Plichta, Michael M; Wolf, Isabella; Hohmann, Sarah; Baumeister, Sarah; Boecker, Regina; Schwarz, Adam J; Zangl, Maria; Mier, Daniela; Diener, Carsten; Meyer, Patric; Holz, Nathalie; Ruf, Matthias; Gerchen, Martin F; Bernal-Casas, David; Kolev, Vasil; Yordanova, Juliana; Flor, Herta; Laucht, Manfred; Banaschewski, Tobias; Kirsch, Peter; Meyer-Lindenberg, Andreas; Brandeis, Daniel
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been used to study the neural correlates of reward anticipation, but the interrelation of EEG and fMRI measures remains unknown. The goal of the present study was to investigate this relationship in response to a well established reward anticipation paradigm using simultaneous EEG-fMRI recording in healthy human subjects. Analysis of causal interactions between the thalamus (THAL), ventral-striatum (VS), and supplementary motor area (SMA), using both mediator analysis and dynamic causal modeling, revealed that (1) THAL fMRI blood oxygenation level-dependent (BOLD) activity is mediating intermodal correlations between the EEG contingent negative variation (CNV) signal and the fMRI BOLD signal in SMA and VS, (2) the underlying causal connectivity network consists of top-down regulation from SMA to VS and SMA to THAL along with an excitatory information flow through a THAL→VS→SMA route during reward anticipation, and (3) the EEG CNV signal is best predicted by a combination of THAL fMRI BOLD response and strength of top-down regulation from SMA to VS and SMA to THAL. Collectively, these findings represent a likely neurobiological mechanism mapping a primarily subcortical process, i.e., reward anticipation, onto a cortical signature.
Panksepp, Jaak; Northoff, Georg
The nature of "the self" has been one of the central problems in philosophy and more recently in neuroscience. This raises various questions: (i) Can we attribute a self to animals? (ii) Do animals and humans share certain aspects of their core selves, yielding a trans-species concept of self? (iii) What are the neural processes that underlie a possible trans-species concept of self? (iv) What are the developmental aspects and do they result in various levels of self-representation? Drawing on recent literature from both human and animal research, we suggest a trans-species concept of self that is based upon what has been called a "core-self" which can be described by self-related processing (SRP) as a specific mode of interaction between organism and environment. When we refer to specific neural networks, we will here refer to the underlying system as the "core-SELF." The core-SELF provides primordial neural coordinates that represent organisms as living creatures-at the lowest level this elaborates interoceptive states along with raw emotional feelings (i.e., the intentions in action of a primordial core-SELF) while higher medial cortical levels facilitate affective-cognitive integration (yielding a fully-developed nomothetic core-self). Developmentally, SRP allows stimuli from the environment to be related and linked to organismic needs, signaled and processed within core-self structures within subcorical-cortical midline structures (SCMS) that provide the foundation for epigenetic emergence of ecologically framed, higher idiographic forms of selfhood across different individuals within a species. These functions ultimately operate as a coordinated network. We postulate that core SRP operates automatically, is deeply affective, and is developmentally and epigenetically connected to sensory-motor and higher cognitive abilities. This core-self is mediated by SCMS, embedded in visceral and instinctual representations of the body that are well integrated with basic
Thatcher, R W; North, D; Biver, C
There are two inter-related categories of EEG measurement: 1, EEG currents or power and; 2, EEG network properties such as coherence and phase delays. The purpose of this study was to compare the ability of these two different categories of EEG measurement to predict performance on the Weschler Intelligence test (WISC-R). Resting eyes closed EEG was recorded from 19 scalp locations with a linked ears reference from 442 subjects aged 5-52 years. The Weschler Intelligence test was administered to the same subjects but not while the EEG was recorded. Subjects were divided into high IQ (> or = 120) and low IQ ( EEG coherence > EEG amplitude asymmetry > absolute power > relative power and power ratios. The strongest correlations to IQ were short EEG phase delays in the frontal lobes and long phase delays in the posterior cortical regions, reduced coherence and increased absolute power. The findings are consistent with increased neural efficiency and increased brain complexity as positively related to intelligence, and with frontal lobe synchronization of neural resources as a significant contributing factor to EEG and intelligence correlations. Quantitative EEG predictions of intelligence provide medium to strong effect size estimates of cognitive functioning while simultaneously revealing a deeper understanding of the neurophysiological substrates of intelligence.
Caicedo, Alexander; Thewissen, Liesbeth; Smits, Anne; Naulaers, Gunnar; Allegaert, Karel; Van Huffel, Sabine
This study investigates the relationship between brain oxygenation, assessed by means of near infrared spectroscopy (NIRS), and brain function, assessed by means of electroencephalography (EEG). Using NIRS signals measuring the regional cerebral oxygen saturation (rScO2) and computing the fractional tissue oxygen extraction (FTOE), we compared how these variables relate to different features extracted from the EEG, such as the inter-burst interval (IBI) duration and amplitude, the amplitude of the EEG, and the amplitude of the burst. A cohort of 22 neonates undergoing sedation by propofol was studied and a regression of the NIRS-derived values to the different EEG features was made. We found that higher values of FTOE were related to higher values of EEG amplitude. These results might be of used in the monitoring of proper brain function in neonates.
Acharya, U Rajendra; S, Vidya; Bhat, Shreya; Adeli, Hojjat; Adeli, Amir
Alcoholism is a severe disorder that affects the functionality of neurons in the central nervous system (CNS) and alters the behavior of the affected person. Electroencephalogram (EEG) signals can be used as a diagnostic tool in the evaluation of subjects with alcoholism. The neurophysiological interpretation of EEG signals in persons with alcoholism (PWA) is based on observation and interpretation of the frequency and power in their EEGs compared to EEG signals from persons without alcoholism. This paper presents a review of the known features of EEGs obtained from PWA and proposes that the impact of alcoholism on the brain can be determined by computer-aided analysis of EEGs through extracting the minute variations in the EEG signals that can differentiate the EEGs of PWA from those of nonaffected persons. The authors advance the idea of automated computer-aided diagnosis (CAD) of alcoholism by employing the EEG signals. This is achieved through judicious combination of signal processing techniques such as wavelet, nonlinear dynamics, and chaos theory and pattern recognition and classification techniques. A CAD system is cost-effective and efficient and can be used as a decision support system by physicians in the diagnosis and treatment of alcoholism especially those who do not specialize in alcoholism or neurophysiology. It can also be of great value to rehabilitation centers to assess PWA over time and to monitor the impact of treatment aimed at minimizing or reversing the effects of the disease on the brain. A CAD system can be used to determine the extent of alcoholism-related changes in EEG signals (low, medium, high) and the effectiveness of therapeutic plans. Copyright © 2014 Elsevier Inc. All rights reserved.
Lier, H. van; Drinkenburg, W.H.I.M.; Coenen, A.M.L.
To date, EEG studies towards strain differences have focussed on pharmacologically altered or pathological EEG activity, but only few studies have investigated strain differences and normal EEG activity. A strong relation between behaviour and EEG activity has been demonstrated, especially for
Gibbs, Steve A; Proserpio, Paola; Terzaghi, Michele; Pigorini, Andrea; Sarasso, Simone; Lo Russo, Giorgio; Tassi, Laura; Nobili, Lino
During the last decade, many clinical and pathophysiological aspects of sleep-related epileptic and non-epileptic paroxysmal behaviors have been clarified. Advances have been achieved in part through the use of intracerebral recording methods such as stereo-electroencephalography (S-EEG), which has allowed a unique "in vivo" neurophysiological insight into focal epilepsy. Using S-EEG, the local features of physiological and pathological EEG activity in different cortical and subcortical structures have been better defined during the entire sleep-wake spectrum. For example, S-EEG has contributed to clarify the semiology of sleep-related seizures as well as highlight the specific epileptogenic networks involved during ictal activity. Moreover, intracerebral EEG recordings derived from patients with epilepsy have been valuable to study sleep physiology and specific sleep disorders. The occasional co-occurrence of NREM-related parasomnias in epileptic patients undergoing S-EEG investigation has permitted the recordings of such events, highlighting the presence of local electrophysiological dissociated states and clarifying the underlying pathophysiological substrate of such NREM sleep disorders. Based on these recent advances, the authors review and summarize the current and relevant S-EEG literature on sleep-related hypermotor epilepsies and NREM-related parasomnias. Finally, novel data and future research hypothesis will be discussed. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
Feng, Xin; Forbes, Erika E.; Kovacs, Maria; George, Charles J.; Lopez-Duran, Nestor L.; Fox, Nathan A.; Cohn, Jeffrey F.
This study examined the relations of school-age children’s depressive symptoms, frontal EEG asymmetry, and maternal history of childhood-onset depression (COD). Participants were 73 children, 43 of whom had mothers with COD. Children’s EEG was recorded at baseline and while watching happy and sad film clips. Depressive symptoms were measured using parent-report of Children’s Depression Inventory. The key findings are the interaction effects between baseline and film frontal EEG asymmetry on child depressive symptoms. Specifically, relative right frontal EEG asymmetry while watching happy or sad film clip was associated with elevated depressive symptoms for children who also exhibited right frontal EEG asymmetry at baseline. Results suggest that right frontal EEG asymmetry that is consistent across situations may be an marker of depression-prone children. PMID:21894523
Storti, Silvia Francesca; Formaggio, Emanuela; Beltramello, Alberto; Fiaschi, Antonio; Manganotti, Paolo
Electroencephalography combined with functional magnetic resonance imaging (EEG-fMRI) identifies blood oxygenation level dependent (BOLD) signal changes associated with physiological and pathological EEG events. In this study we used EEG-fMRI to determine the possible correlation between topographical movement related EEG changes in brain oscillatory activity recorded from EEG electrodes over the scalp and fMRI cortical responses in motor areas during finger movement. Thirty-two channels of EEG were recorded in 12 subjects during eyes-closed condition inside a three T magnetic resonance (MR) scanner using an MR-compatible EEG recording system. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during fMRI acquisition. For EEG data analysis we used a time-frequency approach to measure time by varying the energy in a signal at a given frequency band by the convolution of the EEG signal with a wavelet family in the alpha and beta bands. The correlation between the BOLD signal associated with the EEG regressor provides that sensory motor region is a source of the EEG. We conclude that combined EEG-fMRI can be used to investigate movement-related oscillations of the human brain inside an MRI scanner and wavelet analysis adds further details on the EEG changes. The movement-related changes in the EEG signals are useful to identify the brain activation sources responsible for BOLD-signal changes.
Garcia-Molina, Gary; Bialas, Piotr
Recent research has shown the EEG's spectral changes that occur in synchrony with the respiratory-cycle. During wakefulness, and for healthy subjects it is reported that the EEG power in several frequency bands changes between the expiratory and inspiratory phases. For sleep-disordered breathing (SDB) patients, it is reported that the amplitude of changes in normalized EEG power (referred to as respiratory-cycle related EEG changes RCREC) within a respiratory-cycle decreases after a successful intervention to alleviate the SDB condition. In this paper, we focus on analyzing the changes in the sleep’s EEG spectrum related to the respiratory-cycle for a healthy population comprising 39 subjects. For 3 sleep stages (N2, N3, REM), 6 EEG channels, and 7 frequency bands, two types of EEG spectral analyzes were considered: 1) the ratio between the EEG power during expiration and that during inspiration, and 2) the RCREC. For the first type of analysis and at the population level, no statistically significant difference was found between the EEG power during expiration and that during inspiration. For the second type of analysis, the RCREC for all conditions is at a level that is statistically significantly larger than 0.1. The latter being the value at which the RCREC decreased after successful SDB intervention.
Benedek, Mathias; Bergner, Sabine; Konen, Tanja; Fink, Andreas; Neubauer, Aljoscha C.
Synchronization of EEG alpha activity has been referred to as being indicative of cortical idling, but according to more recent evidence it has also been associated with active internal processing and creative thinking. The main objective of this study was to investigate to what extent EEG alpha synchronization is related to internal processing…
Krotkikh, S. S.; Kirichenko, L. O.
In this work we use discrete wavelet transform for analyzes the frequency structure of EEG signal with evoked potentials after effect of stimulus. The method for determining the response time to a stimulus, based on the evaluation of the wavelet entropy and relative wavelet entropy EEG, has been implemented.
Feng, Xin; Forbes, Erika E.; Kovacs, Maria; George, Charles J.; Lopez-Duran, Nestor L.; Fox, Nathan A.; Cohn, Jeffrey F.
This study examined the relations of school-age children's depressive symptoms, frontal EEG asymmetry, and maternal history of childhood-onset depression (COD). Participants were 73 children, 43 of whom had mothers with COD. Children's EEG was recorded at baseline and while watching happy and sad film clips. Depressive symptoms were measured using…
Anokhin, A.P.; van Baal, G.C.M.; van Beijsterveldt, C.E.M.; de Geus, E.J.C.; Grant, J.; Boomsma, D.I.
Previous studies have demonstrated moderate heritability of the P300 component of event-related brain potentials (ERPs) and high heritability of background electroencephalogram (EEG) power spectrum. However, it is unclear whether EEG and ERPs are influenced by common or independent genetic factors.
Full Text Available Objective: Diabetes is a risk factor for dementia and mild cognitive impairment. The aim of this study was to investigate whether some features of resting-state EEG (rsEEG could be applied as a biomarker to distinguish the subjects with amnestic mild cognitive impairment (aMCI from normal cognitive function in type 2 diabetes. Materials and Methods: In this study, 28 patients with type 2 diabetes (16 aMCI patients and 12 controls were investigated. Recording of the rsEEG series and neuropsychological assessments were performed. The rsEEG signal was first decomposed into delta, theta, alpha, beta, gamma frequency bands. The relative power of each given band/sum of power and the coherence of waves from different brain areas were calculated. The extracted features from rsEEG and neuropsychological assessments were analyzed as well. Results: The main findings of this study were that: 1 compared with the control group, the ratios of power in theta band (P(theta versus power in alpha band (P(alpha (P(theta/P(alpha in the frontal region and left temporal region were significantly higher for aMCI, and 2 for aMCI, the alpha coherences in posterior, fronto-right temporal, fronto-posterior, right temporo-posterior were decreased; the theta coherences in left central-right central (LC-RC and left posterior-right posterior (LP-RP regions were also decreased; but the delta coherences in left temporal-right temporal (LT-RT region were increased. Conclusion: The proposed indexes from rsEEG recordings could be employed to track cognitive function of diabetic patients and also to help in the diagnosis of those who develop aMCI.
Sterman, M. B.
Electrophysiological changes in the sensorimotor pathways were found to accompany the effect of rhythmic EEG patterns in the sensorimotor cortex. Additionally, several striking behavioral changes were seen, including in particular an enhancement of sleep and an elevation of seizure threshold to epileptogenic agents. This raised the possibility that human seizure disorders might be influenced therapeutically by similar training. Our objective in human EEG feedback training became not only the facilitation of normal rhythmic patterns, but also the suppression of abnormal activity, thus requiring complex contingencies directed to the normalization of the sensorimotor EEG. To achieve this, a multicomponent frequency analysis was developed to extract and separate normal and abnormal elements of the EEG signal. Each of these elements was transduced to a specific component of a visual display system, and these were combined through logic circuits to present the subject with a symbolic display. Variable criteria provided for the gradual shaping of EEG elements towards the desired normal pattern. Some 50-70% of patients with poorly controlled seizure disorders experienced therapeutic benefits from this approach in our laboratory, and subsequently in many others. A more recent application of this approach to the modification of human brain function in our lab has been directed to the dichotomous problems of task overload and underload in the contemporary aviation environment. At least 70% of all aviation accidents have been attributed to the impact of these kinds of problems on crew performance. The use of EEG in this context has required many technical innovations and the application of the latest advances in EEG signal analysis. Our first goal has been the identification of relevant EEG characteristics. Additionally, we have developed a portable recording and analysis system for application in this context. Findings from laboratory and in-flight studies suggest that we
Reber, T P; Do Lam, A T A; Axmacher, N; Elger, C E; Helmstaedter, C; Henke, K; Fell, J
Drawing inferences from past experiences enables adaptive behavior in future situations. Inference has been shown to depend on hippocampal processes. Usually, inference is considered a deliberate and effortful mental act which happens during retrieval, and requires the focus of our awareness. Recent fMRI studies hint at the possibility that some forms of hippocampus-dependent inference can also occur during encoding and possibly also outside of awareness. Here, we sought to further explore the feasibility of hippocampal implicit inference, and specifically address the temporal evolution of implicit inference using intracranial EEG. Presurgical epilepsy patients with hippocampal depth electrodes viewed a sequence of word pairs, and judged the semantic fit between two words in each pair. Some of the word pairs entailed a common word (e.g., "winter-red," "red-cat") such that an indirect relation was established in following word pairs (e.g., "winter-cat"). The behavioral results suggested that drawing inference implicitly from past experience is feasible because indirect relations seemed to foster "fit" judgments while the absence of indirect relations fostered "do not fit" judgments, even though the participants were unaware of the indirect relations. A event-related potential (ERP) difference emerging 400 ms post-stimulus was evident in the hippocampus during encoding, suggesting that indirect relations were already established automatically during encoding of the overlapping word pairs. Further ERP differences emerged later post-stimulus (1,500 ms), were modulated by the participants' responses and were evident during encoding and test. Furthermore, response-locked ERP effects were evident at test. These ERP effects could hence be a correlate of the interaction of implicit memory with decision-making. Together, the data map out a time-course in which the hippocampus automatically integrates memories from discrete but related episodes to implicitly influence future
Mørup, Morten; Hansen, Lars Kai; Hermann, Cristoph S.
-Montes, E., Valdes-Sosa, P.A., Nishiyama, N., Mizuhara, H., Yamaguchi, Y., 2004. Decomposing EEG data into space-time-frequency components using parallel factor analysis. Neuroimage 22, 1035-1045). In this article, PARAFAC is used for the first time to decompose wavelet transformed event-related EEG given...... of frequency transformed multi-channel EEG of channel x frequency x time data. The multi-way decomposition method Parallel Factor (PARAFAC), also named Canonical Decomposition (CANDECOMP), was recently used to decompose the wavelet transformed ongoing EEG of channel x frequency x time (Miwakeichi, F., Martinez......In the decomposition of multi-channel EEG signals, principal component analysis (PCA) and independent component analysis (ICA) have widely been used. However, as both methods are based on handling two-way data, i.e. two-dimensional matrices, multi-way methods might improve the interpretation...
Gevensleben, Holger; Holl, Birgit; Albrecht, Björn; Schlamp, Dieter; Kratz, Oliver; Studer, Petra; Wangler, Susanne; Rothenberger, Aribert; Moll, Gunther H; Heinrich, Hartmut
In a randomized controlled trial, neurofeedback (NF) training was found to be superior to a computerised attention skills training concerning the reduction of ADHD symptomatology (Gevensleben et al., 2009). The aims of this investigation were to assess the impact of different NF protocols (theta/beta training and training of slow cortical potentials, SCPs) on the resting EEG and the association between distinct EEG measures and behavioral improvements. In 72 (of initially 102) children with ADHD, aged 8-12, EEG changes after either a NF training (n=46) or the control training (n=26) could be studied. The combined NF training consisted of one block of theta/beta training and one block of SCP training, each block comprising 18 units of 50 minutes (balanced order). Spontaneous EEG was recorded in a two-minute resting condition before the start of the training, between the two training blocks and after the end of the training. Activity in the different EEG frequency bands was analyzed. In contrast to the control condition, the combined NF training was accompanied by a reduction of theta activity. Protocol-specific EEG changes (theta/beta training: decrease of posterior-midline theta activity; SCP training: increase of central-midline alpha activity) were associated with improvements in the German ADHD rating scale. Related EEG-based predictors were obtained. Thus, differential EEG patterns for theta/beta and SCP training provide further evidence that distinct neuronal mechanisms may contribute to similar behavioral improvements in children with ADHD.
Li, Y; Zhang, T; Deng, L; Wang, B
With the continuous improvement of maneuvering performance of modern high-performance aircraft, the protection problem of flight personnel under high G acceleration, the development as well as research on monitoring system and the equipment for human physiological signals processing which include electroencephalogram (EEG) have become more and more important. Due to the particularity of +Gz experimental conditions, the high-risk of human experiments and the great difficulty of dynamic measurement, there is little research on the synchronous acquisition technology of EEG and related physiological signals under +Gz acceleration environment. We propose a framework to execute human experiments using the three-axial high-performance human centrifuge, develop reasonable operation mode and design a new experimental research method for EEG signal acquisition and variation characteristics on three-axial high-performance human centrifuge under the environment of +Gz acceleration. We also propose to build the synchronous real-time acquisition plan of EEG, electrocardiogram, brain blood pressure, ear pulse and related physiological signals under centrifuge +Gz acceleration with different equipments and methods. The good profiles of EEG, heart rate, brain blood pressure and ear pulse are obtained and analyzed comparatively. In addition, the FMS hop-by-hop continuous blood pressure and hemodynamic measurement system Portapres are successfully applied to the ambulatory blood pressure measure under centrifuge +Gz acceleration environment. The proposed methods establish the basis and have an important guiding significance for follow-up experiment development, EEG features spectral analysis and correlation research of all signals.
Zou, Yuan; Nathan, Viswam; Jafari, Roozbeh
Electroencephalography (EEG) is the recording of electrical activity produced by the firing of neurons within the brain. These activities can be decoded by signal processing techniques. However, EEG recordings are always contaminated with artifacts which hinder the decoding process. Therefore, identifying and removing artifacts is an important step. Researchers often clean EEG recordings with assistance from independent component analysis (ICA), since it can decompose EEG recordings into a number of artifact-related and event-related potential (ERP)-related independent components. However, existing ICA-based artifact identification strategies mostly restrict themselves to a subset of artifacts, e.g., identifying eye movement artifacts only, and have not been shown to reliably identify artifacts caused by nonbiological origins like high-impedance electrodes. In this paper, we propose an automatic algorithm for the identification of general artifacts. The proposed algorithm consists of two parts: 1) an event-related feature-based clustering algorithm used to identify artifacts which have physiological origins; and 2) the electrode-scalp impedance information employed for identifying nonbiological artifacts. The results on EEG data collected from ten subjects show that our algorithm can effectively detect, separate, and remove both physiological and nonbiological artifacts. Qualitative evaluation of the reconstructed EEG signals demonstrates that our proposed method can effectively enhance the signal quality, especially the quality of ERPs, even for those that barely display ERPs in the raw EEG. The performance results also show that our proposed method can effectively identify artifacts and subsequently enhance the classification accuracies compared to four commonly used automatic artifact removal methods.
Deligianni, Fani; Centeno, Maria; Carmichael, David W.; Clayden, Jonathan D.
Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity. PMID:25221467
Full Text Available Detection of single-trial movement intentions from EEG is paramount for brain-computer interfacing in neurorehabilitation. These movement intentions contain task-related information and if this is decoded, the neurorehabilitation could potentially be optimized. The aim of this study was to classify single-trial movement intentions associated with two levels of force and speed and three different grasp types using EEG rhythms and components of the movement-related cortical potential (MRCP as features. The feature importance was used to estimate encoding of discriminative information. Two data sets were used. 29 healthy subjects executed and imagined different hand movements, while EEG was recorded over the contralateral sensorimotor cortex. The following features were extracted: delta, theta, mu/alpha, beta, and gamma rhythms, readiness potential, negative slope, and motor potential of the MRCP. Sequential forward selection was performed, and classification was performed using linear discriminant analysis and support vector machines. Limited classification accuracies were obtained from the EEG rhythms and MRCP-components: 0.48±0.05 (grasp types, 0.41±0.07 (kinetic profiles, motor execution, and 0.39±0.08 (kinetic profiles, motor imagination. Delta activity contributed the most but all features provided discriminative information. These findings suggest that information from the entire EEG spectrum is needed to discriminate between task-related parameters from single-trial movement intentions.
