to fewer artifacts at high acceleration factors (R). From 2D retrospective undersampling experiments, the mean square errors in T1ρ and T2 maps were observed to be within 0.1% up to R = 10. BCS was observed to be more robust to patient-specific motion as compared to other compressed sensing schemes and resulted in minimal degradation of parameter maps in the presence of motion. Our results suggested that BCS can provide an acceleration factor of 8 in prospective 3D imaging with reasonable reconstructions. BCS considerably reduces scan time for multiparameter mapping of the whole brain with minimal artifacts, and is more robust to motion-induced signal changes compared to current compressed sensing and principal component analysis-based techniques. © 2015 Wiley Periodicals, Inc.

  17. Hidden Markov event sequence models: toward unsupervised functional MRI brain mapping.

    Science.gov (United States)

    Faisan, Sylvain; Thoraval, Laurent; Armspach, Jean-Paul; Foucher, Jack R; Metz-Lutz, Marie-Noëlle; Heitz, Fabrice

    2005-01-01

    Most methods used in functional MRI (fMRI) brain mapping require restrictive assumptions about the shape and timing of the fMRI signal in activated voxels. Consequently, fMRI data may be partially and misleadingly characterized, leading to suboptimal or invalid inference. To limit these assumptions and to capture the broad range of possible activation patterns, a novel statistical fMRI brain mapping method is proposed. It relies on hidden semi-Markov event sequence models (HSMESMs), a special class of hidden Markov models (HMMs) dedicated to the modeling and analysis of event-based random processes. Activation detection is formulated in terms of time coupling between (1) the observed sequence of hemodynamic response onset (HRO) events detected in the voxel's fMRI signal and (2) the "hidden" sequence of task-induced neural activation onset (NAO) events underlying the HROs. Both event sequences are modeled within a single HSMESM. The resulting brain activation model is trained to automatically detect neural activity embedded in the input fMRI data set under analysis. The data sets considered in this article are threefold: synthetic epoch-related, real epoch-related (auditory lexical processing task), and real event-related (oddball detection task) fMRI data sets. Synthetic data: Activation detection results demonstrate the superiority of the HSMESM mapping method with respect to a standard implementation of the statistical parametric mapping (SPM) approach. They are also very close, sometimes equivalent, to those obtained with an "ideal" implementation of SPM in which the activation patterns synthesized are reused for analysis. The HSMESM method appears clearly insensitive to timing variations of the hemodynamic response and exhibits low sensitivity to fluctuations of its shape (unsustained activation during task). Real epoch-related data: HSMESM activation detection results compete with those obtained with SPM, without requiring any prior definition of the expected

  18. Deep learning with convolutional neural networks for EEG decoding and visualization

    Science.gov (United States)

    Springenberg, Jost Tobias; Fiederer, Lukas Dominique Josef; Glasstetter, Martin; Eggensperger, Katharina; Tangermann, Michael; Hutter, Frank; Burgard, Wolfram; Ball, Tonio

    2017-01-01

    Abstract Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end‐to‐end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end‐to‐end EEG analysis, but a better understanding of how to design and train ConvNets for end‐to‐end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the caus