Fernandez, Thalia; Harmony, Thalia; Mendoza, Omar; Lopez-Alanis, Paula; Marroquin, Jose Luis; Otero, Gloria; Ricardo-Garcell, Josefina
Learning disabilities (LD) are one of the most frequent problems for elementary school-aged children. In this paper, event-related EEG oscillations to semantically related and unrelated pairs of words were studied in a group of 18 children with LD not otherwise specified (LD-NOS) and in 16 children with normal academic achievement. We propose that…
Full Text Available The contribution presents an application of a movement-related EEG temporal development classification which improves the classification score of voluntary movements controlled by closely localized regions of the brain. A dynamic Hidden Markov Model-based (HMM classifier specifically designed to capture EEG temporal behavior was used. Surprisingly, HMM classifiers are rarely used for BCI design despite of their advantages. Because of this we also experimented with Learning Vector Quantization, Perceptron, and Support Vector Machine classifiers using a feature space which captures the temporal dynamics of the data. The results presented in this work show that HMM achieves the best performance due to an a priori information on physiological behavior of EEG inserted to the HMM classifier. Feature extraction process and problems with classification were analyzed as well. Classification scores of 66.7% – 94.7% were achieved in our experiments.
Petrantonakis, Panagiotis C; Hadjileontiadis, Leontios J
Emotion discrimination from electroencephalogram (EEG) has gained attention the last decade as a user-friendly and effective approach to EEG-based emotion recognition (EEG-ER) systems. Nevertheless, challenging issues regarding the emotion elicitation procedure, especially its effectiveness, raise. In this work, a novel method, which not only evaluates the degree of emotion elicitation but localizes the emotion information in the time-frequency domain, as well, is proposed. The latter, incorporates multidimensional directed information at the time-frequency EEG representation, extracted using empirical mode decomposition, and introduces an asymmetry index for adaptive emotion-related EEG segment selection. Experimental results derived from 16 subjects visually stimulated with pictures from the valence/arousal space drawn from the International Affective Picture System database, justify the effectiveness of the proposed approach and its potential contribution to the enhancement of EEG-ER systems.
Potari, A.; Ujma, P.P.; Konrad, B.N.; Genzel, L.; Simor, P.; Kormendi, J.; Gombos, F.; Steiger, A.; Dresler, M.; Bodizs, R.
Impaired sleep is a frequent complaint in ageing and a risk factor for many diseases. Non-rapid eye movement (NREM) sleep EEG delta power reflects neural plasticity and, in line with age-related cognitive decline, decreases with age. Individuals with higher general intelligence are less affected by
Gladwin, T.E.; Lindsen, J.P.; Jong, R. de
The task-switching paradigm provides an opportunity to study whether oscillatory relations in neuronal activity are involved in switching between and maintaining task sets. The EEG of subjects performing an alternating runs [Rogers, R.D., Monsell, S., 1995. Costs of a predictable switch between
Gladwin, TE; Lindsen, JP; de Jong, R
The task-switching paradigm provides an opportunity to study whether oscillatory relations in neuronal activity are involved in switching between and maintaining task sets. The EEG of subjects performing an alternating runs [Rogers, R.D., Monsell, S., 1995. Costs of a predictable switch between
van Beijsterveldt, C.E.M.; Boomsma, D.I.
Twin and family studies of normal variation in the human electroencephalogram (EEG) and event related potentials (ERPs) are reviewed. Most of these studies are characterized by small sample sizes. However, by summarizing these studies in one paper, we may be able to gain some insight into the
Lehne, M.; Ihme, K.; Brouwer, A.M.; Erp, J.B.F. van; Zander, T.O.
Recently, the use of brain-computer interfaces (BCIs) has been extended from active control to passive detection of cognitive user states. These passive BCI systems can be especially useful for automatic error detection in human-machine systems by recording EEG potentials related to human error
Jansen, Marije; White, Thomas P.; Mullinger, Karen J.; Liddle, Elizabeth B.; Gowland, Penny A.; Francis, Susan T.; Bowtell, Richard; Liddle, Peter F.
The simultaneous acquisition and subsequent analysis of EEG and fMRI data is challenging owing to increased noise levels in the EEG data. A common method to integrate data from these two modalities is to use aspects of the EEG data, such as the amplitudes of event-related potentials (ERP) or oscillatory EEG activity, to predict fluctuations in the fMRI data. However, this relies on the acquisition of high quality datasets to ensure that only the correlates of neuronal activity are being studied. In this study, we investigate the effects of head-motion-related artefacts in the EEG signal on the predicted T2*-weighted signal variation. We apply our analyses to two independent datasets: 1) four participants were asked to move their feet in the scanner to generate small head movements, and 2) four participants performed an episodic memory task. We created T2*-weighted signal predictors from indicators of abrupt head motion using derivatives of the realignment parameters, from visually detected artefacts in the EEG as well as from three EEG frequency bands (theta, alpha and beta). In both datasets, we found little correlation between the T2*-weighted signal and EEG predictors that were not convolved with the canonical haemodynamic response function (cHRF). However, all convolved EEG predictors strongly correlated with the T2*-weighted signal variation in various regions including the bilateral superior temporal cortex, supplementary motor area, medial parietal cortex and cerebellum. The finding that movement onset spikes in the EEG predict T2*-weighted signal intensity only when the time course of movements is convolved with the cHRF, suggests that the correlated signal might reflect a BOLD response to neural activity associated with head movement. Furthermore, the observation that broad-spectral EEG spikes tend to occur at the same time as abrupt head movements, together with the finding that abrupt movements and EEG spikes show similar correlations with the T2
Full Text Available Neurofeedback is a mode of treatment that is potentially useful for improving self-regulation skills in persons with autism spectrum disorder. We proposed that operant conditioning of EEG in neurofeedback mode can be accompanied by changes in the relative power of EEG bands. However, the details on the change of the relative power of EEG bands during neurofeedback training course in autism are not yet well explored. In this study, we analyzed the EEG recordings of children diagnosed with autism and enrolled in a prefrontal neurofeedback treatment course. The protocol used in this training was aimed at increasing the ability to focus attention, and the procedure represented the wide band EEG amplitude suppression training along with upregulation of the relative power of gamma activity. Quantitative EEG analysis was completed for each session of neurofeedback using wavelet transform to determine the relative power of gamma and theta/beta ratio, and further to detect the statistical changes within and between sessions. We found a linear decrease of theta/beta ratio and a liner increase of relative power of gamma activity over 18 weekly sessions of neurofeedback in 18 high functioning children with autism. The study indicates that neurofeedback is an effective method for altering EEG characteristics associated with the autism spectrum disorder. Also, it provides information about specific changes of EEG activities and details the correlation between changes of EEG and neurofeedback indexes during the course of neurofeedback. This pilot study contributes to the development of more effective approaches to EEG data analysis during prefrontal neurofeedback training in autism.Key word: Electroencephalography, Neurofeedback, Autism Spectrum Disorder, Gamma activity, EEG bands’ ratios
Meyer, Martin; Neff, Patrick; Grest, Angelina; Hemsley, Colette; Weidt, Steffi; Kleinjung, Tobias
Recent research has used source estimation approaches to identify spatially distinct neural configurations in individuals with chronic, subjective tinnitus (TI). The results of these studies are often heterogeneous, a fact which may be partly explained by an inherent heterogeneity in the TI population and partly by the applied EEG data analysis procedure and EEG hardware. Hence this study was performed to re-enact a formerly published study (Joos et al., 2012) to better understand the reason for differences and overlap between studies from different labs. We re-investigated the relationship between neural oscillations and behavioral measurements of affective states in TI, namely depression and tinnitus-related distress by recruiting 45 TI who underwent resting-state EEG. Comprehensive psychopathological (depression and tinnitus-related distress scores) and psychometric data (including other tinnitus characteristics) were gathered. A principal component analysis (PCA) was performed to unveil independent factors that predict distinct aspects of tinnitus-related pathology. Furthermore, we correlated EEG power changes in the standard frequency bands with the behavioral scores for both the whole-brain level and, as a post hoc approach, for selected regions of interest (ROI) based on sLORETA. Behavioral data revealed significant relationships between measurements of depression and tinnitus-related distress. Notably, no significant results were observed for the depressive scores and modulations of the EEG signal. However, akin to the former study we evidenced a significant relationship between a power increase in the β-bands and tinnitus-related distress. In conclusion, it has emerged that depression and tinnitus-related distress, even though they are assumed not to be completely independent, manifest in distinct neural configurations. Copyright © 2017 Elsevier B.V. All rights reserved.
Buttle, Sarah Grace; Sell, Erick; Dyment, David; Bulusu, Srinivas; Pohl, Daniela
We report the case of an infant with KCNQ2-related neonatal epileptic encephalopathy presenting with intractable seizures beginning on the second day of life, which were resistant to multiple antiepileptic drugs. Continuous EEG recordings starting on the sixth day of life demonstrated a unique pattern of inter-and postictal focal rhythmic pointed theta waves of lambdoid morphology in the immediate postictal period, localizing to the side of the antecedent seizure. Interictal EEG exhibited discontinuous background, including patterns of burst suppression and multifocal discharges, predominantly in the centrotemporal regions, which were aggravated during sleep. MRI demonstrated T1 signal abnormalities in the basal ganglia, bilaterally. Genetic testing revealed a de novo missense mutation in KCNQ2 at position c.545 T>G, encoding a previously unreported substitution (p.Val182Gly). Seizure control was achieved immediately after starting a lidocaine infusion at age 4 weeks. The patient remained largely seizure-free following add-on oral carbamazepine for maintenance therapy and weaning off lidocaine. This is the first report of a patient with KCNQ2-related neonatal epileptic encephalopathy and therapy-refractory seizures aborted by lidocaine, demonstrating a unique EEG pattern of inter- and postictal focal rhythmic pointed theta waves. Whether this pattern could be an early EEG marker for this disorder remains to be confirmed. [Published with video sequences on www.epilepticdisorders.com].
Iturrate, I.; Montesano, L.; Minguez, J.
Objective. A major difficulty of brain-computer interface (BCI) technology is dealing with the noise of EEG and its signal variations. Previous works studied time-dependent non-stationarities for BCIs in which the user’s mental task was independent of the device operation (e.g., the mental task was motor imagery and the operational task was a speller). However, there are some BCIs, such as those based on error-related potentials, where the mental and operational tasks are dependent (e.g., the mental task is to assess the device action and the operational task is the device action itself). The dependence between the mental task and the device operation could introduce a new source of signal variations when the operational task changes, which has not been studied yet. The aim of this study is to analyse task-dependent signal variations and their effect on EEG error-related potentials.Approach. The work analyses the EEG variations on the three design steps of BCIs: an electrophysiology study to characterize the existence of these variations, a feature distribution analysis and a single-trial classification analysis to measure the impact on the final BCI performance.Results and significance. The results demonstrate that a change in the operational task produces variations in the potentials, even when EEG activity exclusively originated in brain areas related to error processing is considered. Consequently, the extracted features from the signals vary, and a classifier trained with one operational task presents a significant loss of performance for other tasks, requiring calibration or adaptation for each new task. In addition, a new calibration for each of the studied tasks rapidly outperforms adaptive techniques designed in the literature to mitigate the EEG time-dependent non-stationarities.
Wang, Li-qun; Wang, Mingshi; Mizuhara, Hiroaki
In this study, P300 that induced by visual stimuli was examined with simultaneous EEG/fMRI. For the purpose of combine the best temporary resolution with the best special resolution together to estimate the brain function, event-related analysis contributed to this methodological trial. A 64 channel MRT-compatible MR EEG amplifier (BrainAmp: made of Brain Production GmbH, Gennany) was used in the measurement simultaneously with fMRI scanning. The reference channel is between Fz, Cz and Pz. Sampling rate of raw EEG was 5 kHz, and the MRT noise reduction was performed. EEG recording synchronized with MRI scan by our original stimulus system, and an oddball paradigm (four-oriented Landolt Ring presentation) was performed in the official manner. After P300 segmentation, the timing of P300 was exported to event-related analysis of fMRI data with SPM99 software. In single subject study, the significant activations appear in the left superior frontal, Broca's area and on both sides of the parietal lobule when P300 occurred. It is suggest that P300 may be an integration carried out by top-down signal from frontal to the parietal lobule, which regulates an Attention-Logical Judgment process. Compared with other current methods, the event related analysis by simultaneous EEG/IMRI is excellent in the point that can describe the cognitive process with reality unifying further temporary and spatial information. It is expected that examination and demonstration of the obtained result will supply with the promotion of this powerful methods.
Wang, Yao; Sokhadze, Estate M.; El-Baz, Ayman S.; Li, Xiaoli; Sears, Lonnie; Casanova, Manuel F.; Tasman, Allan
Neurofeedback is a mode of treatment that is potentially useful for improving self-regulation skills in persons with autism spectrum disorder. We proposed that operant conditioning of EEG in neurofeedback mode can be accompanied by changes in the relative power of EEG bands. However, the details on the change of the relative power of EEG bands during neurofeedback training course in autism are not yet well explored. In this study, we analyzed the EEG recordings of children diagnosed with autism and enrolled in a prefrontal neurofeedback treatment course. The protocol used in this training was aimed at increasing the ability to focus attention, and the procedure represented the wide band EEG amplitude suppression training along with upregulation of the relative power of gamma activity. Quantitative EEG analysis was completed for each session of neurofeedback using wavelet transform to determine the relative power of gamma and theta/beta ratio, and further to detect the statistical changes within and between sessions. We found a linear decrease of theta/beta ratio and a liner increase of relative power of gamma activity over 18 weekly sessions of neurofeedback in 18 high functioning children with autism. The study indicates that neurofeedback is an effective method for altering EEG characteristics associated with the autism spectrum disorder. Also, it provides information about specific changes of EEG activities and details the correlation between changes of EEG and neurofeedback indexes during the course of neurofeedback. This pilot study contributes to the development of more effective approaches to EEG data analysis during prefrontal neurofeedback training in autism. PMID:26834615
Suarez, E; Viegas, M D; Adjouadi, M; Barreto, A
The focus of this study is to investigate the relations that exist between changes in the orientation of simple visual stimuli displayed to a subject and the induced changes in brain activity recorded as EEG signals. These signals are recorded using the Electric Source Imaging with 256 electrodes (ESI-256). The 256-channel EEG signals of four subjects were measured monopolarly. Each subject was stimulated visually for approximately 7.5 minutes. The stimuli consisted of a series of 300 images depicting four basic orientations of a simple graphical element: a white bar on a black background, with each one of the four orientations (horizontal, vertical, +45 degrees and -45 degrees) shown a total of 75 times in a random order. The notion of missing information under certain orientations is not addressed at this juncture. The EEG signals produced by each subject were recorded in a continuous mode using a sampling rate of 1 kHz. Pre-processing of the raw EEG data obtained consisted of epoching, exclusion of faulty electrodes, and reduction of electro-oculogram (EOG) noise due to eye blinks. Topographical maps displaying brain activities and their individual electrode recordings are used as two different means for assessing these changes. It is important to note that the simplicity of the visual stimuli was considered in view of the massive data collected for interpretation. Our goal is to observe and determine new measures that would allow for the quantification and interpretation of such EEG brain activities. Such findings might prove useful for the later use of more complex stimuli and the potential development of size and orientation independent algorithms in image processing.
Full Text Available Abstract Background Evidence for a high degree of heritability of EEG alpha phenotypes has been demonstrated in twin and family studies in a number of populations. However, information on linkage of this phenotype to specific chromosome locations is still limited. This study's aims were to map loci linked to EEG alpha phenotypes and to determine if there was overlap with loci previously mapped for alcohol dependence in an American Indian community at high risk for substance dependence. Methods Each participant gave a blood sample and completed a structured diagnostic interview using the Semi Structured Assessment for the Genetics of Alcoholism. Bipolar EEGs were collected and spectral power determined in the alpha (7.5-12.0 Hz frequency band for two composite scalp locations previously identified by principal components analyses (bilateral fronto-central and bilateral centro-parietal-occipital. Genotypes were determined for a panel of 791 micro-satellite polymorphisms in 410 members of multiplex families using SOLAR. Results Sixty percent of this study population had a lifetime diagnosis of alcohol dependence. Analyses of multipoint variance component LOD scores, for the EEG alpha power phenotype, revealed two loci that had a LOD score of 3.0 or above for the fronto-central scalp region on chromosomes 1 and 6. Additionally, 4 locations were identified with LOD scores above 2.0 on chromosomes 4, 11, 14, 16 for the fronto-central location and one on chromosome 2 for the centro-parietal-occipital location. Conclusion These results corroborate the importance of regions on chromosome 4 and 6 highlighted in prior segregation studies in this and other populations for alcohol dependence-related phenotypes, as well as other areas that overlap with other substance dependence phenotypes identified in previous linkage studies in other populations. These studies additionally support the construct that EEG alpha recorded from fronto-central scalp areas may
Full Text Available Abstract Background Autism Spectrum Conditions (ASC are a set of pervasive neurodevelopmental conditions characterized by a wide range of lifelong signs and symptoms. Recent explanatory models of autism propose abnormal neural connectivity and are supported by studies showing decreased interhemispheric coherence in individuals with ASC. The first aim of this study was to test the hypothesis of reduced interhemispheric coherence in ASC, and secondly to investigate specific effects of task performance on interhemispheric coherence in ASC. Methods We analyzed electroencephalography (EEG data from 15 participants with ASC and 15 typical controls, using Wavelet Transform Coherence (WTC to calculate interhemispheric coherence during face and chair matching tasks, for EEG frequencies from 5 to 40 Hz and during the first 400 ms post-stimulus onset. Results Results demonstrate a reduction of interhemispheric coherence in the ASC group, relative to the control group, in both tasks and for all electrode pairs studied. For both tasks, group differences were generally observed after around 150 ms and at frequencies lower than 13 Hz. Regarding within-group task comparisons, while the control group presented differences in interhemispheric coherence between faces and chairs tasks at various electrode pairs (FT7-FT8, TP7-TP8, P7-P8, such differences were only seen for one electrode pair in the ASC group (T7-T8. No significant differences in EEG power spectra were observed between groups. Conclusions Interhemispheric coherence is reduced in people with ASC, in a time and frequency specific manner, during visual perception and categorization of both social and inanimate stimuli and this reduction in coherence is widely dispersed across the brain. Results of within-group task comparisons may reflect an impairment in task differentiation in people with ASC relative to typically developing individuals. Overall, the results of this research support the value of WTC
Background Autism Spectrum Conditions (ASC) are a set of pervasive neurodevelopmental conditions characterized by a wide range of lifelong signs and symptoms. Recent explanatory models of autism propose abnormal neural connectivity and are supported by studies showing decreased interhemispheric coherence in individuals with ASC. The first aim of this study was to test the hypothesis of reduced interhemispheric coherence in ASC, and secondly to investigate specific effects of task performance on interhemispheric coherence in ASC. Methods We analyzed electroencephalography (EEG) data from 15 participants with ASC and 15 typical controls, using Wavelet Transform Coherence (WTC) to calculate interhemispheric coherence during face and chair matching tasks, for EEG frequencies from 5 to 40 Hz and during the first 400 ms post-stimulus onset. Results Results demonstrate a reduction of interhemispheric coherence in the ASC group, relative to the control group, in both tasks and for all electrode pairs studied. For both tasks, group differences were generally observed after around 150 ms and at frequencies lower than 13 Hz. Regarding within-group task comparisons, while the control group presented differences in interhemispheric coherence between faces and chairs tasks at various electrode pairs (FT7-FT8, TP7-TP8, P7-P8), such differences were only seen for one electrode pair in the ASC group (T7-T8). No significant differences in EEG power spectra were observed between groups. Conclusions Interhemispheric coherence is reduced in people with ASC, in a time and frequency specific manner, during visual perception and categorization of both social and inanimate stimuli and this reduction in coherence is widely dispersed across the brain. Results of within-group task comparisons may reflect an impairment in task differentiation in people with ASC relative to typically developing individuals. Overall, the results of this research support the value of WTC in examining the time
Miller, Benjamin R.; Troyer, Melissa; Busey, Thomas
A current topic in neuroscience addresses the link between brain activity and visual awareness. The electroencephalogram (EEG), which uses non-invasive high temporal resolution scalp recordings to measure brain activity, is a common tool used to probe this question. EEG recordings, however, are difficult to implement in the curriculum of laboratory-based courses. Thus, undergraduate students often lack experience with EEG experiments. We report here an EEG program (Virtual EEG) that can be us...
Balkan, Ozgur; Virji-Babul, Naznin; Miyakoshi, Makoto; Makeig, Scott; Garudadri, Harinath
Here, we investigated EEG-based source-level spectral differences between adolescents with sports-related concussions and healthy age matched controls. We transformed resting state EEG collected in both groups to the source domain using Independent Component Analysis (ICA) and computed the component process power spectra. For group-level analysis in the source domain, we used a probabilistic framework, Measure Projection Analysis (MPA), that has advantages over parametric k-means clustering of brain sources. MPA revealed that some frontal brain sources in the concussed group had significantly more power in the beta band (p<;0.005) and significantly less delta (p<;0.01) and theta band power (p<;0.05) than the healthy control group. These results suggest that a shift in spectral profile toward higher frequencies in some frontal brain regions might distinguish individuals with concussion from healthy controls.
Bonfiglio, Luca; Sello, Stefano; Andre, Paolo; Carboncini, Maria Chiara; Arrighi, Pieranna; Rossi, Bruno
Over the past decades, many studies have linked the variations in frequency of spontaneous blinking with certain aspects of information processing and in particular with attention and working memory functions. On the other hand, according to the theory postulated by Crick and Koch, the actual function of primary consciousness is based on the reciprocal interaction between attention and working memory in the automatic and serial mode. The purpose of this study was to investigate for electrophysiological correlates compatible with the cognitive nature of spontaneous blinking, by using the EEG recordings obtained in a group of seven healthy volunteers while they rested quietly though awake, with their eyes open, but not actively engaged in attention-demanding goal-directed behaviours. The global wavelet analysis - at total of 189 three-second EEG epochs time-locked to the blink - revealed an increase in the delta band signal corresponding to the blink. In particular, a reconstruction of the EEG signal by means of inverse-wavelet transform (IWT) showed a blink-related P300-like wave at mid-parietal site. We assumed this phenomenon to represent an electrophysiological sign of the automatic processing of contextual environmental information. This might play a role in maintaining perceptive awareness of the environment at a low level of processing, while the subject is not engaged in attention-demanding tasks but rather introspectively oriented mental activities or free association(s).
Full Text Available Brain-machine interfaces (BMI rely on the accurate classification of event-related potentials (ERPs and their performance greatly depends on the appropriate selection of classifier parameters and features from dense-array electroencephalography (EEG signals. Moreover, in order to achieve a portable and more compact BMI for practical applications, it is also desirable to use a system capable of accurate classification using information from as few EEG channels as possible. In the present work, we propose a method for classifying P300 ERPs using a combination of Fisher Discriminant Analysis (FDA and a multiobjective hybrid real-binary Particle Swarm Optimization (MHPSO algorithm. Specifically, the algorithm searches for the set of EEG channels and classifier parameters that simultaneously maximize the classification accuracy and minimize the number of used channels. The performance of the method is assessed through offline analyses on datasets of auditory ERPs from sound discrimination experiments. The proposed method achieved a higher classification accuracy than that achieved by traditional methods while also using fewer channels. It was also found that the number of channels used for classification can be significantly reduced without greatly compromising the classification accuracy.
Fitzgerald, Zoë; Thayer, Zoë; Mohamed, Armin; Miller, Laurie A
Some patients with epilepsy demonstrate normal memory when this is tested at relatively short intervals (e.g., 30 min), but substantial loss over longer delay periods (e.g., days or weeks) when compared to healthy control subjects. This pattern of "accelerated long-term forgetting" (ALF) affects the everyday lives of patients, yet goes undetected by standard neuropsychological memory tests, and its pathophysiologic basis is poorly understood. By testing memory over a period of concurrent ambulatory electroencephalography (EEG), the current study aimed to investigate possible factors contributing to ALF. Thirty-nine patients diagnosed with epilepsy or probable epilepsy underwent 5 days of continuous ambulatory EEG: 18 had normal EEG studies, 10 had focal epileptic discharges, 5 had generalized epileptic discharges, and 6 had one or more seizures. Fifteen matched healthy control subjects also participated, but did not undergo EEG. Subjects were taught 13-item word and design lists to criterion, and recall was tested at 30 min, 24 h, and 4 days. Subjects also completed questionnaires pertaining to everyday memory and mood. Group analyses (excluding patients who experienced seizures during monitoring) indicated that patients who experienced generalized discharges during the 24-h to 4-day delay intervals showed higher rates of forgetting for nonverbal information. Those with focal discharges showed ALF between 30 min and 4 days for verbal information, whereas those with normal EEGs over the 4 days recording had no evidence of ALF. Surprisingly, mood and epilepsy variables (such as duration of disease or number of anticonvulsant medications) showed no significant correlation with ALF. Although no aspect of nighttime sleep architecture was found to be related to recall after the first 24 h, daytime naps were associated with better retention. Self-report of everyday memory functioning was related to recall at longer delays, but not at 30 min. The present findings indicated
Al-Qazzaz, Noor Kamal; Ali, Sawal Hamid Bin Mohd; Ahmad, Siti Anom; Islam, Mohd Shabiul; Escudero, Javier
Stroke survivors are more prone to developing cognitive impairment and dementia. Dementia detection is a challenge for supporting personalized healthcare. This study analyzes the electroencephalogram (EEG) background activity of 5 vascular dementia (VaD) patients, 15 stroke-related patients with mild cognitive impairment (MCI), and 15 control healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the discrimination of VaD, stroke-related MCI patients, and control subjects using fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR); second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. Nineteen channels were recorded and analyzed using the independent component analysis and wavelet analysis (ICA-WT) denoising technique. Using ANOVA, linear spectral power including relative powers (RP) and power ratio were calculated to test whether the EEG dominant frequencies were slowed down in VaD and stroke-related MCI patients. Non-linear features including permutation entropy (PerEn) and fractal dimension (FD) were used to test the degree of irregularity and complexity, which was significantly lower in patients with VaD and stroke-related MCI than that in control subjects (ANOVA; p ˂ 0.05). This study is the first to use fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) dimensionality reduction technique with EEG background activity of dementia patients. The impairment of post-stroke patients was detected using support vector machine (SVM) and k-nearest neighbors (kNN) classifiers. A comparative study has been performed to check the effectiveness of using FNPAQR dimensionality reduction technique with the SVM and kNN classifiers. FNPAQR with SVM and kNN obtained 91.48 and 89.63% accuracy, respectively, whereas without using the FNPAQR exhibited 70 and 67.78% accuracy for SVM and k
Beres, Anna M
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.
Sheridan, P H; Sato, S; Foster, N; Bruno, G; Cox, C; Fedio, P; Chase, T N
Fourteen patients with Alzheimer's disease were evaluated by psychometric testing, fluorodeoxyglucose positron emission tomography (PET), and EEG. They were divided into two groups according to the EEG findings. Group A (seven patients) had normal alpha backgrounds and group B (seven patients) had decreased alpha backgrounds. Group A had significantly higher WAIS Performance IQ scores (p = 0.005) than group B. Group A also had higher Weschler Memory Scale scores (p = 0.047) and parietal glucose metabolic rates (p = 0.038) than group B, but these differences are not statistically significant given the multiple comparisons made between the two groups. Relative intactness of parietal lobe function, as measured by psychometric testing and PET, appears to correlate with preservation of EEG alpha background. The EEG may be useful in assessing regional cortical involvement or the clinical stage in Alzheimer's disease.
Previous research on meditation and the EEG is described, and findings relating to EEG patterns during meditation are discussed. Comparisons of meditation with other altered states are reviewed and it is concluded that, on the basis of existing EEG evidence, there is some reason for differentiating between meditation and drowsing. Research on alpha-blocking and habituation of the blocking response during meditation is reviewed, and the effects of meditation on EEG patterns outside of meditati...
Deiber, Marie-Pierre; Meziane, Hadj Boumediene; Hasler, Roland; Rodriguez, Cristelle; Toma, Simona; Ackermann, Marine; Herrmann, François; Giannakopoulos, Panteleimon
Future treatments of Alzheimer's disease need the identification of cases at high risk at the preclinical stage of the disease before the development of irreversible structural damage. We investigated here whether subtle cognitive deterioration in a population of healthy elderly individuals could be predicted by EEG signals at baseline under cognitive activation. Continuous EEG was recorded in 97 elderly control subjects and 45 age-matched mild cognitive impairment (MCI) cases during a simple attentional and a 2-back working memory task. Upon 18-month neuropsychological follow-up, the final sample included 55 stable (sCON) and 42 deteriorated (dCON) controls. We examined the P1, N1, P3, and PNwm event-related components as well as the oscillatory activities in the theta (4-7 Hz), alpha (8-13 Hz), and beta (14-25 Hz) frequency ranges (ERD/ERS: event-related desynchronization/synchronization, and ITC: inter-trial coherence). Behavioral performance, P1, and N1 components were comparable in all groups. The P3, PNwm, and all oscillatory activity indices were altered in MCI cases compared to controls. Only three EEG indices distinguished the two control groups: alpha and beta ERD (dCON > sCON) and beta ITC (dCON working memory processes but mostly affects attention, resulting in an enhanced recruitment of attentional resources. In addition, cognitive decline alters neural firing synchronization at high frequencies (14-25 Hz) at early stages, and possibly affects lower frequencies (4-13 Hz) only at more severe stages.
Full Text Available Electroencephalograms (EEGs measure a brain signal that contains abundant information about the human brain function and health. For this reason, recent clinical brain research and brain computer interface (BCI studies use EEG signals in many applications. Due to the significant noise in EEG traces, signal processing to enhance the signal to noise power ratio (SNR is necessary for EEG analysis, especially for non-invasive EEG. A typical method to improve the SNR is averaging many trials of event related potential (ERP signal that represents a brain’s response to a particular stimulus or a task. The averaging, however, is very sensitive to variable delays. In this study, we propose two time delay estimation (TDE schemes based on a joint maximum likelihood (ML criterion to compensate the uncertain delays which may be different in each trial. We evaluate the performance for different types of signals such as random, deterministic, and real EEG signals. The results show that the proposed schemes provide better performance than other conventional schemes employing averaged signal as a reference, e.g., up to 4 dB gain at the expected delay error of 10°.
This paper proposes a novel approach to detect epilepsy seizures by using Electroencephalography (EEG), which is one of the most common methods for the diagnosis of epilepsy, based on 1-Dimension Local Binary Pattern (1D-LBP) and grey relational analysis (GRA) methods. The main aim of this paper is to evaluate and validate a novel approach, which is a computer-based quantitative EEG analyzing method and based on grey systems, aimed to help decision-maker. In this study, 1D-LBP, which utilizes all data points, was employed for extracting features in raw EEG signals, Fisher score (FS) was employed to select the representative features, which can also be determined as hidden patterns. Additionally, GRA is performed to classify EEG signals through these Fisher scored features. The experimental results of the proposed approach, which was employed in a public dataset for validation, showed that it has a high accuracy in identifying epileptic EEG signals. For various combinations of epileptic EEG, such as A-E, B-E, C-E, D-E, and A-D clusters, 100, 96, 100, 99.00 and 100% were achieved, respectively. Also, this work presents an attempt to develop a new general-purpose hidden pattern determination scheme, which can be utilized for different categories of time-varying signals.
van Dellen, Edwin; de Waal, Hanneke; van der Flier, Wiesje M; Lemstra, Afina W; Slooter, Arjen J C; Smits, Lieke L; van Straaten, Elisabeth C W; Stam, Cornelis J; Scheltens, Philip
The aim of this study was to test whether disturbed EEG resting-state functional connectivity and network organization are a potential neurophysiological substrate of cognitive impairment in dementia with Lewy bodies. EEG recordings were obtained in dementia with Lewy bodies patients, Alzheimer's disease patients and controls, matched for age and sex (N = 66 for each group; 14 [21%] female; mean age: 70 years). We analyzed functional connectivity of band-filtered EEG time series using the phase lag index. Functional brain network topology was analyzed with the minimum spanning tree. Mini-Mental State Examination, Trail Making Test A, and Visual Association Test were used as cognitive measures. Dementia with Lewy bodies patients showed lower connectivity strength in the alpha frequency band, compared to both controls and Alzheimer's disease patients (P dementia with Lewy bodies patients was less efficient and contained less hubs (P dementia with Lewy bodies patients, lower alpha band phase lag index correlated with Visual Association Test scores and Trail Making Test scores (ρ = 0.33 and ρ = 0.31, respectively), whereas leaf fraction (a measure of 'network efficiency') correlated with Visual Association Test scores (ρ = 0.29) and Mini-Mental State Examination scores (ρ = 0.27). Functional networks of dementia with Lewy bodies patients are characterized by decreased connectivity strength and a loss of network efficiency and hubs. Severity of these disturbances is related to cognitive impairment, suggesting that network disturbances mediate between neuropathology and the clinical syndrome in dementia with Lewy bodies. © 2015 International Parkinson and Movement Disorder Society.
Moungou, Athanasia; Thonnard, Jean-Louis; Mouraux, André
When sliding our fingertip against a textured surface, complex vibrations are produced in the skin. It is increasingly recognised that the neural transduction and processing of these vibrations plays an important role in the dynamic tactile perception of textures. The aim of the present study was to develop a novel means to tag the cortical activity related to the processing of these vibrations, by periodically modulating the amplitude of texture exploration-induced vibrations such as to record a steady-state evoked potential (SS-EP). The EEG was recorded while the right index fingertip was scanned against four different textures using a constant exploration velocity. Amplitude modulation of the elicited vibrations was achieved by periodically modulating the force applied against the finger. Frequency analysis of the recorded EEG signals showed that modulation of the vibrations induced by the fingertip-texture interactions elicited an SS-EP at the frequency of modulation (3 Hz) as well as its second harmonic (6 Hz), maximal over parietal regions contralateral to the stimulated side. Textures generating stronger vibrations also generated SS-EPs of greater magnitude. Our results suggest that frequency tagging using SS-EPs can be used to isolate and explore the brain activity related to the tactile exploration of natural textures.
Full Text Available Quick detection of motor intentions is critical in order to minimize the time required to activate a neuroprosthesis. We propose a Markov Switching Model (MSM to achieve quick detection of an event related desynchronization (ERD elicited by motor imagery (MI and recorded by electroencephalography (EEG. Conventional brain computer interfaces (BCI rely on sliding window classifiers in order to perform online continuous classification of the rest vs. MI classes. Based on this approach, the detection of abrupt changes in the sensorimotor power suffers from an intrinsic delay caused by the necessity of computing an estimate of variance across several tenths of a second. Here we propose to avoid explicitly computing the EEG signal variance, and estimate the ERD state directly from the voltage information, in order to reduce the detection latency. This is achieved by using a model suitable in situations characterized by abrupt changes of state, the MSM. In our implementation, the model takes the form of a Gaussian observation model whose variance is governed by two latent discrete states with Markovian dynamics. Its objective is to estimate the brain state (i.e., rest vs. ERD given the EEG voltage, spatially filtered by common spatial pattern (CSP, as observation. The two variances associated with the two latent states are calibrated using the variance of the CSP projection during rest and MI, respectively. The transition matrix of the latent states is optimized by the “quickest detection” strategy that minimizes a cost function of detection latency and false positive rate. Data collected by a dry EEG system from 50 healthy subjects, was used to assess performance and compare the MSM with several logistic regression classifiers of different sliding window lengths. As a result, the MSM achieves a significantly better tradeoff between latency, false positive and true positive rates. The proposed model could be used to achieve a more reactive and
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
Wascher, Edmund; Heppner, Holger; Hoffmann, Sven
In applied contexts, psychophysiological measures have a long tradition to evaluate the user state. EEG correlates that indicate mechanisms of information processing, however, are hardly accessible since discrete time stamps that are necessary for this approach are commonly not available in natural situations. However, eye blinks may close this gap. Eye blinks are assumed to mark distinct points in information processing, necessary to segment the incoming data stream. By using mobile EEG in a simulated working situation we demonstrate that eye-blink-related potentials provide reliable information about cognitive processing in distinct working environments. During cognitive tasks, an increase in the fronto-central N2 component as well as evoked theta activity can be shown, both indices of enhanced cognitive control. The posterior P3 is reduced during physical tasks (sorting of boxes), probably reflecting the more continuous nature of this task. The data are discussed within a model of dopaminergic modulation of blink activity that involves both task specific aspects like executive control and modulating influences of motivation or fatigue. © 2013.
Ferreira, Camila; Deslandes, Andréa; Moraes, Helena; Cagy, Maurício; Basile, Luiz Fernando; Piedade, Roberto; Ribeiro, Pedro
Several studies have investigated the relationship between asymmetrical EEG activity over the frontal cortex and mood. This study aimed at investigating the association between state fluctuations in frontal alpha EEG asymmetry and state changes followed by 24 h of sleep deprivation (SD). Our results show that sleep deprivation caused a significant alteration in the asymmetry values. Activation shifted from the left hemisphere, before SD, to the right hemisphere, after SD, in all frontal electrode pairs. In addition, according to the self-rating scale of SD-related mood effects, subjects became significantly less alerted and active, and sleepier. According to these results, increased right prefrontal activation might be potentially associated with the negative mood states typically seen after sleep deprivation, although the causal relationship is still uncertain. However, more studies will be necessary to establish the viability of EEG asymmetry and the cerebral lateralization hypothesis to explain the SD-related affective changes.
Full Text Available The spectral power of intracranial field potentials shows movement-related modulations during reaching movements to different target positions that in frequencies up to the high-γ range (approximately 50 to above 200 Hz can be reliably used for single-trial inference of movement parameters. However, identifying spectral power modulations suitable for single-trial analysis for non-invasive approaches remains a challenge. We recorded non-invasive electroencephalography (EEG during a self-paced center-out and center-in arm movement task, resulting in 8 reaching movement classes (4 center-out, 4 center-in. We found distinct slow (≤ 5 Hz, μ (7.5 to 10 Hz, β (12.5 to 25 Hz, low-γ (27.5 to approximately 50Hz and high-γ (above 50 Hz movement onset- and end-related responses. Movement class-specific spectral power modulations were restricted to the β band at approximately 1 s after movement end and could be explained by the sensitivity of this response to different static, post-movement electromyography (EMG levels. Based on the β band, significant single-trial inference of reaching movement endpoints was possible. The findings of the present study support the idea that single-trial decoding of different reaching movements from non-invasive EEG spectral power modulations is possible, but also suggest that the informative time window is after movement end and that the informative frequency range is restricted to the β band.
Silva, F.H. Lopes da; Smith, N. Ty; Zwart, Aart; Nichols, W.W.
1. 1. The “Halothane-brain compartment” system was investigated in dogs. The input was the inspired concentration of Halothane. The output was the intensity of EEG spectral components. The EEG was analysed by a hybrid system (analogue filters and digital integration in a small computer). For the
Puce, Aina; Hämäläinen, Matti S
Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed.
Stefano Filho, Carlos A; Attux, Romis; Castellano, Gabriela
Hands motor imagery (MI) has been reported to alter synchronization patterns amongst neurons, yielding variations in the mu and beta bands' power spectral density (PSD) of the electroencephalography (EEG) signal. These alterations have been used in the field of brain-computer interfaces (BCI), in an attempt to assign distinct MI tasks to commands of such a system. Recent studies have highlighted that information may be missing if knowledge about brain functional connectivity is not considered. In this work, we modeled the brain as a graph in which each EEG electrode represents a node. Our goal was to understand if there exists any linear correlation between variations in the synchronization patterns-that is, variations in the PSD of mu and beta bands-induced by MI and alterations in the corresponding functional networks. Moreover, we (1) explored the feasibility of using functional connectivity parameters as features for a classifier in the context of an MI-BCI; (2) investigated three different types of feature selection (FS) techniques; and (3) compared our approach to a more traditional method using the signal PSD as classifier inputs. Ten healthy subjects participated in this study. We observed significant correlations ( p classification, with mean accuracies of (90 ± 8)% and (87 ± 7)% for the mu and beta band, respectively, versus (83 ± 8)% and (83 ± 7)% for the same bands for the graph method. Moreover, the number of features for the graph method was considerably larger. However, results for both methods were relatively close, and even overlapped when the uncertainties of the accuracy rates were considered. Further investigation regarding a careful exploration of other graph metrics may provide better alternatives.
Mothes, Hendrik; Enge, Sören; Strobel, Alexander
To date, the interplay betwexen neurophysiological and individual difference factors in altruistic punishment has been little understood. To examine this issue, 45 individuals participated in a Dictator Game with punishment option while the feedback-related negativity (FRN) was derived from the electroencephalogram (EEG). Unlike previous EEG studies on the Dictator Game, we introduced a third party condition to study the effect of fairness norm violations in addition to employing a first person perspective. For the first time, we also examined the role of individual differences, specifically fairness concerns, positive/negative affectivity, and altruism/empathy as well as recipients' financial situation during altruistic punishment. The main results show that FRN amplitudes were more pronounced for unfair than for fair assignments in both the first person and third party perspectives. These findings suggest that FRN amplitudes are sensitive to fairness norm violations and play a crucial role in the recipients' evaluation of dictator assignments. With respect to individual difference factors, recipients' current financial situation affected the FRN fairness effect in the first person perspective, indicating that when being directly affected by the assignments, more affluent participants experienced stronger violations of expectations in altruistic punishment decisions. Regarding individual differences in trait empathy, in the third party condition FRN amplitudes were more pronounced for those who scored lower in empathy. This may suggest empathy as another motive in third party punishment. Independent of the perspective taken, higher positive affect was associated with more punishment behavior, suggesting that positive emotions may play an important role in restoring violated fairness norms.
Wang, Zhongjin; Wang, Shuang; Ding, Meiping
Quantitative EEG and event-related potential P300 were used to evaluate impairment of cerebral function in patient with partial epilepsy. W value was calculated (power of EEG δ and θ rhythm divided by power of α and β rhythm ) for the extent of focal cortical dysfunction. The W values in left partial epilepsy group, right partial epilepsy group and control group during interictal period compared. The latency, amplitude and reaction time of P300 potential change were observed in each groups. The W values in F(8), T(4) and T(6) regions in patients with left partial epilepsy (P P300 was 54. 76%, the latency, amplitude and reaction team were significantly different to the control group. The abnormal rate of P300 in left and right partial epilepsy groups were 77. 78% and 37.50%, respectively, and the former is significantly higher than the latter. The amplitudes of P300 in C(z) and P(z) of left partial epilepsy were significantly lower than those of right partial epilepsy and control group (P P300 in C(z) and P(z) of all partial epilepsy were significantly longer than those of control group (P < 0.05), however, no difference was found between left and right partial epilepsy. In partial epilepsy the cortical dysfunction occurs ipsilaterally to the epileptogenic zone, and extent of cortical dysfunction is positively correlated with duration of disease course. Cerebral dysfunction in left partial epilepsy is more severe than that in right partial epilepsy.
Maarten Andreas Hogervorst
Full Text Available While studies exist that compare different physiological variables with respect to their association with mental workload, it is still largely unclear which variables supply the best information about momentary workload of an individual and what is the benefit of combining them. We investigated workload using the n-back task, controlling for body movements and visual input. We recorded EEG, skin conductance, respiration, ECG, pupil size and eye blinks of 14 subjects. Various variables were extracted from these recordings and used as features in individually tuned classification models. Online classification was simulated by using the first part of the data as training set and the last part of the data for testing the models. The results indicate that EEG performs best, followed by eye related measures and peripheral physiology. Combining variables from different sensors did not significantly improve workload assessment over the best performing sensor alone. Best classification accuracy, a little over 90% (SD 4%, was reached for distinguishing between high and low workload on the basis of 2 minute segments of EEG and eye related variables. A similar and not significantly different performance of 86% (SD 5% was reached using only EEG from single electrode location Pz.
Garn, Heinrich; Waser, Markus; Deistler, Manfred; Benke, Thomas; Dal-Bianco, Peter; Ransmayr, Gerhard; Schmidt, Helena; Sanin, Guenter; Santer, Peter; Caravias, Georg; Seiler, Stephan; Grossegger, Dieter; Fruehwirt, Wolfgang; Schmidt, Reinhold
To investigate which single quantitative electro-encephalographic (QEEG) marker or which combination of markers correlates best with Alzheimer's disease (AD) severity as measured by the Mini-Mental State Examination (MMSE). We compared quantitative EEG markers for slowing (relative band powers), synchrony (coherence, canonical correlation, Granger causality) and complexity (auto-mutual information, Shannon/Tsallis entropy) in 118 AD patients from the multi-centric study PRODEM Austria. Signal spectra were determined using an indirect spectral estimator. Analyses were adjusted for age, sex, duration of dementia, and level of education. For the whole group (39 possible, 79 probable AD cases) MMSE scores explained 33% of the variations in relative theta power during face encoding, and 31% of auto-mutual information in resting state with eyes closed. MMSE scores explained also 25% of the overall QEEG factor. This factor was thus subordinate to individual markers. In probable AD, QEEG coefficients of determination were always higher than in the whole group, where MMSE scores explained 51% of the variations in relative theta power. Selected QEEG markers show strong associations with AD severity. Both cognitive and resting state should be used for QEEG assessments. Our data indicate theta power measured during face-name encoding to be most closely related to AD severity. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
Ohmann, Katharina; Stahl, Jutta; Mussweiler, Thomas; Kedia, Gayannée
A wide array of social decisions relies on social comparisons. As such, these decisions require fast access to relative information. Therefore, we expect that signatures of the comparative process should be observable in electrophysiological components at an early stage of information processing. However, to date, little is known about the neural time course of social target comparisons. Therefore, we tested this hypothesis in 2 electroencephalography (EEG) studies using a social distance effect paradigm. The distance effect capitalizes on the fact that stimuli close on a certain dimension take longer to compare than stimuli clearly differing on this dimension. Here, we manipulated the distance of face characteristics regarding their levels of attractiveness (Study 1) and trustworthiness (Study 2), 2 essential social dimensions. In both studies, size comparisons served as a nonsocial control condition. In Study 1, distance related effects were apparent 170 ms (vertex positive potential, VPP) and 200 ms (N2) after stimulus onset for attractiveness comparisons. In Study 2, trustworthiness comparisons took effect already after 100 ms (N1) and likewise carried over to an event-related N2. Remarkably, we observed a similar temporal pattern for social (attractiveness, trustworthiness) and nonsocial (size) dimensions. These results speak in favor of an early encoding of comparative information and emphasize the primary role of comparison in social information processing. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
The authors have previously published calculations that show that, despite the high resistivity of the skull, the spatial sensitivity of magnetoencephalography, MEG, is no better than that of electroencephalography, EEG...
Badcock, Nicholas A; Preece, Kathryn A; de Wit, Bianca; Glenn, Katharine; Fieder, Nora; Thie, Johnson; McArthur, Genevieve
Background. Previous work has demonstrated that a commercial gaming electroencephalography (EEG) system, Emotiv EPOC, can be adjusted to provide valid auditory event-related potentials (ERPs) in adults that are comparable to ERPs recorded by a research-grade EEG system, Neuroscan. The aim of the current study was to determine if the same was true for children. Method. An adapted Emotiv EPOC system and Neuroscan system were used to make simultaneous EEG recordings in nineteen 6- to 12-year-old children under "passive" and "active" listening conditions. In the passive condition, children were instructed to watch a silent DVD and ignore 566 standard (1,000 Hz) and 100 deviant (1,200 Hz) tones. In the active condition, they listened to the same stimuli, and were asked to count the number of 'high' (i.e., deviant) tones. Results. Intraclass correlations (ICCs) indicated that the ERP morphology recorded with the two systems was very similar for the P1, N1, P2, N2, and P3 ERP peaks (r = .82 to .95) in both passive and active conditions, and less so, though still strong, for mismatch negativity ERP component (MMN; r = .67 to .74). There were few differences between peak amplitude and latency estimates for the two systems. Conclusions. An adapted EPOC EEG system can be used to index children's late auditory ERP peaks (i.e., P1, N1, P2, N2, P3) and their MMN ERP component.
Full Text Available When a person recognizes an error during a task, an error-related potential (ErrP can be measured as response. It has been shown that ErrPs can be automatically detected in tasks with time-discrete feedback, which is widely applied in the field of Brain-Computer Interfaces (BCIs for error correction or adaptation. However, there are only a few studies that concentrate on ErrPs during continuous feedback.With this study, we wanted to answer three different questions: (i Can ErrPs be measured in electroencephalography (EEG recordings during a task with continuous cursor control? (ii Can ErrPs be classified using machine learning methods and is it possible to discriminate errors of different origins? (iii Can we use EEG to detect the severity of an error? To answer these questions, we recorded EEG data from 10 subjects during a video game task and investigated two different types of error (execution error, due to inaccurate feedback; outcome error, due to not achieving the goal of an action. We analyzed the recorded data to show that during the same task, different kinds of error produce different ErrP waveforms and have a different spectral response. This allows us to detect and discriminate errors of different origin in an event-locked manner. By utilizing the error-related spectral response, we show that also a continuous, asynchronous detection of errors is possible.Although the detection of error severity based on EEG was one goal of this study, we did not find any significant influence of the severity on the EEG.
Full Text Available The aim of the present paper is to show how the variation of the EEG frontal cortical asymmetry is related to the general appreciation perceived during the observation of TV advertisements, in particular considering the influence of the gender and age on it. In particular, we investigated the influence of the gender on the perception of a car advertisement (Experiment 1 and the influence of the factor age on a chewing gum commercial (Experiment 2. Experiment 1 results showed statistically significant higher approach values for the men group throughout the commercial. Results from Experiment 2 showed significant lower values by older adults for the spot, containing scenes not very enjoyed by them. In both studies, there was no statistical significant difference in the scene relative to the product offering between the experimental populations, suggesting the absence in our study of a bias towards the specific product in the evaluated populations. These evidences state the importance of the creativity in advertising, in order to attract the target population.
Full Text Available The capacity to focus one’s attention for an extended period of time can be increased through training in contemplative practices. However, the cognitive processes engaged during meditation that support trait changes in cognition are not well characterized. We conducted a longitudinal wait-list controlled study of intensive meditation training. Retreat participants practiced focused attention meditation techniques for three months during an initial retreat. Wait-list participants later undertook formally identical training during a second retreat. Dense-array scalp-recorded electroencephalogram (EEG data were collected during six minutes of mindfulness of breathing meditation at three assessment points during each retreat. Second-order blind source separation, along with a novel semi-automatic artifact removal tool, was used for data preprocessing. We observed replicable reductions in meditative state-related beta-band power bilaterally over anteriocentral and posterior scalp regions. In addition, individual alpha frequency decreased across both retreats and in direct relation to the amount of meditative practice. These findings provide evidence for replicable longitudinal changes in brain oscillatory activity during meditation and increase our understanding of the cortical processes engaged during meditation that may support long-term improvements in cognition.
Full Text Available The paper discusses the changes in relations of visual and auditory inputs between the hemispheres in a child with hyperactive syndrome and its effects which may lead to better attention engagement in auditory and visual information analysis. The method included the use of cartographic EEG and clinical procedure in a 10-year-old boy with hyperactive syndrome and attention deficit disorder, who has theta dysfunction manifested in standard EEG. Cartographic EEG patterns was performed on NihonKohden Corporation, EEG - 1200K Neurofax apparatus in longitudinal bipolar electrode assembly schedule by utilizing10/20 International electrode positioning. Impedance was maintained below 5 kΩ, with not more than 1 kΩ differences between the electrodes. Lower filter was set at 0.53 Hz and higher filter at 35 Hz. Recording was performed in a quiet period and during stimulation procedures that include speech and language basis. Standard EEG and Neurofeedback (NFB treatment indicated higher theta load, alpha 2 and beta 1 activity measured in the cartographic EEG which was done after the relative failure of NFB treatment. After this, the NFB treatment was applied which lasted for six months, in a way that when the boy was reading, the visual input was enhanced to the left hemisphere and auditory input was reduced to the right hemisphere. Repeated EEG mapping analysis showed that there was a significant improvement, both in EEG findings as well as in attention, behavioural and learning disorders. The paper discusses some aspects of learning, attention and behaviour in relation to changes in the standard EEG, especially in cartographic EEG and NFB findings.
Ferrez, Pierre W; del R Millan, José
Brain-computer interfaces (BCIs) are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on the presence of error-related potentials (ErrP) in the electroencephalogram (EEG) recorded right after the occurrence of an error. Several studies show the presence of ErrP in typical choice reaction tasks. However, in the context of a BCI, the central question is: "Are ErrP also elicited when the error is made by the interface during the recognition of the subject's intent?"; We have thus explored whether ErrP also follow a feedback indicating incorrect responses of the simulated BCI interface. Five healthy volunteer subjects participated in a new human-robot interaction experiment, which seem to confirm the previously reported presence of a new kind of ErrP. However, in order to exploit these ErrP, we need to detect them in each single trial using a short window following the feedback associated to the response of the BCI. We have achieved an average recognition rate of correct and erroneous single trials of 83.5% and 79.2%, respectively, using a classifier built with data recorded up to three months earlier.
Januszko, Piotr; Niemcewicz, Szymon; Gajda, Tomasz; Wołyńczyk-Gmaj, Dorota; Piotrowska, Anna Justyna; Gmaj, Bartłomiej; Piotrowski, Tadeusz; Szelenberger, Waldemar
To investigate local arousal fluctuations in adults who received ICSD-2 diagnosis of somnambulism. EEG neuroimaging (eLORETA) was utilized to compare current density distribution for 4s epochs immediately preceding sleepwalking episode (from -4.0 s to 0 s) to the distribution during earlier 4s epochs (from -8.0 s to -4.0 s) in 20 EEG segments from 15 patients. Comparisons between eLORETA images revealed significant (t>4.52; psleepwalking, with greater current density within beta 3 frequency range (24-30 Hz) in Brodmann areas 33 and 24. Sleepwalking motor events are associated with arousal-related activation of cingulate motor area. These results support the notion of blurred boundaries between wakefulness and NREM sleep in sleepwalking. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
Full Text Available The aim of this work is to show how Wavelet and S-transform Power Spectrum Analysis could be used for detection of the time-frequency spectral differences in series of Event-Related Potentials recorded from neurotic and stable persons. We compared the EEG records in simple counting task condition of 30 healthy subjects divided in stable and neurotic groups according to there scores in neuroticism scale on Eysenck's Personality Questionnaire. Significant differences were found in the theta and alpha EEG bands. The stable persons are characterized with more prominent theta and less prominent alpha spectral power compared to the neurotic group. The application of complex decomposed functions for both Wavelet and S-transform Power Spectrum Analysis showed to be more useful for the discrimination between both groups of subjects.
Zhu, Jian; Coppens, Ryan P; Rabinovich, Norka E; Gilbert, David G
The mechanisms underlying bupropion's efficacy as an antidepressant and a smoking cessation aid are far from being fully characterized. The present study is the first to examine the effects of bupropion on visuospatial task-related parietal EEG alpha power asymmetry-an asymmetry that has previously been found to be associated with severity of depressive symptoms (i.e., the more depressive symptoms, the greater alpha power in the right vs. left parietal area [Henriques & Davidson, 1997; Rabe, Debener, Brocke, & Beauducel, 2005]). Participants, all of whom were smokers and none of whom were clinically depressed, were randomly assigned to the Placebo group (n = 79) or Bupropion group (n = 31) in a double-blind study. EEG during the performance of the visuospatial task was collected before and after 14 days on placebo or bupropion sustained-release capsules. Relative to the Placebo group, the Bupropion group (especially, the Bupropion subgroup who had a positive right versus left parietal alpha power asymmetry at pretreatment) had a reduction in the parietal alpha asymmetry (driven largely by a decrease in right parietal alpha power). These findings support the hypothesis that bupropion can induce changes in parietal EEG asymmetry that have been shown in previous literature to be associated with a reduction in depressive states and traits. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Variability in human resting state electroencephalography (EEG) may reflect emotion regulation processes (for a review, see Knyazev, 2007). For instance, it has been suggested that correlation between slow (1-3 Hz) and fast (13-30 Hz) activity (or δ-β coherence) may reflect functional synchronization between limbic and cortical brain systems. Indirect support comes from several studies reporting relationships between δ-β coherence and subjectively reported behavioral inhibition and state anxiety. The present study sought to extend this work and tested the prediction that objectively, experimentally, measured threat-selective attention should also be related to δ-β coherence. EEG frequency band power and dot probe task performance were assessed in forty healthy women and results demonstrated a negative association between delta-beta coherence and automatic, anxiety-driven attentional avoidance of threatening pictorial stimuli. These first reported objective measures for cognitive-emotional behavior obtained in relation to delta-beta coherence provide additional support for the hypothesis that this EEG parameter may reflect emotion regulation processes and supports suggestions that δ-β coherence may be a useful tool in the experimental study of affect and psychopathology. In addition, results showed an unexpected negative association between δ-β coherence and self-reported trait anxiety (but no association with behavioral inhibition). Copyright © 2011 Elsevier B.V. All rights reserved.
Goodman, Ronald N; Rietschel, Jeremy C; Lo, Li-Chuan; Costanzo, Michelle E; Hatfield, Bradley D
The relationship between trait and state measures of frontal lobe EEG alpha-band asymmetry in regard to indexing the approach-withdrawal dimension of emotion is unclear. The comparative predictive power of these constructs to explain emotion regulation and cognitive performance was examined under varying degrees of emotional challenge. The Capability Model posits the neural underpinnings of the relative difference in electrical activity between the left and right frontal lobes as a situational mechanism possibly indexing prefrontal-amygdalar interactions and psychological state. EEG, skin conductance, heart rate and acoustic startle amplitude were collected during a working memory task under three increasing levels of stress (final level was threat of shock). During threat of shock participants with higher state asymmetry exhibited greater emotion regulation compared to those with lower scores as indexed by significant attenuation of eyeblink startle magnitudes. The trait measure of frontal EEG asymmetry failed to account for significant variability in emotion regulation. Results implicate state-specific relative left frontal lobe activity as having an adaptive role in the regulation of emotion during cognitive challenge, but only under conditions of sufficient stress. Copyright © 2012 Elsevier B.V. All rights reserved.
Gilbert, D G; McClernon, F J; Rabinovich, N E; Dibb, W D; Plath, L C; Hiyane, S; Jensen, R A; Meliska, C J; Estes, S L; Gehlbach, B A
Changes in task-related mood and physiology associated with 31 days of smoking abstinence were assessed in smokers, 34 of whom were randomly assigned to a quit group and 22 to a continuing-to-smoke control group. A large financial incentive for smoking abstinence resulted in very low participant attrition. Individuals were tested during prequit baselines and at 3, 10, 17, and 31 days of abstinence. Abstinence was associated with decreases in heart rate and serum cortisol, a slowing of electroencephalogram (EEG) activity, and task-dependent and trait-depression-dependent hemispheric EEG asymmetries. Differences between the quit group and the smoking group showed no tendency to resolve across the 31 days of abstinence. Trait depression and neuroticism correlated with increases in left-relative-to-right frontal EEG slow-wave (low alpha) activity at both 3 and 31 days of abstinence. In contrast, prequit nicotine intake and Fagerström Tolerance scores correlated with alpha asymmetry and with greater EEG slowing only at Day 3. Thus, the effects of smoking abstinence appear to last for at least several months.
Meledin, Irina; Abu Tailakh, Muhammad; Gilat, Shlomo; Yogev, Hagai; Golan, Agneta; Novack, Victor; Shany, Eilon
To compare amplitude-integrated EEG (aEEG) and conventional EEG (EEG) activity in premature neonates. Biweekly aEEG and EEG were simultaneously recorded in a cohort of infants born less than 34 weeks gestation. aEEG recordings were visually assessed for lower and upper border amplitude and bandwidth. EEG recordings were compressed for visual evaluation of continuity and assessed using a signal processing software for interburst intervals (IBI) and frequencies' amplitude. Ten-minute segments of aEEG and EEG indices were compared using regression analysis. A total of 189 recordings from 67 infants were made, from which 1697 aEEG/EEG pairs of 10-minute segments were assessed. Good concordance was found for visual assessment of continuity between the 2 methods. EEG IBI, alpha and theta frequencies' amplitudes were negatively correlated to the aEEG lower border while conceptional age (CA) was positively correlated to aEEG lower border ( P EEG to aEEG. aEEG recordings in high-risk premature neonates reflect reliably EEG background information related to continuity and amplitude.
Nicholas A. Badcock
Full Text Available Background. Previous work has demonstrated that a commercial gaming electroencephalography (EEG system, Emotiv EPOC, can be adjusted to provide valid auditory event-related potentials (ERPs in adults that are comparable to ERPs recorded by a research-grade EEG system, Neuroscan. The aim of the current study was to determine if the same was true for children.Method. An adapted Emotiv EPOC system and Neuroscan system were used to make simultaneous EEG recordings in nineteen 6- to 12-year-old children under “passive” and “active” listening conditions. In the passive condition, children were instructed to watch a silent DVD and ignore 566 standard (1,000 Hz and 100 deviant (1,200 Hz tones. In the active condition, they listened to the same stimuli, and were asked to count the number of ‘high’ (i.e., deviant tones.Results. Intraclass correlations (ICCs indicated that the ERP morphology recorded with the two systems was very similar for the P1, N1, P2, N2, and P3 ERP peaks (r = .82 to .95 in both passive and active conditions, and less so, though still strong, for mismatch negativity ERP component (MMN; r = .67 to .74. There were few differences between peak amplitude and latency estimates for the two systems.Conclusions. An adapted EPOC EEG system can be used to index children’s late auditory ERP peaks (i.e., P1, N1, P2, N2, P3 and their MMN ERP component.
Davis, Kathryn A.; Ung, Hoameng; Wulsin, Drausin; Wagenaar, Joost; Fox, Emily; Patterson, Ned; Vite, Charles; Worrell, Gregory; Litt, Brian
Objective Brain regions are localized for resection during epilepsy surgery based upon rare seizures observed during a short time period of intracranial EEG (iEEG) monitoring. Interictal epileptiform bursts, which are more prevalent than seizures, may provide complementary information to aid in epilepsy evaluation. In this study, we leverage a long-term iEEG dataset from canines with naturally occurring epilepsy to investigate interictal bursts and their electrographic relationship to seizures. Methods Four dogs were included in this study, each previously monitored with continuous iEEG for periods of 475.7, 329.9, 45.8, and 451.8 days respectively for a total of over 11,000 hours. Seizures and bursts were detected and validated by two board-certified epileptologists. A published Bayesian model was applied to analyze the dynamics of interictal epileptic bursts on EEG and compare them to seizures. Results In three dogs, bursts were stereotyped and found to be statistically similar to periods before or near seizure onsets. Seizures from one dog during status epilepticus were markedly different than other seizures in terms of burst similarity. Significance Shorter epileptic bursts explored in this work have the potential to yield significant information about the distribution of epileptic events. In our data, bursts are at least an order of magnitude more prevalent than seizures and occur much more regularly. Our finding that bursts often display pronounced similarity to seizure onsets suggests that they contain relevant information about the epileptic networks from which they arise and may aide in the clinical evaluation of epilepsy in patients. PMID:26608448
Freeman, Frederick G.
The increased use of automation in the cockpits of commercial planes has dramatically decreased the workload requirements of pilots, enabling them to function more efficiently and with a higher degree of safety. Unfortunately, advances in technology have led to an unexpected problem: the decreased demands on pilots have increased the probability of inducing 'hazardous states of awareness.' A hazardous state of awareness is defined as a decreased level of alertness or arousal which makes an individual less capable of reacting to unique or emergency types of situations. These states tend to be induced when an individual is not actively processing information. Under such conditions a person is likely to let his/her mind wander, either to internal states or to irrelevant external conditions. As a result, they are less capable of reacting quickly to emergency situations. Since emergencies are relatively rare, and since the high automated cockpit requires progressively decreasing levels of engagement, the probability of being seduced into a lowered state of awareness is increasing. This further decreases the readiness of the pilot to react to unique circumstances such as system failures. The HEM Lab at NASA-Langley Research Center has been studying how these states of awareness are induced and what the physiological correlates of these different states are. Specifically, they have been interested in studying electroencephalographic (EEG) measures of different states of alertness to determine if such states can be identified and, hopefully, avoided. The project worked on this summer involved analyzing the EEG and the event related potentials (ERP) data collected while subjects performed under two conditions. Each condition required subjects to perform a relatively boring vigilance task. The purpose of using these tasks was to induce a decreased state of awareness while still requiring the subject to process information. Each task involved identifying an infrequently
Gladwin, Thomas Edward; Lindsen, Job Pieter; de Jong, Ritske
The task-switching paradigm provides an opportunity to study whether oscillatory relations in neuronal activity are involved in switching between and maintaining task sets. The EEG of subjects performing an alternating runs [Rogers, R.D., Monsell, S., 1995. Costs of a predictable switch between simple cognitive tasks. Journal of Experimental Psychology: General 124, 207-231] task-switching task was analyzed using event-related potentials, the lateralized readiness potential, instantaneous amplitude and the phase-locking value [Lachaux, J.P., Rodriguez, E., Martinirie, J., Varela, F.J., 1999. Measuring phase synchrony in brain signals. Human Brain Mapping 8, 194-208]. The two tasks differed in the relevant modality (visual versus auditory) and the hand with which responses were to be given. The mixture model [de Jong, R., 2000. An intention driven account of residual switch costs. In: Monsell, S., Driver, J. (Eds.), Attention and Performance XVII: Cognitive Control. MIT Press, Cambridge] was used to assign pre-stimulus switch probabilities to switch trials based on reaction time; these probabilities were used to create a fast-slow distinction between trials on both switch and hold trials. Results showed both time- and time-frequency-domain effects, during the intervals preceding stimuli, of switching versus maintenance, response speed of the upcoming stimulus, and response hand. Of potential importance for task-switching theory were interactions between reaction time by switch-hold trial type that were found for a frontal slow negative potential and the lateralized readiness potential during the response-stimulus interval, indicating that effective preparation for switch trials involves different anticipatory activity than for hold trials. Theta-band oscillatory activity during the pre-stimulus period was found to be higher when subsequent reaction times were shorter, but this response speed effect did not interact with trial type. The response hand of the upcoming
Davis, Kathryn A; Ung, Hoameng; Wulsin, Drausin; Wagenaar, Joost; Fox, Emily; Patterson, Ned; Vite, Charles; Worrell, Gregory; Litt, Brian
Brain regions are localized for resection during epilepsy surgery based on rare seizures observed during a short period of intracranial electroencephalography (iEEG) monitoring. Interictal epileptiform bursts, which are more prevalent than seizures, may provide complementary information to aid in epilepsy evaluation. In this study, we leverage a long-term iEEG dataset from canines with naturally occurring epilepsy to investigate interictal bursts and their electrographic relationship to seizures. Four dogs were included in this study, each monitored previously with continuous iEEG for periods of 475.7, 329.9, 45.8, and 451.8 days, respectively, for a total of >11,000 h. Seizures and bursts were detected and validated by two board-certified epileptologists. A published Bayesian model was applied to analyze the dynamics of interictal epileptic bursts on EEG and compare them to seizures. In three dogs, bursts were stereotyped and found to be statistically similar to periods before or near seizure onsets. Seizures from one dog during status epilepticus were markedly different from other seizures in terms of burst similarity. Shorter epileptic bursts explored in this work have the potential to yield significant information about the distribution of epileptic events. In our data, bursts are at least an order of magnitude more prevalent than seizures and occur much more regularly. Our finding that bursts often display pronounced similarity to seizure onsets suggests that they contain relevant information about the epileptic networks from which they arise and may aide in the clinical evaluation of epilepsy in patients. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.
Lawrence, Lee Matthew; Ciorciari, Joseph; Kyrios, Michael
The behavioural and cognitive phenomena associated with Compulsive Buying (CB) have been investigated previously but the underlying neurophysiological cognitive process has received less attention. This study specifically investigated the electrophysiology of CB associated with executive processing and cue-reactivity in order to reveal differences in neural connectivity (EEG Coherence) and distinguish it from characteristics of addiction or mood disorder. Participants (N=24, M=25.38 yrs, S.D.=7.02 yrs) completed the Sensitivity to Punishment Sensitivity to Reward Questionnaire and a visual memory task associated with shopping items. Sensitivities to reward and punishment were examined with EEG coherence measures for preferred and non-preferred items and compared to CB psychometrics. Widespread EEG coherence differences were found in numerous regions, with an apparent left shifted lateralisation for preferred and right shifted lateralisation for non-preferred items. Different neurophysiological networks presented with CB phenomena, reflecting cue reactivity and episodic memory, from increased arousal and attachment to items. © 2013 Published by Elsevier Ireland Ltd.
Ray, William J.
The goal of this work is the identification of states especially as related to the process of error production and lapses of awareness as might be experienced during aviation. Given the need for further articulation of the characteristics of 'error prone state' or 'hazardous state of awareness,' this NASA grant focused on basic ground work for the study of the psychophysiology of these states. In specific, the purpose of this grant was to establish the necessary methodology for addressing three broad questions. The first is how the error prone state should be conceptualized, and whether it is similar to a dissociative state, a hypnotic state, or absent mindedness. Over 1200 subjects completed a variety of psychometric measures reflecting internal states and proneness to mental lapses and absent mindedness; the study suggests that there exists a consistency of patterns displayed by individuals who self-report dissociative experiences such that those individuals who score high on measures of dissociation also score high on measures of absent mindedness, errors, and absorption, but not on scales of hypnotizability. The second broad question is whether some individuals are more prone to enter these states than others. A study of 14 young adults who scored either high or low on the dissociation experiences scale performed a series of six tasks. This study suggests that high and low dissociative individuals arrive at the experiment in similar electrocortical states and perform cognitive tasks (e.g., mental math) in a similar manner; it is in the processing of internal emotional states that differences begin to emerge. The third question to be answered is whether recent research in nonlinear dynamics, i.e., chaos, offer an addition and/or alternative to traditional signal processing methods, i.e., fast Fourier transforms, and whether chaos procedures can be modified to offer additional information useful in identifying brain states. A preliminary review suggests that
Full Text Available In brain-computer interface (BCI applications the detection of neural processing as revealed by event-related potentials (ERPs is a frequently used approach to regain communication for people unable to interact through any peripheral muscle control. However, the commonly used electroencephalography (EEG provides signals of low signal-to-noise ratio, making the systems slow and inaccurate. As an alternative noninvasive recording technique, the magnetoencephalography (MEG could provide more advantageous electrophysiological signals due to a higher number of sensors and the magnetic fields not being influenced by volume conduction. We investigated whether MEG provides higher accuracy in detecting event-related fields (ERFs compared to detecting ERPs in simultaneously recorded EEG, both evoked by a covert attention task, and whether a combination of the modalities is advantageous. In our approach, a detection algorithm based on spatial filtering is used to identify ERP/ERF components in a data-driven manner. We found that MEG achieves higher decoding accuracy (DA compared to EEG and that the combination of both further improves the performance significantly. However, MEG data showed poor performance in cross-subject classification, indicating that the algorithm's ability for transfer learning across subjects is better in EEG. Here we show that BCI control by covert attention is feasible with EEG and MEG using a data-driven spatial filter approach with a clear advantage of the MEG regarding DA but with a better transfer learning in EEG.
Reichert, Christoph; Dürschmid, Stefan; Heinze, Hans-Jochen; Hinrichs, Hermann
In brain-computer interface (BCI) applications the detection of neural processing as revealed by event-related potentials (ERPs) is a frequently used approach to regain communication for people unable to interact through any peripheral muscle control. However, the commonly used electroencephalography (EEG) provides signals of low signal-to-noise ratio, making the systems slow and inaccurate. As an alternative noninvasive recording technique, the magnetoencephalography (MEG) could provide more advantageous electrophysiological signals due to a higher number of sensors and the magnetic fields not being influenced by volume conduction. We investigated whether MEG provides higher accuracy in detecting event-related fields (ERFs) compared to detecting ERPs in simultaneously recorded EEG, both evoked by a covert attention task, and whether a combination of the modalities is advantageous. In our approach, a detection algorithm based on spatial filtering is used to identify ERP/ERF components in a data-driven manner. We found that MEG achieves higher decoding accuracy (DA) compared to EEG and that the combination of both further improves the performance significantly. However, MEG data showed poor performance in cross-subject classification, indicating that the algorithm's ability for transfer learning across subjects is better in EEG. Here we show that BCI control by covert attention is feasible with EEG and MEG using a data-driven spatial filter approach with a clear advantage of the MEG regarding DA but with a better transfer learning in EEG.
Fogh, Kasper Wandahl; Greve, Marc
This project is investigating EEG-technology, and how this can be used in games. Specificly, we are investigating how EEG measures brain activity, how you can interact with the technology and how good it works. Furthermore we investigate how the interaction can be used in a game. We investigate through theory on EEG, classification algorithms, Emotivs software and our own game working with both active and passive interaction. We found that even though the technology is new at a consumerlev...
Chervin, Ronald D.; Garetz, Susan L.; Ruzicka, Deborah L.; Hodges, Elise K.; Giordani, Bruno J.; Dillon, James E.; Felt, Barbara T.; Hoban, Timothy F.; Guire, Kenneth E.; O'Brien, Louise M.; Burns, Joseph W.
Study Objectives: Pediatric obstructive sleep apnea (OSA) is associated with hyperactive behavior, cognitive deficits, psychiatric morbidity, and sleepiness, but objective polysomnographic measures of OSA presence or severity among children scheduled for adenotonsillectomy have not explained why. To assess whether sleep fragmentation might explain neurobehavioral outcomes, we prospectively assessed the predictive value of standard arousals and also respiratory cycle-related EEG changes (RCREC), thought to reflect inspiratory microarousals. Methods: Washtenaw County Adenotonsillectomy Cohort II participants included children (ages 3-12 years) scheduled for adenotonsillectomy, for any clinical indication. At enrollment and again 7.2 ± 0.9 (SD) months later, children had polysomnography, a multiple sleep latency test, parent-completed behavioral rating scales, cognitive testing, and psychiatric evaluation. The RCREC were computed as previously described for delta, theta, alpha, sigma, and beta EEG frequency bands. Results: Participants included 133 children, 109 with OSA (apnea-hypopnea index [AHI] ≥ 1.5, mean 8.3 ± 10.6) and 24 without OSA (AHI 0.9 ± 0.3). At baseline, the arousal index and RCREC showed no consistent, significant associations with neurobehavioral morbidities, among all subjects or the 109 with OSA. At follow-up, the arousal index, RCREC, and neurobehavioral measures all tended to improve, but neither baseline measure of sleep fragmentation effectively predicted outcomes (all p > 0.05, with only scattered exceptions, among all subjects or those with OSA). Conclusion: Sleep fragmentation, as reflected by standard arousals or by RCREC, appears unlikely to explain neurobehavioral morbidity among children who undergo adenotonsillectomy. Clinical Trial Registration: ClinicalTrials.gov, ID: NCT00233194 Citation: Chervin RD, Garetz SL, Ruzicka DL, Hodges EK, Giordani BJ, Dillon JE, Felt BT, Hoban TF, Guire KE, O'Brien LM, Burns JW. Do respiratory cycle-related
Full Text Available Claudia Domnick1, Michael Hauck1,2,3, Kenneth L Casey3, Andreas K Engel1, Jürgen Lorenz1,3,41Department of Neurophysiology and Pathophysiology; 2Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; 3Department of Neurology, University of Michigan, Ann Arbor, MI, USA; 4Faculty of Life Sciences, Hamburg University of Applied Sciences, Hamburg, GermanyAbstract: Nociceptive input reaches the brain via two different types of nerve fibers, moderately fast A-delta and slowly conducting C-fibers, respectively. To explore their distinct roles in normal and inflammatory pain we used laser stimulation of normal and capsaicin treated skin at proximal and distal arm sites in combination with time frequency transformation of electroencephalography (EEG data. Comparison of phase-locked (evoked and non-phase-locked (total EEG to laser stimuli revealed three significant pain-related oscillatory responses. First, an evoked response in the delta-theta band, mediated by A-fibers, was reduced by topical capsaicin treatment. Second, a decrease of total power in the alpha-to-gamma band reflected both an A- and C-nociceptor-mediated response with only the latter being reduced by capsaicin treatment. Finally, an enhancement of total power in the upper beta band was mediated exclusively by C-nociceptors and appeared strongly augmented by capsaicin treatment. These findings suggest that phase-locking of brain activity to stimulus onset is a critical feature of A-delta nociceptive input, allowing rapid orientation to salient and potentially threatening events. In contrast, the subsequent C-nociceptive input exhibits clearly less phase coupling to the stimulus. It may primarily signal the tissue status allowing more long-term behavioral adaptations during ongoing inflammatory events that accompany tissue damage.Keywords: C-fibers, oscillations, EEG, laser, capsaicin, inflammatory pain
Bekkedal, Marni Y V; Rossi, John; Panksepp, Jaak
At present there is no direct brain measure of basic emotional dynamics from the human brain. EEG provides non-invasive approaches for monitoring brain electrical activity to emotional stimuli. Event-related desynchronization/synchronization (ERD/ERS) analysis, based on power shifts in specific frequency bands, has some potential as a method for differentiating responses to basic emotions as measured during brief presentations of affective stimuli. Although there appears to be fairly consistent theta ERS in frontal regions of the brain during the earliest phases of processing affective auditory stimuli, the patterns do not readily distinguish between specific emotions. To date it has not been possible to consistently differentiate brain responses to emotion-specific affective states or stimuli, and some evidence to suggests the theta ERS more likely measures general arousal processes rather than yielding veridical indices of specific emotional states. Perhaps cortical EEG patterns will never be able to be used to distinguish discrete emotional states from the surface of the brain. The implications and limitations of such approaches for understanding human emotions are discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.
Full Text Available The open-source toolbox “TopoToolbox” is a suite of functions that use sensor topography to calculate psychologically meaningful measures (similarity, magnitude, and timing from multisensor event-related EEG and MEG data. Using a GUI and data visualization, TopoToolbox can be used to calculate and test the topographic similarity between different conditions (Tian and Huber, 2008. This topographic similarity indicates whether different conditions involve a different distribution of underlying neural sources. Furthermore, this similarity calculation can be applied at different time points to discover when a response pattern emerges (Tian and Poeppel, 2010. Because the topographic patterns are obtained separately for each individual, these patterns are used to produce reliable measures of response magnitude that can be compared across individuals using conventional statistics (Davelaar et al. Submitted and Huber et al., 2008. TopoToolbox can be freely downloaded. It runs under MATLAB (The MathWorks, Inc. and supports user-defined data structure as well as standard EEG/MEG data import using EEGLAB (Delorme and Makeig, 2004.
Tian, Xing; Poeppel, David; Huber, David E
The open-source toolbox "TopoToolbox" is a suite of functions that use sensor topography to calculate psychologically meaningful measures (similarity, magnitude, and timing) from multisensor event-related EEG and MEG data. Using a GUI and data visualization, TopoToolbox can be used to calculate and test the topographic similarity between different conditions (Tian and Huber, 2008). This topographic similarity indicates whether different conditions involve a different distribution of underlying neural sources. Furthermore, this similarity calculation can be applied at different time points to discover when a response pattern emerges (Tian and Poeppel, 2010). Because the topographic patterns are obtained separately for each individual, these patterns are used to produce reliable measures of response magnitude that can be compared across individuals using conventional statistics (Davelaar et al. Submitted and Huber et al., 2008). TopoToolbox can be freely downloaded. It runs under MATLAB (The MathWorks, Inc.) and supports user-defined data structure as well as standard EEG/MEG data import using EEGLAB (Delorme and Makeig, 2004).
Schubert, Ruth; Ritter, Petra; Wüstenberg, Torsten; Preuschhof, Claudia; Curio, Gabriel; Sommer, Werner; Villringer, Arno
Recent studies investigating the influence of spatial-selective attention on primary somatosensory processing have produced inconsistent results. The aim of this study was to explore the influence of tactile spatial-selective attention on spatiotemporal aspects of evoked neuronal activity in the primary somatosensory cortex (S1). We employed simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) in 14 right-handed subjects during bilateral index finger Braille stimulation to investigate the relationship between attentional effects on somatosensory evoked potential (SEP) components and the blood oxygenation level-dependent (BOLD) signal. The 1st reliable EEG response following left tactile stimulation (P50) was significantly enhanced by spatial-selective attention, which has not been reported before. FMRI analysis revealed increased activity in contralateral S1. Remarkably, the effect of attention on the P50 component as well as long-latency SEP components starting at 190 ms for left stimuli correlated with attentional effects on the BOLD signal in contralateral S1. The implications are 2-fold: First, the correlation between early and long-latency SEP components and the BOLD effect suggest that spatial-selective attention enhances processing in S1 at 2 time points: During an early passage of the signal and during a later passage, probably via re-entrant feedback from higher cortical areas. Second, attentional modulations of the fast electrophysiological signals and the slow hemodynamic response are linearly related in S1.
Plante, D T; Goldstein, M R; Landsness, E C; Peterson, M J; Riedner, B A; Ferrarelli, F; Wanger, T; Guokas, J J; Tononi, G; Benca, R M
Sleep spindles are believed to mediate several sleep-related functions including maintaining disconnection from the external environment during sleep, cortical development, and sleep-dependent memory consolidation. Prior studies that have examined sleep spindles in major depressive disorder (MDD) have not demonstrated consistent differences relative to control subjects, which may be due to sex-related variation and limited spatial resolution of spindle detection. Thus, this study sought to characterize sleep spindles in MDD using high-density electroencephalography (hdEEG) to examine the topography of sleep spindles across the cortex in MDD, as well as sex-related variation in spindle topography in the disorder. All-night hdEEG recordings were collected in 30 unipolar MDD participants (19 women) and 30 age and sex-matched controls. Topography of sleep spindle density, amplitude, duration, and integrated spindle activity (ISA) were assessed to determine group differences. Spindle parameters were compared between MDD and controls, including analysis stratified by sex. As a group, MDD subjects demonstrated significant increases in frontal and parietal spindle density and ISA compared to controls. When stratified by sex, MDD women demonstrated increases in frontal and parietal spindle density, amplitude, duration, and ISA; whereas MDD men demonstrated either no differences or decreases in spindle parameters. Given the number of male subjects, this study may be underpowered to detect differences in spindle parameters in male MDD participants. This study demonstrates topographic and sex-related differences in sleep spindles in MDD. Further research is warranted to investigate the role of sleep spindles and sex in the pathophysiology of MDD. Copyright © 2012 Elsevier B.V. All rights reserved.
Blanco Menéndez, R; Aguado Balsas, A M; Blanco, E; Lobo Rodríguez, B; Vera De La Puente, E
CADASIL syndrome (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarts and Leukoencephalopathy) includes some neurological signs and symptoms (gait disturbances, epileptic seizures, pseudobulbar palsy, migraines, etc.), as well as neuropsychological dysfunctions (cognitive and executive impairment, emotional disorders and, frequently, dementia). This syndrome is a good model of white matter damage and potential disconnection syndromes. In this article, the neuropsychological profile of a 47 year-old woman diagnosed of CADASIL is investigated and thoroughly discussed. Results show the presence of a moderate cognitive and executive impairment, specially of memory, psychomotor abilities, executive functions and verbal fluency, but not dementia, overall suggesting the presence of a temporal-frontal-subcortical disfunction. This clinical pattern is an illuminating example of the neuropsychological consequences of the partial disconnection of prefrontal cortex from the thalamus and basal ganglia.
Hillard, Brent; El-Baz, Ayman S; Sears, Lonnie; Tasman, Allan; Sokhadze, Estate M
Neurofeedback is a nonpharmacological treatment for attention-deficit hyperactivity disorder (ADHD). We propose that operant conditioning of electroencephalogram (EEG) in neurofeedback training aimed to mitigate inattention and low arousal in ADHD, will be accompanied by changes in EEG bands' relative power. Patients were 18 children diagnosed with ADHD. The neurofeedback protocol ("Focus/Alertness" by Peak Achievement Trainer) has a focused attention and alertness training mode. The neurofeedback protocol provides one for Focus and one for Alertness. This does not allow for collecting information regarding changes in specific EEG bands (delta, theta, alpha, low and high beta, and gamma) power within the 2 to 45 Hz range. Quantitative EEG analysis was completed on each of twelve 25-minute-long sessions using a custom-made MatLab application to determine the relative power of each of the aforementioned EEG bands throughout each session, and from the first session to the last session. Additional statistical analysis determined significant changes in relative power within sessions (from minute 1 to minute 25) and between sessions (from session 1 to session 12). Analysis was of relative power of theta, alpha, low and high beta, theta/alpha, theta/beta, and theta/low beta and theta/high beta ratios. Additional secondary measures of patients' post-neurofeedback outcomes were assessed, using an audiovisual selective attention test (IVA + Plus) and behavioral evaluation scores from the Aberrant Behavior Checklist. Analysis of data computed in the MatLab application, determined that theta/low beta and theta/alpha ratios decreased significantly from session 1 to session 12, and from minute 1 to minute 25 within sessions. The findings regarding EEG changes resulting from brain wave self-regulation training, along with behavioral evaluations, will help elucidate neural mechanisms of neurofeedback aimed to improve focused attention and alertness in ADHD.
Zhang, H.; Chavarriaga, R.; Khaliliardali, Z.; Gheorghe, L.; Iturrate, I.; Millán, J. d. R.
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.
Chavarriaga, Ricardo; Perrin, Xavier; Siegwart, Roland; Millán, José del R
The exploitation of EEG signatures of cognitive processes can provide valuable information to improve interaction with brain actuated devices. In this work we study these correlates in a realistic situation simulated in a virtual reality environment. We focus on cortical potentials linked to the anticipation of future events (i.e. the contingent negative variation, CNV) and error-related potentials elicited by both visual and tactile feedback. Experiments with 6 subjects show brain activity consistent with previous studies using simpler stimuli, both at the level of ERPs and single trial classification. Moreover, we observe comparable signals irrespective of whether the subject was required to perform motor actions. Altogether, these results support the possibility of using these signals for practical brain machine interaction.
Boha, Roland; Tóth, Brigittal; Gaál, Zsófia Anna; Kardos, Zsófia; File, Bálint; Molnár, Márk
During mental arithmetic operations working memory plays an important role, but there are only few studies in which an attempt was made to separate this effect from the process of arithmetics per se. In this study the effects of arithmetic on the EEG of young adults (14 participants, six of them women, mean age 21.57 years, SD: 2.62) was investigated during a subtraction task in the theta (4-8 Hz) frequency band. Besides the power density spectrum analysis phase synchrony based on recently developed graph theoretical methods were used and strength of local connections (cluster coefficient; C) and global interconnectedness of network (characteristic path length; L) were determined. Before the arithmetic task passive viewing (control situation) and a number recognition paradigms were used. During the arithmetic task compared to the control situation significantly increasing phase synchrony and C values were found. L was significantly shorter (F(2, 26) = 818.77, p power; (F(1, 13) = 7.9708, p = 0.01447 and decreased L values were found in the left frontal region compared to the right side (F(1, 13) = 6.0734, p = 0.0284), which can also be interpreted as an indicator of optimized network topology of information processing.
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
Full Text Available Unlike assistive technology for verbal communication, the brain–machine or brain–computer interface (BMI/BCI has not been established as a nonverbal 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 nonverbal 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. This study also developed an efficient method for detecting implicit negative emotional responses to specific faces by using EEG signals.
Chervin, Ronald D; Garetz, Susan L; Ruzicka, Deborah L; Hodges, Elise K; Giordani, Bruno J; Dillon, James E; Felt, Barbara T; Hoban, Timothy F; Guire, Kenneth E; O'Brien, Louise M; Burns, Joseph W
Pediatric obstructive sleep apnea (OSA) is associated with hyperactive behavior, cognitive deficits, psychiatric morbidity, and sleepiness, but objective polysomnographic measures of OSA presence or severity among children scheduled for adenotonsillectomy have not explained why. To assess whether sleep fragmentation might explain neurobehavioral outcomes, we prospectively assessed the predictive value of standard arousals and also respiratory cycle-related EEG changes (RCREC), thought to reflect inspiratory microarousals. Washtenaw County Adenotonsillectomy Cohort II participants included children (ages 3-12 years) scheduled for adenotonsillectomy, for any clinical indication. At enrollment and again 7.2 ± 0.9 (SD) months later, children had polysomnography, a multiple sleep latency test, parent-completed behavioral rating scales, cognitive testing, and psychiatric evaluation. The RCREC were computed as previously described for delta, theta, alpha, sigma, and beta EEG frequency bands. Participants included 133 children, 109 with OSA (apnea-hypopnea index [AHI] ≥ 1.5, mean 8.3 ± 10.6) and 24 without OSA (AHI 0.9 ± 0.3). At baseline, the arousal index and RCREC showed no consistent, significant associations with neurobehavioral morbidities, among all subjects or the 109 with OSA. At follow-up, the arousal index, RCREC, and neurobehavioral measures all tended to improve, but neither baseline measure of sleep fragmentation effectively predicted outcomes (all p > 0.05, with only scattered exceptions, among all subjects or those with OSA). Sleep fragmentation, as reflected by standard arousals or by RCREC, appears unlikely to explain neurobehavioral morbidity among children who undergo adenotonsillectomy. ClinicalTrials.gov, ID: NCT00233194.
Adrian P Burgess
Full Text Available Although event-related potentials (ERPs are widely used to study sensory, perceptual and cognitive processes, it remains unknown whether they are phase-locked signals superimposed upon the ongoing electroencephalogram (EEG or result from phase-alignment of the EEG. Previous attempts to discriminate between these hypotheses have been unsuccessful but here a new test is presented based on the prediction that ERPs generated by phase-alignment will be associated with event-related changes in frequency whereas evoked-ERPs will not. Using empirical mode decomposition (EMD, which allows measurement of narrow-band changes in the EEG without predefining frequency bands, evidence was found for transient frequency slowing in recognition memory ERPs but not in simulated data derived from the evoked model. Furthermore, the timing of phase-alignment was frequency dependent with the earliest alignment occurring at high frequencies. Based on these findings, the Firefly model was developed, which proposes that both evoked and induced power changes derive from frequency-dependent phase-alignment of the ongoing EEG. Simulated data derived from the Firefly model provided a close match with empirical data and the model was able to account for i the shape and timing of ERPs at different scalp sites, ii the event-related desynchronization in alpha and synchronization in theta, and iii changes in the power density spectrum from the pre-stimulus baseline to the post-stimulus period. The Firefly Model, therefore, provides not only a unifying account of event-related changes in the EEG but also a possible mechanism for cross-frequency information processing.
Burgess, Adrian P
Although event-related potentials (ERPs) are widely used to study sensory, perceptual and cognitive processes, it remains unknown whether they are phase-locked signals superimposed upon the ongoing electroencephalogram (EEG) or result from phase-alignment of the EEG. Previous attempts to discriminate between these hypotheses have been unsuccessful but here a new test is presented based on the prediction that ERPs generated by phase-alignment will be associated with event-related changes in frequency whereas evoked-ERPs will not. Using empirical mode decomposition (EMD), which allows measurement of narrow-band changes in the EEG without predefining frequency bands, evidence was found for transient frequency slowing in recognition memory ERPs but not in simulated data derived from the evoked model. Furthermore, the timing of phase-alignment was frequency dependent with the earliest alignment occurring at high frequencies. Based on these findings, the Firefly model was developed, which proposes that both evoked and induced power changes derive from frequency-dependent phase-alignment of the ongoing EEG. Simulated data derived from the Firefly model provided a close match with empirical data and the model was able to account for i) the shape and timing of ERPs at different scalp sites, ii) the event-related desynchronization in alpha and synchronization in theta, and iii) changes in the power density spectrum from the pre-stimulus baseline to the post-stimulus period. The Firefly Model, therefore, provides not only a unifying account of event-related changes in the EEG but also a possible mechanism for cross-frequency information processing.
Full Text Available Analysis of nonlinear quantitative EEG (qEEG markers describing complexity of signal in relation to severity of Alzheimer’s disease (AD was the focal point of this study. In this study, 79 patients diagnosed with probable AD were recruited from the multi-centric Prospective Dementia Database Austria (PRODEM. EEG recordings were done with the subjects seated in an upright position in a resting state with their eyes closed. Models of linear regressions explaining disease severity, expressed in Mini Mental State Examination (MMSE scores, were analyzed by the nonlinear qEEG markers of auto mutual information (AMI, Shannon entropy (ShE, Tsallis entropy (TsE, multiscale entropy (MsE, or spectral entropy (SpE, with age, duration of illness, and years of education as co-predictors. Linear regression models with AMI were significant for all electrode sites and clusters, where R 2 is 0.46 at the electrode site C3, 0.43 at Cz, F3, and central region, and 0.42 at the left region. MsE also had significant models at C3 with R 2 > 0.40 at scales τ = 5 and τ = 6 . ShE and TsE also have significant models at T7 and F7 with R 2 > 0.30 . Reductions in complexity, calculated by AMI, SpE, and MsE, were observed as the MMSE score decreased.
Gaeta, G; Susac, A; Supek, S; Babiloni, F; Vecchiato, G
The present work aims to investigate the electroencephalographic (EEG) activity elicited by the observation of emotional pictures selected from the International Affective Picture System (IAPS) database. We analyzed the evoked activity within time intervals of increasing duration taking into account the related ratings of Valence and Arousal. The scalp statistical maps of Power Spectral Density (PSD), related to pictures with high valence, revealed an enhanced activity across frontal areas in the theta band and the involvement of fronto-parietal circuits in the alpha band. Difference in the processing of low and high arousing pictures, however, seems to be highly dependent on the valence dimension: for low valenced pictures, the difference in arousal was processed immediately after the observation of the picture, while for the high-valenced ones the processing took part in the second part of the observation. These results appear to be congruent with the literature, while the novelty of the current study is represented by the comparison of the activity elicited in different time windows by both the Arousal and Valence dimensions. It is possible, in this way, to observe how the processing of one variable influences the other, creating a dynamic description of the Valence-Arousal space.
Chinoy, Evan D.; Frey, Danielle J.; Kaslovsky, Daniel N.; Meyer, Francois G.; Wright, Kenneth P.
Objective Whether there are age-related changes in slow wave activity (SWA) rise time, a marker of homeostatic sleep drive, is unknown. Additionally, although sleep medication use is highest among older adults, the quantitative electroencephalographic (EEG) profile of the most commonly prescribed sleep medication, zolpidem, in older adults is also unknown. We therefore quantified age-related and regional brain differences in sleep EEG with and without zolpidem. Methods Thirteen healthy young adults aged 21.9 ± 2.2 years and 12 healthy older adults aged 67.4 ± 4.2 years participated in a randomized, double-blind, within-subject study that compared placebo to 5 mg zolpidem. Results Older adults showed a smaller rise in SWA and zolpidem increased age-related differences in SWA rise time such that age differences were observed earlier after latency to persistent sleep. Age-related differences in EEG power differed by brain region. Older, but not young, adults showed zolpidem-dependent reductions in theta and alpha frequencies. Zolpidem decreased stage 1 in older adults and did not alter other age-related sleep architecture parameters. Conclusions SWA findings provide additional support for reduced homeostatic sleep drive or reduced ability to respond to sleep drive with age. Consequences of reduced power in theta and alpha frequencies in older adults remain to be elucidated. PMID:24980066
Ferri, Lorenzo; Bisulli, Francesca; Mai, Roberto; Licchetta, Laura; Leta, Chiara; Nobili, Lino; Mostacci, Barbara; Pippucci, Tommaso; Tinuper, Paolo
Dishevelled EGL-10 and pleckstrin domain-containing protein 5 (DEPDC5) mutations are found in a wide spectrum of focal epilepsies ranging from epilepsy caused by malformation of cortical development to non-lesional epilepsy, including sleep-related hypermotor epilepsy (SHE). A surgical approach has been anecdotally reported in patients with DEPDC5 mutations, but most of these cases had a lesional etiology. We describe a stereo-EEG (SEEG) study in a patient with drug-resistant/non-lesional SHE. Patient was screened for known mutations associated with SHE. SEEG disclosed bilateral synchronous and independent activity prevailing on the right central-anterior cingulate cortex, without a clear spatially defined epileptogenic zone. Due to the lack of a clear epileptogenic zone, surgery was contraindicated. Years later a DEPDC5 mutation was identified. We suggest that genetic analysis should be considered before performing SEEG study in a patient with drug resistant non-lesional SHE, in the presence of seizures in wakefulness and unclear anatomo-electroclinical correlation. If DEPDC5 mutations are identified, the presurgical evaluation should be tailored to look for MRI-negative focal cortical dysplasia and a wide epileptogenic network. The appropriate management and potential benefit of surgery for genetic non-lesional epilepsy have yet to be clarified. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.
Bhattacharyya, Saugat; Konar, Amit; Tibarewala, D N
The paper proposes a novel approach toward EEG-driven position control of a robot arm by utilizing motor imagery, P300 and error-related potentials (ErRP) to align the robot arm with desired target position. In the proposed scheme, the users generate motor imagery signals to control the motion of the robot arm. The P300 waveforms are detected when the user intends to stop the motion of the robot on reaching the goal position. The error potentials are employed as feedback response by the user. On detection of error the control system performs the necessary corrections on the robot arm. Here, an AdaBoost-Support Vector Machine (SVM) classifier is used to decode the 4-class motor imagery and an SVM is used to decode the presence of P300 and ErRP waveforms. The average steady-state error, peak overshoot and settling time obtained for our proposed approach is 0.045, 2.8% and 44 s, respectively, and the average rate of reaching the target is 95%. The results obtained for the proposed control scheme make it suitable for designs of prosthetics in rehabilitative applications.
2007). Large muscle movements, as well as neck, jaw, tongue and shoulder movements are known to generate disruptive artifacts in EEG signals, which...movements of eyes, neck and shoulders and EEG, little is known about hand and finger effects on EEG signal. Large muscle movement artifacts and ocular...solving Gamma 30 Hz and higher Blending of multiple brain functions ; Muscle related artifacts 2.2. EEG Artifacts EEG recordings are intended to
Giertuga, Katarzyna; Zakrzewska, Marta Z.; Bielecki, Maksymilian; Racicka-Pawlukiewicz, Ewa; Kossut, Malgorzata; Cybulska-Klosowicz, Anita
Numerous studies indicate that attention deficit/hyperactivity disorder (ADHD) is related to some developmental trends, as its symptoms change widely over time. Nevertheless, the etiology of this phenomenon remains ambiguous. There is a disagreement whether ADHD is related to deviations in brain development or to a delay in brain maturation. The model of deviated brain development suggests that the ADHD brain matures in a fundamentally different way, and does not reach normal maturity at any developmental stage. On the contrary, the delayed brain maturation model assumes that the ADHD brain indeed matures in a different, delayed way in comparison to healthy age-matched controls, yet eventually reaches proper maturation. We investigated age-related changes in resting-state EEG activity to find evidence to support one of the alternative models. A total of 141 children and teenagers participated in the study; 67 diagnosed with ADHD and 74 healthy controls. The absolute power of delta, theta, alpha, and beta frequency bands was analyzed. We observed a significant developmental pattern of decreasing absolute EEG power in both groups. Nonetheless, ADHD was characterized by consistently lower absolute EGG power, mostly in the theta frequency band, in comparison to healthy controls. Our results are in line with the deviant brain maturation theory of ADHD, as the observed effects of age-related changes in EEG power are parallel but different in the two groups. PMID:28620288
Full Text Available Numerous studies indicate that attention deficit/hyperactivity disorder (ADHD is related to some developmental trends, as its symptoms change widely over time. Nevertheless, the etiology of this phenomenon remains ambiguous. There is a disagreement whether ADHD is related to deviations in brain development or to a delay in brain maturation. The model of deviated brain development suggests that the ADHD brain matures in a fundamentally different way, and does not reach normal maturity at any developmental stage. On the contrary, the delayed brain maturation model assumes that the ADHD brain indeed matures in a different, delayed way in comparison to healthy age-matched controls, yet eventually reaches proper maturation. We investigated age-related changes in resting-state EEG activity to find evidence to support one of the alternative models. A total of 141 children and teenagers participated in the study; 67 diagnosed with ADHD and 74 healthy controls. The absolute power of delta, theta, alpha, and beta frequency bands was analyzed. We observed a significant developmental pattern of decreasing absolute EEG power in both groups. Nonetheless, ADHD was characterized by consistently lower absolute EGG power, mostly in the theta frequency band, in comparison to healthy controls. Our results are in line with the deviant brain maturation theory of ADHD, as the observed effects of age-related changes in EEG power are parallel but different in the two groups.
Gladwin, Thomas E.; t' Hart, Bernhard M.; de Jong, Ritske
Different aspects of preparation, especially processes related to knowing what to prepare versus applying that foreknowledge effectively, may be reflected in different types of brain activity, e.g., the lateralized readiness potential (LRP), beta-band event-related desynchronization and phase
Balogh, Lívia; Czobor, Pál
Error-related bioelectric signals constitute a special subgroup of event-related potentials. Researchers have identified two evoked potential components to be closely related to error processing, namely error-related negativity (ERN) and error-positivity (Pe), and they linked these to specific cognitive functions. In our article first we give a brief description of these components, then based on the available literature, we review differences in error-related evoked potentials observed in patients across psychiatric disorders. The PubMed and Medline search engines were used in order to identify all relevant articles, published between 2000 and 2009. For the purpose of the current paper we reviewed publications summarizing results of clinical trials. Patients suffering from schizophrenia, anorexia nervosa or borderline personality disorder exhibited a decrease in the amplitude of error-negativity when compared with healthy controls, while in cases of depression and anxiety an increase in the amplitude has been observed. Some of the articles suggest specific personality variables, such as impulsivity, perfectionism, negative emotions or sensitivity to punishment to underlie these electrophysiological differences. Research in the field of error-related electric activity has come to the focus of psychiatry research only recently, thus the amount of available data is significantly limited. However, since this is a relatively new field of research, the results available at present are noteworthy and promising for future electrophysiological investigations in psychiatric disorders.
Caat, Michael ten
Electroencephalography (EEG) measures electrical brain activity by electrodes attached to the scalp. Multichannel EEG refers to a measurement with a large number of electrodes. EEG has clinical as well as scientific applications, including neurology, psychology, pharmacy, linguistics, and biology.
Pfabigan, Daniela M; Seidel, Eva-Maria; Sladky, Ronald; Hahn, Andreas; Paul, Katharina; Grahl, Arvina; Küblböck, Martin; Kraus, Christoph; Hummer, Allan; Kranz, Georg S; Windischberger, Christian; Lanzenberger, Rupert; Lamm, Claus
The anticipation of favourable or unfavourable events is a key component in our daily life. However, the temporal dynamics of anticipation processes in relation to brain activation are still not fully understood. A modified version of the monetary incentive delay task was administered during separate functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) sessions in the same 25 participants to assess anticipatory processes with a multi-modal neuroimaging set-up. During fMRI, gain and loss anticipation were both associated with heightened activation in ventral striatum and reward-related areas. EEG revealed most pronounced P300 amplitudes for gain anticipation, whereas CNV amplitudes distinguished neutral from gain and loss anticipation. Importantly, P300, but not CNV amplitudes, were correlated to neural activation in the ventral striatum for both gain and loss anticipation. Larger P300 amplitudes indicated higher ventral striatum blood oxygen level dependent (BOLD) response. Early stimulus evaluation processes indexed by EEG seem to be positively related to higher activation levels in the ventral striatum, indexed by fMRI, which are usually associated with reward processing. The current results, however, point towards a more general motivational mechanism processing salient stimuli during anticipation. Copyright © 2014. Published by Elsevier Inc.
Cajochen, C.; Foy, R.; Dijk, D. J.; Czeisler, C. A. (Principal Investigator)
The effect of sleep deprivation (40 h) on topographic and temporal aspects of electroencephalographic (EEG) activity during sleep was investigated by all night spectral analysis in six young volunteers. The sleep-deprivation-induced increase of EEG power density in the delta and theta frequencies (1-7 Hz) during nonREM sleep, assessed along the antero-posterior axis (midline: Fz, Cz, Pz, Oz), was significantly larger in the more frontal derivations (Fz, Cz) than in the more parietal derivations (Pz, Oz). This frequency-specific frontal predominance was already present in the first 30 min of recovery sleep, and dissipated in the course of the 8-h sleep episode. The data demonstrate that the enhancement of slow wave EEG activity during sleep following extended wakefulness is most pronounced in frontal cortical areas.
Mladenović, D; Hrnčić, D; Rašić-Marković, A; Macut, Dj; Stanojlović, O
Liver failure is associated with a neuropsychiatric syndrome, known as hepatic encephalopathy (HE). Finasteride, inhibitor of neurosteroid synthesis, may improve the course of HE. The aim of our study was to investigate the influence of finasteride on mean and relative power density of EEG bands, determined by spectral analysis, in rat model of thioacetamide-induced HE. Male Wistar rats were divided into groups: (1) control; (2) thioacetamide-treated group, TAA (900 mg/kg); (3) finasteride-treated group, FIN (150 mg/kg); and (4) group treated with finasteride (150 mg/kg) and thioacetamide (900 mg/kg), FIN + TAA. Daily doses of FIN (50 mg/kg) and TAA (300 mg/kg) were administered during 3 subsequent days, and in FIN + TAA group FIN was administered 2 h before every dose of TAA. EEG was recorded 22-24 h after treatment and analyzed by fast Fourier transformation. While TAA did not induce significant changes in the beta band, mean and relative power in this band were significantly higher in FIN + TAA versus control group (p EEG changes that correspond to mild TAA-induced HE.
Markovska-Simoska, Silvana; Pop-Jordanova, Nada
In recent decades, resting state electroencephalographic (EEG) measures have been widely used to document underlying neurophysiological dysfunction in attention deficit hyperactivity disorder (ADHD). Although most EEG studies focus on children, there is a growing interest in adults with ADHD too. The aim of this study was to objectively assess and compare the absolute and relative EEG power as well as the theta/beta ratio in children and adults with ADHD. The evaluated sample comprised 30 male children and 30 male adults with ADHD diagnosed according to DSM-IV criteria. They were compared with 30 boys and 30 male adults matched by age. The mean age (±SD) of the children's group was 9 (±2.44) years and the adult group 35.8 (±8.65) years. EEG was recorded during an eyes-open condition. Spectral analysis of absolute (μV2) and relative power (%) was carried out for 4 frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-21 Hz). The findings obtained for ADHD children are increased absolute power of slow waves (theta and delta), whereas adults exhibited no differences compared with normal subjects. For the relative power spectra there were no differences between the ADHD and control groups. Across groups, the children showed greater relative power than the adults in the delta and theta bands, but for the higher frequency bands (alpha and beta) the adults showed more relative power than children. Only ADHD children showed greater theta/beta ratio compared to the normal group. Classification analysis showed that ADHD children could be differentiated from the control group by the absolute theta values and theta/beta ratio at Cz, but this was not the case with ADHD adults. The question that should be further explored is if these differences are mainly due to maturation processes or if there is a core difference in cortical arousal between ADHD children and adults. © EEG and Clinical Neuroscience Society (ECNS) 2016.
Toledo, Diana R; Barela, José A; Manzano, Gilberto M; Kohn, André F
The aim of this work was to compare cortical beta oscillatory activity between young (YA) and older (OA) adults during the assessment of ankle proprioception. We analyzed the response time (RT) to kinesthetic perception and beta event-related desynchronization/synchronization (ERD/ERS) in response to passive ankle movement applied at a slow speed, 0.5°/s. The relationship between ERD/ERS and RT was investigated by classifying the signals into fast-, medium-, and slow-RT. The results showed a temporal relationship between beta oscillation changes and RT for both groups, i.e., earlier ERD and ERS were obtained for trials with faster response time. ERD was larger and delayed in OA compared to the YA, and beta ERS was present only for OA. These findings suggest that a less efficient proprioceptive signaling reaching the brain of OA requires a higher level of brain processing and hence the differences in ERD potentials between YA and OA. Furthermore, the occurrence of ERS in OA might represent a compensatory strategy of active cortical resetting for adequate sensorimotor behavior due to the age-related reduced peripheral input and neuromuscular impairments. Altered balance between excitatory and inhibitory intracortical activity in older adults presumably explains the changes in beta oscillations. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Gärtner, Matti; Bajbouj, Malek
Mood states have a strong impact on how we process incoming information. It has been proposed that positive mood facilitates elaborative, relational encoding, whereas negative mood promotes a more careful, stimulus-driven encoding style. Previous electrophysiological studies have linked successful information encoding to power increases in slow (30 Hz) gamma oscillations, as well as to power decreases in midrange (8-30 Hz) alpha/beta oscillations. Whether different mood states modulate encoding-related oscillations has not been investigated yet. In order to address this question, we used an experimental mood induction procedure and recorded electroencephalograms from 20 healthy participants while they performed a free recall memory task after positive and negative mood induction. We found distinct oscillatory patterns in positive and negative mood. Successful encoding in positive mood was accompanied by widespread power increases in the delta band, whereas encoding success in negative mood was specifically accompanied by frontal power decreases in the beta band. On the behavioral level, memory performance was enhanced in positive mood. Our findings show that mood differentially modulates the neural correlates of successful information encoding and thus contribute to an understanding of how mood shapes different processing styles. © The Author (2014). Published by Oxford University Press. For Permissions, please email: email@example.com.
Li, Yingli; O'Boyle, Michael
The electroencephalogram (EEG) was used to investigate variation in mental rotation (MR) strategies between males and females and different college majors. Beta activation was acquired from 40 participants (10 males and 10 females in physical science; 10 males and 10 females in social science) when performing the Vandenberg and Kuse (1978) mental…
Marshall, Peter J.; Young, Thomas; Meltzoff, Andrew N.
There is increasing interest in neurobiological methods for investigating the shared representation of action perception and production in early development. We explored the extent and regional specificity of EEG desynchronization in the infant alpha frequency range (6-9 Hz) during action observation and execution in 14-month-old infants.…
Kim, Min-Ki; Kim, Miyoung; Oh, Eunmi; Kim, Sung-Phil
.... Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity...
Kim, Min-Ki; Kim, Miyoung; Oh, Eunmi; Kim, Sung-Phil
.... Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity...
McEvoy, L. K.; Smith, M. E.; Gevins, A.
OBJECTIVE: Task-related EEG is sensitive to changes in cognitive state produced by increased task difficulty and by transient impairment. If task-related EEG has high test-retest reliability, it could be used as part of a clinical test to assess changes in cognitive function. The aim of this study was to determine the reliability of the EEG recorded during the performance of a working memory (WM) task and a psychomotor vigilance task (PVT). METHODS: EEG was recorded while subjects rested quietly and while they performed the tasks. Within session (test-retest interval of approximately 1 h) and between session (test-retest interval of approximately 7 days) reliability was calculated for four EEG components: frontal midline theta at Fz, posterior theta at Pz, and slow and fast alpha at Pz. RESULTS: Task-related EEG was highly reliable within and between sessions (r0.9 for all components in WM task, and r0.8 for all components in the PVT). Resting EEG also showed high reliability, although the magnitude of the correlation was somewhat smaller than that of the task-related EEG (r0.7 for all 4 components). CONCLUSIONS: These results suggest that under appropriate conditions, task-related EEG has sufficient retest reliability for use in assessing clinical changes in cognitive status.
Glickman, Matthew R.; Tang, Akaysha (University of New Mexico, Albuquerque, NM)
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.
Surjo R Soekadar
Full Text Available Objective: Transcranial direct current stimulation (tDCS improves motor learning and can influence emotional processing or attention. However, it remained unclear whether learned electroencephalography (EEG-based brain-machine interface (BMI control during tDCS is feasible and how application of transcranial electric currents during BMI control would interfere with feature-extraction of physiological brain signals. Here we tested this combination and evaluated stimulation-dependent artifacts across different EEG frequencies and stability of motor imagery-based BMI control. Approach: Ten healthy volunteers were invited to two BMI-sessions, each comprising two 60-trial blocks. During the trials, modulation of mu-rhythms (8-15Hz associated with motor imagery recorded over C4 was translated into online cursor movements on a computer screen. During block 2, either sham (session A or anodal tDCS (session B was applied at 1mA with the stimulation electrode placed 1cm anterior of C4. Main results: tDCS was associated with a significant signal power increase in the lower frequencies most evident in the signal spectrum of the EEG channel closest to the stimulation electrode. Stimulation-dependent signal power increase exhibited a decay of 12dB per decade, leaving frequencies above 9Hz unaffected. Analysis of BMI control performance did not indicate a difference between blocks and tDCS conditions. Conclusion: Application of tDCS during learned EEG-based self-regulation of brain oscillations above 9Hz is feasible and safe, and might improve applicability of BMI systems in patient populations.
Jyoti, Amar; Plano, Andrea; Riedel, Gernot; Platt, Bettina
Sleep disturbances are common in Alzheimer's disease (AD) and now assumed to contribute to disease onset and progression. Here, we investigated whether activity, sleep/wake pattern, and electroencephalogram (EEG) profiles are altered in the knock-in PLB1Triple mouse model from 5 to 21 months of age. PLB1Triple mice displayed a progressive increase in wakefulness and non-rapid eye movement sleep fragmentation from 9 months onward, whereas PLB1WT wild type controls showed such deterioration only at 21 months. Impaired habituation to spatial novelty was also detected in PLB1Triple mice. Hippocampal power spectra of transgenic mice revealed progressive, vigilance stage-, brain region-, and age-specific changes. Age had an impact on EEG spectra in both cohorts but led to accelerated genotype-dependent differences, ultimately affecting all bands at 21 months. Overall, although PLB1Triple animals display only subtle amyloid and tau pathologies, robust sleep-wake and EEG abnormalities emerged. We hypothesize that such endophenotypes are sensitive, noninvasive, and reliable biomarker to identify onset and progression of AD. Copyright © 2015 Elsevier Inc. All rights reserved.
Haidmayer, I; Schulter, G
Clicks were delivered in trains of 2,5 s duration at click repetition rates of 5 or 8/s to provoke rhythmical activity in the vertex-EEG ('driving response') and to condition the driving response to neutral stimuli in a discrimination paradigm. The Eysenck Personality Inventory was administered to define an independent personality variable, i.e. extraversion-introversion; the interaction of background activity, driving and conditioned driving response with the personality dimension was analyzed. In the background EEG there was a significant difference in the absolute power of the fast alpha-band between introverts and extraverts. Driving was only observed in the fundamental frequency of acoustic stimulation, not in the first harmonic. There was no interaction between driving response and extraversion-introversion or resting EEG activity. Classical conditioning of the driving response was successful with introverts only. Results were interpreted within the framework of Eysenck's personality theory. The possibility to study neurophysiological correlates of memory processes in humans by means of conditioned driving responses is discussed.
Full Text Available The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjects was created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the results of other so far published works. The classifier based on hidden Markov models was able to distinguish between movement and resting states (classification score of 94–100%, but it was unable to recognize the type of the movement. This is caused by the large fraction of other (nonmovement related EEG activities in the recorded signals. A classification method based on advanced EEG signal denoising is being currently developed to overcome this problem.
Spence, Jeffrey S; Brier, Matthew R; Hart, John; Ferree, Thomas C
Linear statistical models are used very effectively to assess task-related differences in EEG power spectral analyses. Mixed models, in particular, accommodate more than one variance component in a multisubject study, where many trials of each condition of interest are measured on each subject. Generally, intra- and intersubject variances are both important to determine correct standard errors for inference on functions of model parameters, but it is often assumed that intersubject variance is the most important consideration in a group study. In this article, we show that, under common assumptions, estimates of some functions of model parameters, including estimates of task-related differences, are properly tested relative to the intrasubject variance component only. A substantial gain in statistical power can arise from the proper separation of variance components when there is more than one source of variability. We first develop this result analytically, then show how it benefits a multiway factoring of spectral, spatial, and temporal components from EEG data acquired in a group of healthy subjects performing a well-studied response inhibition task. Copyright © 2011 Wiley Periodicals, Inc.
van der Meer, Johan; Pampel, André; van Someren, Eus; Ramautar, Jennifer; van der Werf, Ysbrand; Gomez-Herrero, German; Lepsien, Jöran; Hellrung, Lydia; Hinrichs, Hermann; Möller, Harald; Walter, Martin
This data set contains electroencephalography (EEG) data as well as simultaneous EEG with functional magnetic resonance imaging (EEG/fMRI) data. During EEG/fMRI, the EEG cap was outfitted with a hardware-based add-on consisting of carbon-wire loops (CWL). These yielded six extra'CWL' signals related
Petrovic, Jelena; Lazic, Katarina; Ciric, Jelena; Kalauzi, Aleksandar; Saponjic, Jasna
In order to identify the differences for the onset and progression of functionally distinct cholinergic innervation disorders, we investigated the effect of bilateral nucleus basalis (NB) and pedunculopontine tegmental nucleus (PPT) lesions on sleep/wake states and electroencephalographic (EEG) microstructure in rats, chronically implanted for sleep recording. Bilateral NB lesion transiently altered Wake/NREM duration within the sensorimotor cortex, and Wake/REM duration within the motor cortex, while there was no change in the sleep/wake states distributions following the bilateral PPT lesion. Bilateral PPT lesion sustainably increased the Wake/REM and REM/Wake transitions followed by inconsistent dysregulation of the NREM/REM and REM/NREM transitions in sensorimotor cortex, but oppositely by their increment throughout four weeks in motor cortex. Bilateral NB lesion sustainably decreased the NREM/REM and REM/NREM transitions during four weeks in the sensorimotor cortex, but oppositely increased them in the motor cortex. We have shown that the sustained beta and gamma augmentation within the sensorimotor and motor cortex, and across all sleep/wake states, simultaneously with Wake delta amplitude attenuation only within the sensorimotor cortex, were the underlying EEG microstructure for the sleep/wake states transitions structure disturbance following bilateral PPT lesion. In contrast, the bilateral NB lesion only augmented REM theta in sensorimotor cortex during three weeks. We have shown that the NB and PPT lesions induced differing, structure-related EEG microstructure and transition structure disturbances particularly expressed in motor cortex during NREM and REM sleep. We evidenced for the first time the different topographical expression of the functionally distinct cholinergic neuronal innervation impairment in rat. Copyright © 2013 Elsevier B.V. All rights reserved.
Daniel M. Blumberger
Full Text Available Combining transcranial magnetic stimulation (TMS with electroencephalography (EEG allows for the assessment of various neurophysiological processes in the human cortex. One of these paradigms, short-latency afferent inhibition (SAI, is thought to be a sensitive measure of cholinergic activity. In a previous study, we demonstrated the temporal pattern of this paradigm from both the motor (M1 and dorsolateral prefrontal cortex (DLPFC using simultaneous TMS–EEG recording. The SAI paradigm led to marked modulations at N100. In this study, we aimed to investigate the age-related effects on TMS-evoked potentials (TEPs with the SAI from M1 and the DLPFC in younger (18–59 years old and older (≥60 years old participants. Older participants showed significantly lower N100 modulation in M1–SAI as well as DLPFC–SAI compared to the younger participants. Furthermore, the modulation of N100 by DLPFC–SAI in the older participants correlated with executive function as measured with the Trail making test. This paradigm has the potential to non-invasively identify cholinergic changes in cortical regions related to cognition in older participants.
Askamp, Jessica; van Putten, Michel Johannes Antonius Maria
The sensitivity of routine EEG recordings for interictal epileptiform discharges in epilepsy is limited. In some patients, inpatient video-EEG may be performed to increase the likelihood of finding abnormalities. Although many agree that home EEG recordings may provide a cost-effective alternative
Luijtelaar, E.L.J.M. van; Verbraak, M.J.P.M.; Bunt, P.M. van den; Keijsers, G.P.J.; Arns, M.W.
The concept of burnout remains enigmatic since it is only determined by behavioral characteristics. Moreover, the differential diagnosis with depression and chronic fatigue syndrome is difficult. EEG-related variables in 13 patients diagnosed with burnout syndrome were compared with 13 healthy
Christos A. Frantzidis
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.
Frantzidis, Christos A; Vivas, Ana B; Tsolaki, Anthoula; Klados, Manousos A; Tsolaki, Magda; Bamidis, Panagiotis D
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.
Full Text Available We present a program (Ragu; Randomization Graphical User interface for statistical analyses of multichannel event-related EEG and MEG experiments. Based on measures of scalp field differences including all sensors, and using powerful, assumption-free randomization statistics, the program yields robust, physiologically meaningful conclusions based on the entire, untransformed, and unbiased set of measurements. Ragu accommodates up to two within-subject factors and one between-subject factor with multiple levels each. Significance is computed as function of time and can be controlled for type II errors with overall analyses. Results are displayed in an intuitive visual interface that allows further exploration of the findings. A sample analysis of an ERP experiment illustrates the different possibilities offered by Ragu. The aim of Ragu is to maximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest that interact with and bias statistics.
Koenig, Thomas; Kottlow, Mara; Stein, Maria; Melie-García, Lester
We present a program (Ragu; Randomization Graphical User interface) for statistical analyses of multichannel event-related EEG and MEG experiments. Based on measures of scalp field differences including all sensors, and using powerful, assumption-free randomization statistics, the program yields robust, physiologically meaningful conclusions based on the entire, untransformed, and unbiased set of measurements. Ragu accommodates up to two within-subject factors and one between-subject factor with multiple levels each. Significance is computed as function of time and can be controlled for type II errors with overall analyses. Results are displayed in an intuitive visual interface that allows further exploration of the findings. A sample analysis of an ERP experiment illustrates the different possibilities offered by Ragu. The aim of Ragu is to maximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest) that interact with and bias statistics.
Askamp, Jessica; van Putten, Michel J A M
The sensitivity of routine EEG recordings for interictal epileptiform discharges in epilepsy is limited. In some patients, inpatient video-EEG may be performed to increase the likelihood of finding abnormalities. Although many agree that home EEG recordings may provide a cost-effective alternative to these recordings, their use is still not introduced everywhere. We surveyed Dutch neurologists and patients and evaluated a novel mobile EEG device (Mobita, TMSi). Key specifications were compared with three other current mobile EEG devices. We shortly discuss algorithms to assist in the review process. Thirty percent (33 out of 109) of Dutch neurologists reported that home EEG recordings are used in their hospital. The majority of neurologists think that mobile EEG can have additional value in investigation of unclear paroxysms, but not in the initial diagnosis after a first seizure. Poor electrode contacts and signal quality, limited recording time and absence of software for reliable and effective assistance in the interpretation of EEGs have been important constraints for usage, but in recent devices discussed here, many of these problems have been solved. The majority of our patients were satisfied with the home EEG procedure and did not think that our EEG device was uncomfortable to wear, but they did feel uneasy wearing it in public. © 2013.
Kaare Bjarke Mikkelsen
Full Text Available A method for measuring electroencephalograms (EEG from the outer ear, so-called ear-EEG, has recently been proposed. The method could potentially enable robust recording of EEG in natural environments. The objective of this study was to substantiate the ear-EEG method by using a larger population of subjects and several paradigms. For rigour, we considered simultaneous scalp and ear-EEG recordings with common reference. More precisely, 32 conventional scalp electrodes and 12 ear electrodes allowed a thorough comparison between conventional and ear electrodes, testing several different placements of references.The paradigms probed of auditory onset response, mismatch negativity, auditory steady state response and alpha power attenuation.By comparing event related potential (ERP waveforms from the mismatch response paradigm, the signal measured from the ear electrodes was found to reflect the same cortical activity as that from nearby scalp electrodes. It was also found that referencing the ear-EEG electrodes to another within-ear electrode affects the time-domain recorded waveform (relative to scalp recordings, but not the timing of individual components. It was furthermore found that auditory steady state responses and alpha-band modulation were measured reliably with the ear-EEG modality. Finally, our findings showed that the auditory mismatch response was difficult to monitor with the ear-EEG. We conclude that ear-EEG yields similar performance as conventional EEG for spectrogram-based analysis, similar timing of ERP components, and equal signal strength for sources close to the ear. Ear-EEG can reliably measure activity from regions of the cortex which are located close to the ears, especially in paradigms employing frequency-domain analyses.
Rummel, C.; Abela, E.; Hauf, M.; Wiest, R.; Schindler, K.
Epileptic seizures are associated with high behavioral stereotypy of the patients. In the EEG of epilepsy patients characteristic signal patterns can be found during and between seizures. Here we use ordinal patterns to analyze EEGs of epilepsy patients and quantify the degree of signal determinism. Besides relative signal redundancy and the fraction of forbidden patterns we introduce the fraction of under-represented patterns as a new measure. Using the logistic map, parameter scans are performed to explore the sensitivity of the measures to signal determinism. Thereafter, application is made to two types of EEGs recorded in two epilepsy patients. Intracranial EEG shows pronounced determinism peaks during seizures. Finally, we demonstrate that ordinal patterns may be useful for improving analysis of non-invasive simultaneous EEG-fMRI.
Lier, Hester van
Brain activity and behaviour are related to each other. Psychoactive drugs can influence both brain activity and behaviour. In order to be able to understand the interplay between brain activity as measured by the electroencephalogram (EEG), behaviour, and psychoactive drugs, it is not sufficient to describe changes in either behaviour or EEG separately. Rather, changes in EEG caused by psychoactive drugs should be described in direct concurrent relation with the subject's ongoing behaviour. ...
Full Text Available Burst suppression (BS is an electroencephalogram (EEG pattern that is characterized by brief bursts of spikes, sharp waves, or slow waves of relatively high amplitude alternating with periods of relatively flat EEG or isoelectric periods. The pattern is usually associated with coma, severe encephalopathy of various etiologies, or general anesthesia. We describe an unusual case of anoxic brain injury in which a BS pattern was seen during behaviorally defined sleep during a routine outpatient EEG study.
May 20, 2004 ... Epilepsy is primarily a clinical diagnosis, but the EEG ... seizure onset and the epilepsy syndrome. However, a normal inter-ictal EEG can never refute or exclude a clinical diagno- sis of epilepsy. Organic mental disorders is increasingly an ... to metabolic changes, infections, toxins, trauma and tumours.
Lopes da Silva, F.; Mulert, C.; Lemieux, L.
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
... test. If it's necessary for your child to sleep during the EEG, the doctor will suggest ways to help make this easier. The Procedure An EEG can be done in the doctor's office, a lab, or a hospital. Your child will be asked to lie on ...
May 20, 2004 ... 13th National Psychiatry Congress. The EEG in psychiatry. Roland Eastman. Division of Neurology, University of Cape Town, Cape Town, South Africa orders. Epilepsy is primarily a clinical diagnosis, but the EEG may provide strong support by the finding of inter-ictal epi- leptogenic discharges and also be ...
Full Text Available During Non-Rapid Eye Movement sleep (NREM the brain is relatively disconnected from the environment, while connectedness between brain areas is also decreased. Evidence indicates that these dynamic connectivity changes are delivered by microstructural elements of sleep: short periods of environmental stimuli evaluation followed by sleep promoting procedures. The connectivity patterns of the latter, among other aspects of sleep microstructure, are still to be fully elucidated.We suggest here a methodology for the assessment and investigation of the connectivity patterns of EEG microstructural elements, such as sleep spindles. The methodology combines techniques in the preprocessing, estimation, error assessing and visualization of results levels in order to allow the detailed examination of the connectivity aspects (levels and directionality of information flow over frequency and time with notable resolution, while dealing with the volume conduction and EEG reference assessment. The high temporal and frequency resolution of the methodology will allow the association between the microelements and the dynamically forming networks that characterise them, and consequently possibly reveal aspects of the EEG microstructure. The proposed methodology is initially tested on artificially generated signals for proof of concept and subsequently applied to real EEG recordings via a custom built MATLAB-based tool developed for such studies. Preliminary results from 843 fast sleep spindles recorded in whole night sleep of 5 healthy volunteers indicate a prevailing pattern of interactions between centroparietal and frontal regions.We demonstrate hereby an opening to our knowledge attempt to estimate the scalp EEG connectivity that characterizes fast sleep spindles via an EEG-element connectivity methodology we propose. The application of the latter, via a computational tool we developed suggests it is able to investigate the connectivity patterns related to the
O'Sullivan, S S
BACKGROUND: The EEG is a commonly requested test on patients attending psychiatric services, predominantly to investigate for a possible organic brain syndrome causing behavioural changes. AIMS: To assess referrals for EEG from psychiatric services in comparison with those from other sources. We determine which clinical factors were associated with an abnormal EEG in patients referred from psychiatric sources. METHODS: A retrospective review of EEG requests in a 1-year period was performed. Analysis of referral reasons for psychiatric patients was undertaken, and outcome of patients referred from psychiatric services post-EEG was reviewed. RESULTS: One thousand four hundred and seventy EEGs were reviewed, of which 91 (6.2%) were referred from psychiatry. Neurology service referrals had detection rates of abnormal EEGs of 27%, with psychiatric referrals having the lowest abnormality detection rate of 17.6% (p < 0.1). In psychiatric-referred patients the only significant predictors found of an abnormal EEG were a known history of epilepsy (p < 0.001), being on clozapine (p < 0.05), and a possible convulsive seizure (RR = 6.51). Follow-up data of 53 patients did not reveal a significant clinical impact of EEG results on patient management. CONCLUSIONS: Many patients are referred for EEG from psychiatric sources despite a relatively low index of suspicion of an organic brain disorders, based on reasons for referral documented, with an unsurprising low clinical yield.
Clark, Jonathan B.; Riley, Terrence
As clinical aerospace neurologists we do not favor using screening EEG in pilot selection on unselected and otherwise asymptomatic individuals. The role of EEG in aviation screening should be as an adjunct to diagnosis, and the decision to disqualify a pilot should never be based solely on the EEG. Although a policy of using a screening EEG in an unselected population might detect an individual with a potentially increased relative risk, it would needlessly exclude many applicants who would probably never have a seizure. A diagnostic test performed on an asymptomatic individual without clinical indications, in a population with a low prevalence of disease (seizure) may be of limited or possibly detrimental value. We feel that rather than do EEGs on all candidates, a better approach would be to perform an EEG for a specific indication, such as family history of seizure, single convulsion (seizure) , history of unexplained loss of consciousness or head injury. Routine screening EEGs in unselected aviation applications are not done without clinical indication in the U.S. Air Force, Navy, or NASA. The USAF discontinued routine screening EEGs for selection in 1978, the U.S. Navy discontinued it in 1981 , and NASA discontinued it in 1995. EEG as an aeromedical screening tool in the US Navy dates back to 1939. The US Navy routinely used EEGs to screen all aeromedical personnel from 1961 to 1981. The incidence of epileptiform activity on EEG in asymptomatic flight candidates ranges from 0.11 to 2.5%. In 3 studies of asymptomatic flight candidates with epileptiform activity on EEG followed for 2 to 15 years, 1 of 31 (3.2%), 1 of 30 (3.3%), and 0 of 14 (0%) developed a seizure, for a cumulative risk of an individual with an epileptiform EEG developing a seizure of 2.67% (2 in 75). Of 28,658 student naval aviation personnel screened 31 had spikes and/or slow waves on EEG, and only 1 later developed a seizure. Of the 28,627 who had a normal EEG, 4 later developed seizures, or
Koivisto, Mika; Grassini, Simone; Hurme, Mikko; Salminen-Vaparanta, Niina; Railo, Henry; Vorobyev, Victor; Tallus, Jussi; Paavilainen, Teemu; Revonsuo, Antti
Clinical data and behavioral studies using transcranial magnetic stimulation (TMS) suggest right-hemisphere dominance for top-down modulation of visual processing in humans. We used concurrent TMS-EEG to directly test for hemispheric differences in causal influences of the right and left intraparietal cortex on visual event-related potentials (ERPs). We stimulated the left and right posterior part of intraparietal sulcus (IPS1) while the participants were viewing and rating the visibility of bilaterally presented Gabor patches. Subjective visibility ratings showed that TMS of right IPS shifted the visibility toward the right hemifield, while TMS of left IPS did not have any behavioral effect. TMS of right IPS, but not left one, reduced the amplitude of posterior N1 potential, 180-220 ms after stimulus-onset. The attenuation of N1 occurred bilaterally over the posterior areas of both hemispheres. Consistent with previous TMS-fMRI studies, this finding suggests that the right IPS has top-down control on the neural processing in visual cortex. As N1 most probably reflects reactivation of early visual areas, the current findings support the view that the posterior parietal cortex in the right hemisphere amplifies recurrent interactions in ventral visual areas during the time-window that is critical for conscious perception. Copyright © 2017. Published by Elsevier Ltd.
Bell, Martha Ann; Cuevas, Kimberly
Developmental research is enhanced by use of multiple methodologies for examining psychological processes. The electroencephalogram (EEG) is an efficient and relatively inexpensive method for the study of developmental changes in brain-behavior relations. In this review, we highlight some of the challenges for using EEG in cognitive development…
Farina, Benedetto; Speranza, Anna Maria; Dittoni, Serena; Gnoni, Valentina; Trentini, Cristina; Vergano, Carola Maggiora; Liotti, Giovanni; Brunetti, Riccardo; Testani, Elisa; Della Marca, Giacomo
In this study, we evaluated cortical connectivity modifications by electroencephalography (EEG) lagged coherence analysis, in subjects with dissociative disorders and in controls, after retrieval of attachment memories. We asked thirteen patients with dissociative disorders and thirteen age- and sex-matched healthy controls to retrieve personal attachment-related autobiographical memories through adult attachment interviews (AAI). EEG was recorded in the closed eyes resting state before and after the AAI. EEG lagged coherence before and after AAI was compared in all subjects. In the control group, memories of attachment promoted a widespread increase in EEG connectivity, in particular in the high-frequency EEG bands. Compared to controls, dissociative patients did not show an increase in EEG connectivity after the AAI. Conclusions: These results shed light on the neurophysiology of the disintegrative effect of retrieval of traumatic attachment memories in dissociative patients.
Shi, Yuan; He, DanDan; Qin, Fang
In this paper, we have done Bayes Discriminant analysis to EEG data of experiment objects which are recorded impersonally come up with a relatively accurate method used in feature extraction and classification decisions. In accordance with the strength of α wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we have done Bayes Discriminant analysis to EEG data of six objects. Results In use of part of EEG data of 63 people, we have done Bayes Discriminant analysis, the electrode classification accuracy rates is 64.4%. Bayes Discriminant has higher prediction accuracy, EEG features (mainly αwave) extract more accurate. Bayes Discriminant would be better applied to the feature extraction and classification decisions of EEG data.
Full Text Available We investigated age-related changes in electroencephalographic (EEG coupling of theta-, alpha-, and beta-frequency bands during bottom-up and top-down attention. Arrays were presented with either automatic pop-out (bottom-up or effortful search (top-down behavior to younger and older participants. The phase-locking value (PLV was used to estimate coupling strength between scalp recordings. Behavioral performance decreased with age, with a greater age-related decline in accuracy for the search than for the pop-out condition. Aging was associated with a declined coupling strength of theta and alpha frequency bands, with a greater age-related decline in whole-brain coupling values for the search than for the pop-out condition. Specifically, prefronto-frontal coupling in theta- and alpha-bands, fronto-parietal and parieto-occipital couplings in beta-band for younger group showed a right hemispheric dominance, which was reduced with aging to compensate for the inhibitory dysfunction. While pop-out target detection was mainly associated with greater parieto-occipital beta-coupling strength compared to search condition regardless of aging. Furthermore, prefronto-frontal coupling in theta-, alpha- and beta-bands, and parieto-occipital coupling in beta-band functioned as predictors of behavior for both groups. Taken together these findings provide evidence that prefronto-frontal coupling of theta-, alpha-, and beta-bands may serve as a possible basis of aging during visual attention, while parieto-occipital coupling in beta-band could serve for a bottom-up function and be vulnerable to top-down attention control for younger and older groups.
Piano, Carla; Imperatori, Claudio; Losurdo, Anna; Bentivoglio, Anna Rita; Cortelli, Pietro; Della Marca, Giacomo
To evaluate EEG functional connectivity in the sensory-motor network, during wake and sleep, in patients with Huntington Disease (HD). 23 patients with HD and 23 age- and sex-matched healthy controls were enrolled. EEG connectivity analysis was performed by means of exact Low Resolution Electric Tomography (eLORETA). In wake, HD patients showed an increase of delta lagged phase synchronization (T=3.60; pHuntington's Disease, and to define therapeutically strategies. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
Swee Sim Kok
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.
Dijk, D. J.; Duffy, J. F.
The light-entrainable circadian pacemaker located in the suprachiasmatic nucleus of the hypothalamus regulates the timing and consolidation of sleep by generating a paradoxical rhythm of sleep propensity; the circadian drive for wakefulness peaks at the end of the day spent awake, ie close to the onset of melatonin secretion at 21.00-22.00 h and the circadian drive for sleep crests shortly before habitual waking-up time. With advancing age, ie after early adulthood, sleep consolidation declines, and time of awakening and the rhythms of body temperature, plasma melatonin and cortisol shift to an earlier clock hour. The variability of the phase relationship between the sleep-wake cycle and circadian rhythms increases, and in old age sleep is more susceptible to internal arousing stimuli associated with circadian misalignment. The propensity to awaken from sleep advances relative to the body temperature nadir in older people, a change that is opposite to the phase delay of awakening relative to internal circadian rhythms associated with morningness in young people. Age-related changes do not appear to be associated with a shortening of the circadian period or a reduction of the circadian drive for wake maintenance. These changes may be related to changes in the sleep process itself, such as reductions in slow-wave sleep and sleep spindles as well as a reduced strength of the circadian signal promoting sleep in the early morning hours. Putative mediators and modulators of circadian sleep regulation are discussed.
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.
Full Text Available Simultaneous EEG-fMRI recordings have seen growing application in the evaluation of epilepsy, namely in the characterization of brain networks related to epileptic activity. In EEG-correlated fMRI studies, epileptic events are usually described as boxcar signals based on the timing information retrieved from the EEG, and subsequently convolved with a heamodynamic response function to model the associated BOLD changes. Although more flexible approaches may allow a higher degree of complexity for the haemodynamics, the issue of how to model these dynamics based on the EEG remains an open question. In this work, a new methodology for the integration of simultaneous EEG-fMRI data in epilepsy is proposed, which incorporates a transfer function from the EEG to the BOLD signal. Independent component analysis (ICA of the EEG is performed, and a number of metrics expressing different models of the EEG-BOLD transfer function are extracted from the resulting time courses. These metrics are then used to predict the fMRI data and to identify brain areas associated with the EEG epileptic activity. The methodology was tested on both ictal and interictal EEG-fMRI recordings from one patient with a hypothalamic hamartoma. When compared to the conventional analysis approach, plausible, consistent and more significant activations were obtained. Importantly, frequency-weighted EEG metrics yielded superior results than those weighted solely on the EEG power, which comes in agreement with previous literature. Reproducibility, specificity and sensitivity should be addressed in an extended group of patients in order to further validate the proposed methodology and generalize the presented proof of concept.
Full Text Available INTRODUCTION: Individuals with dyslexia exhibit associated learning deficits and impaired executive functions. The Wisconsin Card Sorting Test (WCST is a learning-based task that relies heavily on executive functioning, in particular, attention shift and working memory. Performance during early and late phases of a series within the task represents learning and implementation of a newly learned rule. Here, we aimed to examine two event-related potentials associated with learning, feedback-related negativity (FRN-P300 complex, in individuals with dyslexia performing the WCST. METHODS: Adolescents with dyslexia and age-matched typical readers performed the Madrid card sorting test (MCST, a computerized version of the WCST. Task performance, reading measures, and cognitive measures were collected. FRN and the P300 complex were acquired using the event-related potentials methodology and were compared in early vs late errors within a series. RESULTS: While performing the MCST, both groups showed a significant reduction in average reaction times and a trend toward decreased error rates. Typical readers performed consistently better than individuals with dyslexia. FRN amplitudes in early phases were significantly smaller in dyslexic readers, but were essentially equivalent to typical readers in the late phase. P300 amplitudes were initially smaller among readers with dyslexia and tended to decrease further in late phases. Differences in FRN amplitudes for early vs late phases were positively correlated with those of P300 amplitudes in the entire sample. CONCLUSION: Individuals with dyslexia demonstrate a behavioral and electrophysiological change within single series of the MCST. However, learning patterns seem to differ between individuals with dyslexia and typical readers. We attribute these differences to the lower baseline performance of individuals with dyslexia. We suggest that these changes represent a fast compensatory mechanism, demonstrating
Soekadar, S.R.; Witkowski, M.; Garcia Cossio, E.; Birbaumer, N.; Cohen, L.G.
Objective: Transcranial direct current stimulation (tDCS) improves motor learning and can affect emotional processing and attention. However, it is unclear whether learned electroencephalography (EEG)-based brain-machine interface (BMI) control during tDCS is feasible, how application of
Beersma, D.G.M.; Achermann, P.
Sleep interventions may have direct effects on slow-wave activity (SWA, i.e. power of the sleep EEG signal in the 0.75-4.5 Hz range) as well as indirect ones caused by changes in REM sleep (REMS) latency. The effects of changes in REMS latency on SWA were investigated by analysing simulations with a
Babiloni, Claudio; Brancucci, Alfredo; Arendt-Nielsen, Lars; Babiloni, Fabio; Capotosto, Paolo; Carducci, Filippo; Cincotti, Febo; Romano, Lara; Chen, Andrew C N; Rossini, Paolo Maria
The authors delineated the time evolution of alpha event-related desynchronization over human frontal, parietal, and primary sensorimotor areas during the expectancy of a go/no-go task. The main issue under investigation was whether anticipatory processes impinged upon cortical areas in sequential or parallel mode. Compared with the control condition, in the experimental condition there was an Alpha 1 desynchronization over the central midline, an Alpha 2 desynchronization increasing over primary sensorimotor areas, and an Alpha 3 desynchronization increasing in parallel over bilateral primary sensorimotor areas. These processes had different temporal features. Results disclose an anticipatory activity of central midline areas and primary sensorimotor areas in both parallel and sequential modes. This reflects an adaptive, energy-consuming strategy rather than an economic waiting for the go stimulus.
Ma, Zhanyu; Tan, Zheng-Hua; Prasad, Swati
Classification of the Electroencephalogram (EEG) signal is a challengeable task in the brain-computer interface systems. The marginalized discrete wavelet transform (mDWT) coefficients extracted from the EEG signals have been frequently used in researches since they reveal features related...... vector machine (SVM) based classifier, the SDMM based classifier performs more stable and shows a promising improvement, with both channel selection strategies....
Kamp, A.; Arnolds, D.E.A.T.; Lopes da Silva, F.H.; Boeijinga, P.; Aitink, W.
In cat the relation between various behaviours and the spectral properties of the hippocampal EEG was investigated. Both EEG and behaviour were quantified and results were evaluated statistically. Significant relationships were found between the properties of the hippocampal EEG and motor acts
Wulsin, D. F.; Gupta, J.R; Mani, R; Blanco, J. A.; Litt, B.
Clinical electroencephalography (EEG) records vast amounts of human complex data yet is still reviewed primarily by human readers. Deep Belief Nets (DBNs) are a relatively new type of multi-layer neural network commonly tested on two-dimensional image data, but are rarely applied to times-series data such as EEG. We apply DBNs in a semi-supervised paradigm to model EEG waveforms for classification and anomaly detection. DBN performance was comparable to standard classifiers on our EEG dataset...
Cincotti, F; Babiloni, C; Miniussi, C; Carducci, F; Moretti, D; Salinari, S; Pascual-Marqui, R; Rossini, P M; Babiloni, F
EEG scalp potential distributions recorded in humans are affected by low spatial resolution and by the dependence on the electrical reference used. High resolution EEG technologies are available to drastically increase the spatial resolution of the raw EEG. Such technologies include the computation of surface Laplacian (SL) of the recorded potentials, as well as the use of realistic head models to estimate the cortical sources via linear inverse procedure (low resolution brain electromagnetic tomography, LORETA). However, these deblurring procedures are generally used in conjunction with EEG recordings with 64-128 scalp electrodes and with realistic head models obtained via sequential magnetic resonance images (MRIs) of the subjects. Such recording setup it is not often available in the clinical context, due to both the unavailability of these technologies and the scarce compliance of the patients with them. In this study we addressed the use of SL and LORETA deblurring techniques to analyze data from a standard 10-20 system (19 electrodes) in a group of Alzheimer disease (AD) patients. EEG data related to unilateral finger movements were gathered from 10 patients affected by AD. SL and LORETA techniques were applied for source estimation of EEG data. The use of MRIs for the construction of head models was avoided by using the quasi-realistic head model of the Brain Imaging Neurology Institute of Montreal. A similar cortical activity estimated by the SL and LORETA techniques was observed during an identical time period of the acquired EEG data in the examined population. The results of the present study suggest that both SL and LORETA approaches can be usefully applied in the clinical context, by using quasi-realistic head modeling and a standard 10-20 system as electrode montage (19 electrodes). These results represent a reciprocal cross-validation of the two mathematically independent techniques in a clinical environment.
The relation between EEG prefrontal asymmetry and subjective feelings of mood following 24 hours of sleep deprivation Relação entre assimetria pré-frontal no EEG e sensações subjetivas de humor após 24 horas de privação de sono
Full Text Available Several studies have investigated the relationship between asymmetrical EEG activity over the frontal cortex and mood. This study aimed at investigating the association between state fluctuations in frontal alpha EEG asymmetry and state changes followed by 24 h of sleep deprivation (SD. Our results show that sleep deprivation caused a significant alteration in the asymmetry values. Activation shifted from the left hemisphere, before SD, to the right hemisphere, after SD, in all frontal electrode pairs. In addition, according to the self-rating scale of SD-related mood effects, subjects became significantly less alerted and active, and sleepier. According to these results, increased right prefrontal activation might be potentially associated with the negative mood states typically seen after sleep deprivation, although the causal relationship is still uncertain. However, more studies will be necessary to establish the viability of EEG asymmetry and the cerebral lateralization hypothesis to explain the SD-related affective changes.Diversos estudos têm investigado a relação entre a atividade assimétrica do EEG no córtex frontal e mudanças no humor. Adotando tal abordagem, o presente estudo teve como objetivo investigar a associação entre os estados de oscilação na assimetria frontal de alfa e mudanças no estado emocional ou motivacional após 24h de privação de sono. Os resultados mostram que 24h de privação de sono ocasionaram alterações significativas nos valores de assimetria. Ativação cerebral mudou do hemisfério esquerdo, antes da privação de sono, para o hemisfério direito, após a privação de sono, em todos os pares de eletrodos frontais. Além disso, de acordo com a escala relacionada aos efeitos subjetivos do humor após privação de sono, os sujeitos mostraram-se significativamente menos alerta e ativos e mais sonolentos. É possível que as duas variáveis estejam associadas, embora a relação causal seja ainda
Coan, James A; Allen, John J B
Frontal EEG asymmetry appears to serve as (1) an individual difference variable related to emotional responding and emotional disorders, and (2) a state-dependent concomitant of emotional responding. Such findings, highlighted in this review, suggest that frontal EEG asymmetry may serve as both a moderator and a mediator of emotion- and motivation-related constructs. Unequivocal evidence supporting frontal EEG asymmetry as a moderator and/or mediator of emotion is lacking, as insufficient attention has been given to analyzing the frontal EEG asymmetries in terms of moderators and mediators. The present report reviews the frontal EEG asymmetry literature from the framework of moderators and mediators, and overviews data analytic strategies that would support claims of moderation and mediation.
Jones, N A; Field, T
EEG asymmetry, specifically greater relative right frontal activation, is associated with negative affect. Depressed adults show stable patterns of this asymmetry. The present study assessed the effects of massage therapy and music therapy on frontal EEG asymmetry in depressed adolescents. Thirty adolescents with greater relative right frontal EEG activation and symptoms of depression were given either massage therapy (n = 14) or music therapy (n = 16). EEG was recorded for three-minute periods before, during, and after therapy. Frontal EEG asymmetry was significantly attenuated during and after the massage and music sessions.
Oknina, L B; Kuptsova, S V; Romanov, A S; Masherov, E L; Kuznetsova, O A; Sharova, E V
The going of present pilot study is an analysis of features changes of EEG short pieces registered from 32 sites, at perception of musical melodies healthy examinees depending on logic (cognizance) and emotional (it was pleasant it was not pleasant) melody estimations. For this purpose changes of event-related synchronization/desynchronization, and also wavelet-synchrony of EEG-responses at 31 healthy examinees at the age from 18 till 60 years were compared. It is shown that at a logic estimation of music the melody cognizance is accompanied the event-related desynchronization in the left fronto-parietal-temporal area. At an emotional estimation of a melody the event-related synchronization in left fronto - temporal area for the pleasant melodies, desynchronization in temporal area for not pleasant and desynchronization in occipital area for the melodies which are not causing the emotional response is typical. At the analysis of wavelet-synchrony of EEG characterizing jet changes of interaction of cortical zones, it is revealed that the most distinct topographical distinctions concern type of processing of the heard music: logic (has learned-hasn't learned) or emotional (it was pleasant-it was not pleasant). If at an emotional estimation changes interhemispheric communications between associative cortical zones (central, frontal, temporal), are more expressed at logic - between inter - and intrahemispheric communications of projective zones of the acoustic analyzer (temporal area). It is supposed that the revealed event-related synchronization/desynhronization reflects, most likely, an activation component of an estimation of musical fragments whereas the wavelet-analysis provides guidance on character of processing of musical stimulus.
Verrusio, Walter; Ettorre, Evaristo; Vicenzini, Edoardo; Vanacore, Nicola; Cacciafesta, Mauro; Mecarelli, Oriano
The aim of this study is to investigate the influence of Mozart's music on brain activity through spectral analysis of the EEG in young healthy adults (Adults), in healthy elderly (Elderly) and in elderly with Mild Cognitive Impairment (MCI). EEG recording was performed at basal rest conditions and after listening to Mozart's K448 or "Fur Elise" Beethoven's sonatas. After listening to Mozart, an increase of alpha band and median frequency index of background alpha rhythm activity (a pattern of brain wave activity linked to memory, cognition and open mind to problem solving) was observed both in Adults and in Elderly. No changes were observed in MCI. After listening to Beethoven, no changes in EEG activity were detected. This results may be representative of the fact that said Mozart's music is able to "activate" neuronal cortical circuits related to attentive and cognitive functions. Copyright © 2015 Elsevier Inc. All rights reserved.
Engel, J. Jr.; Henry, T.R.; Risinger, M.W.; Mazziotta, J.C.; Sutherling, W.W.; Levesque, M.F.; Phelps, M.E.
One hundred fifty-three patients with medically refractory partial epilepsy underwent chronic stereotactic depth-electrode EEG (SEEG) evaluations after being studied by positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) and scalp-sphenoidal EEG telemetry. We carried out retrospective standardized reviews of local cerebral metabolism and scalp-sphenoidal ictal onsets to determine when SEEG recordings revealed additional useful information. FDG-PET localization was misleading in only 3 patients with temporal lobe SEEG ictal onsets for whom extratemporal or contralateral hypometabolism could be attributed to obvious nonepileptic structural defects. Two patients with predominantly temporal hypometabolism may have had frontal epileptogenic regions, but ultimate localization remains uncertain. Scalp-sphenoidal ictal onsets were misleading in 5 patients. For 37 patients with congruent focal scalp-sphenoidal ictal onsets and temporal hypometabolic zones, SEEG recordings never demonstrated extratemporal or contralateral epileptogenic regions; however, 3 of these patients had nondiagnostic SEEG evaluations. The results of subsequent subdural grid recordings indicated that at least 1 of these patients may have been denied beneficial surgery as a result of an equivocal SEEG evaluation. Weighing risks and benefits, it is concluded that anterior temporal lobectomy is justified without chronic intracranial recording when specific criteria for focal scalp-sphenoidal ictal EEG onsets are met, localized hypometabolism predominantly involves the same temporal lobe, and no other conflicting information has been obtained from additional tests of focal functional deficit, structural imaging, or seizure semiology.
Schulze, A. E.
Interest in sleep research was stimulated by the discovery of a number of physiological changes that occur during sleep and by the observed effects of sleep on physical and mental performance and status. The use of the relatively new methods of EEG measurement, transmission, and automatic scoring makes sleep analysis and categorization feasible. Sleep research involving the use of the EEG as a fundamental input has the potential of answering many unanswered questions involving physical and mental behavior, drug effects, circadian rhythm, and anesthesia.
Lin, Yuan-Pin; Jung, Tzyy-Ping; Chen, Jyh-Horng
This study explores the electroencephalographic (EEG) correlates of emotions during music listening. Principal component analysis (PCA) is used to correlate EEG features with complex music appreciation. This study also applies machine-leaning algorithms to demonstrate the feasibility of classifying EEG dynamics in four subjectively-reported emotional states. The high classification accuracy (81.58+/-3.74%) demonstrates the feasibility of using EEG features to assess emotional states of human subjects. Further, the spatial and spectral patterns of the EEG most relevant to emotions seem reproducible across subjects.
Yuan, Q; Liu, X H; Li, D C; Wang, H L; Liu, Y S
Objective. To observe the effect of noise and music on EEG power spectrum. Method. 12 healthy male pilots aged 30 +/- 0.58 years served as the subjects. Dynamic EEG from 16 regions was recorded during quiet, under noise or when listening to music using Oxford MR95 Holter recorder. Changes of EEG power spectrum of delta, theta, alpha1, alpha2, beta1 and beta2, frequency components in 16 regions were analyzed. Result. The total alpha1 power was significantly decreased, while the total theta power was significantly increased when listening to music; It implies that the interhemispheric transmission of information in the frontotemporal areas might be involved. Conclusion. The changes of the EEG power spectrum were closely related to man's emotions; relaxation was associated with music; Individual difference exists in the influence of sound on EEG.
Mette Thrane Foged
Full Text Available Concurrent EEG and fMRI is increasingly used to characterize the spatial-temporal dynamics of brain activity. However, most studies to date have been limited to conventional echo-planar imaging (EPI. There is considerable interest in integrating recently developed high-speed fMRI methods with high-density EEG to increase temporal resolution and sensitivity for task-based and resting state fMRI, and for detecting interictal spikes in epilepsy. In the present study using concurrent high-density EEG and recently developed high-speed fMRI methods, we investigate safety of radiofrequency (RF related heating, the effect of EEG on cortical signal-to-noise ratio (SNR in fMRI, and assess EEG data quality.The study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors that were equipped with 64- and 256-channel MR-compatible EEG systems, respectively, and receive only array head coils. Data were collected in 11 healthy controls (3 males, age range 18-70 years and 13 patients with epilepsy (8 males, age range 21-67 years. Three of the healthy controls were scanned with the 256-channel EEG system, the other subjects were scanned with the 64-channel EEG system. Scalp surface temperature, SNR in occipital cortex and head movement were measured with and without the EEG cap. The degree of artifacts and the ability to identify background activity was assessed by visual analysis by a trained expert in the 64 channel EEG data (7 healthy controls, 13 patients.RF induced heating at the surface of the EEG electrodes during a 30-minute scan period with stable temperature prior to scanning did not exceed 1.0° C with either EEG system and any of the pulse sequences used in this study. There was no significant decrease in cortical SNR due to the presence of the EEG cap (p > 0.05. No significant differences in the visually analyzed EEG data quality were found between
Foged, Mette Thrane; Lindberg, Ulrich; Vakamudi, Kishore; Larsson, Henrik B W; Pinborg, Lars H; Kjær, Troels W; Fabricius, Martin; Svarer, Claus; Ozenne, Brice; Thomsen, Carsten; Beniczky, Sándor; Paulson, Olaf B; Posse, Stefan
Concurrent EEG and fMRI is increasingly used to characterize the spatial-temporal dynamics of brain activity. However, most studies to date have been limited to conventional echo-planar imaging (EPI). There is considerable interest in integrating recently developed high-speed fMRI methods with high-density EEG to increase temporal resolution and sensitivity for task-based and resting state fMRI, and for detecting interictal spikes in epilepsy. In the present study using concurrent high-density EEG and recently developed high-speed fMRI methods, we investigate safety of radiofrequency (RF) related heating, the effect of EEG on cortical signal-to-noise ratio (SNR) in fMRI, and assess EEG data quality. The study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors that were equipped with 64- and 256-channel MR-compatible EEG systems, respectively, and receive only array head coils. Data were collected in 11 healthy controls (3 males, age range 18-70 years) and 13 patients with epilepsy (8 males, age range 21-67 years). Three of the healthy controls were scanned with the 256-channel EEG system, the other subjects were scanned with the 64-channel EEG system. Scalp surface temperature, SNR in occipital cortex and head movement were measured with and without the EEG cap. The degree of artifacts and the ability to identify background activity was assessed by visual analysis by a trained expert in the 64 channel EEG data (7 healthy controls, 13 patients). RF induced heating at the surface of the EEG electrodes during a 30-minute scan period with stable temperature prior to scanning did not exceed 1.0° C with either EEG system and any of the pulse sequences used in this study. There was no significant decrease in cortical SNR due to the presence of the EEG cap (p > 0.05). No significant differences in the visually analyzed EEG data quality were found between EEG
Ney, JP; Van Der Goes, DN; Nuwer, MR; Nelson, L; Eccher, MA
Objectives: To evaluate the effect of intensive care unit continuous EEG (cEEG) monitoring on inpatient mortality, hospital charges, and length of stay. Methods: A retrospective cross-sectional study was conducted using the Nationwide Inpatient Sample, a dataset representing 20% of inpatient discharges in nonfederal US hospitals. Adult discharge records reporting mechanical ventilation and EEG (routine EEG or cEEG) were included. cEEG was compared with routine EEG alone in association with th...
Ruzas, Christopher M; DeWitt, Peter E; Bennett, Kimberly S; Chapman, Kevin E; Harlaar, Nicole; Bennett, Tellen D
Traumatic brain injury (TBI) causes substantial morbidity and mortality in US children. Post-traumatic seizures (PTS) occur in 11-42% of children with severe TBI and are associated with unfavorable outcome. Electroencephalographic (EEG) monitoring may be used to detect PTS and antiepileptic drugs (AEDs) may be used to treat PTS, but national rates of EEG and AED use are not known. The purpose of this study was to describe the frequency and timing of EEG and AED use in children hospitalized after severe TBI. Retrospective cohort study of 2165 children at 30 hospitals in a probabilistically linked dataset from the National Trauma Data Bank (NTDB) and the Pediatric Health Information Systems (PHIS) database, 2007-2010. We included children (age 24 h, and non-missing disposition. The primary outcomes were EEG and AED use. Overall, 31.8% of the cohort had EEG monitoring. Of those, 21.8% were monitored on the first hospital day. The median duration of EEG monitoring was 2.0 (IQR 1.0, 4.0) days. AEDs were prescribed to 52.0% of the cohort, of whom 61.8% received an AED on the first hospital day. The median duration of AED use was 8.0 (IQR 4.0, 17.0) days. EEG monitoring and AED use were more frequent in children with known risk factors for PTS. EEG monitoring and AED use were not related to hospital TBI volume. EEG use is relatively uncommon in children with severe TBI, but AEDs are frequently prescribed. EEG monitoring and AED use are more common in children with known risk factors for PTS.
Background:Autism is currently viewed as a genetically determined neurode- velopmental disorder although its definite underlying etiology remains to be established. Aim of the Study: Our purpose was to assess autism related morphological neuroimaging changes of the brain and EEG abnormalities in correlation to the.
Field, Tiffany; Martinez, Alex; Nawrocki, Thomas; Pickens, Jeffrey; Fox, Nathan A.; Schanberg, Saul
Fourteen chronically depressed female adolescents listened to rock music for a 23-minute session. EEG was recorded and saliva samples were collected to determine the effects of the music on stress hormone cortisol levels. No differences were reported for mood state; however, cortisol levels decreased and relative right-frontal activation was…
Grant, Arthur C.; Abdel-Baki, Samah G.; Omurtag, Ahmet; Sinert, Richard; Chari, Geetha; Malhotra, Schweta; Weedon, Jeremy; Fenton, Andre A.; Zehtabchi, Shahriar
Measuring the diagnostic accuracy (DA) of an EEG device is unconventional and complicated by imperfect interrater reliability. We sought to compare the DA of a miniature, wireless, battery-powered EEG device (“microEEG”) to a reference EEG machine in emergency department (ED) patients with altered mental status (AMS). 225 ED patients with AMS underwent 3 EEGs. EEG1 (Nicolet Monitor, “reference”) and EEG2 (microEEG) were recorded simultaneously with EEG cup electrodes using a signal splitter. ...
Balsters, Joshua H.; O'Connell, Redmond G.; Martin, Mary P.; Galli, Alessandra; Cassidy, Sarah M.; Kilcullen, Sophia M.; Delmonte, Sonja; Brennan, Sabina; Meaney, Jim F.; Fagan, Andrew J.; Bokde, Arun L. W.; Upton, Neil; Lai, Robert; Laruelle, Marc; Lawlor, Brian; Robertson, Ian H.
Rising life expectancies coupled with an increasing awareness of age-related cognitive decline have led to the unwarranted use of psychopharmaceuticals, including acetylcholinesterase inhibitors (AChEIs), by significant numbers of healthy older individuals. This trend has developed despite very limited data regarding the effectiveness of such drugs on non-clinical groups and recent work indicates that AChEIs can have negative cognitive effects in healthy populations. For the first time, we use a combination of EEG and simultaneous EEG/fMRI to examine the effects of a commonly prescribed AChEI (donepezil) on cognition in healthy older participants. The short- and long-term impact of donepezil was assessed using two double-blind, placebo-controlled trials. In both cases, we utilised cognitive (paired associates learning (CPAL)) and electrophysiological measures (resting EEG power) that have demonstrated high-sensitivity to age-related cognitive decline. Experiment 1 tested the effects of 5 mg/per day dosage on cognitive and EEG markers at 6-hour, 2-week and 4-week follow-ups. In experiment 2, the same markers were further scrutinised using simultaneous EEG/fMRI after a single 5 mg dose. Experiment 1 found significant negative effects of donepezil on CPAL and resting Alpha and Beta band power. Experiment 2 replicated these results and found additional drug-related increases in the Delta band. EEG/fMRI analyses revealed that these oscillatory differences were associated with activity differences in the left hippocampus (Delta), right frontal-parietal network (Alpha), and default-mode network (Beta). We demonstrate the utility of simple cognitive and EEG measures in evaluating drug responses after acute and chronic donepezil administration. The presentation of previously established markers of age-related cognitive decline indicates that AChEIs can impair cognitive function in healthy older individuals. To our knowledge this is the first study to identify the precise
Kondziella, Daniel; Friberg, Christian K; Wellwood, Ian; Reiffurth, Clemens; Fabricius, Martin; Dreier, Jens P
Continuous EEG (cEEG) may allow monitoring of patients with aneurysmal subarachnoid hemorrhage (SAH) for delayed cerebral ischemia (DCI) and seizures, including non-convulsive seizures (NCSz), and non-convulsive status epilepticus (NCSE). We aimed to evaluate: (a) the diagnostic accuracy of cEEG as a confirmatory test, (b) the prognostic value of EEG patterns suggestive of seizures and DCI, and (c) the effectiveness of intensified neuromonitoring using cEEG in terms of improved clinical outcome following SAH. A systematic review was performed with eligible studies selected from multiple indexing databases through June 2014. The methodological quality of these studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. Eighteen studies were identified, including cEEG data from 481 patients with aneurysmal SAH. NCSz were diagnosed in 7-18 % of patients; NCSE in 3-13 %. NCSE was associated with increased age (mean age 68 years) and mortality (82-100 %) compared to the entire patient population (53.9 years; mortality 13 %; p values EEG patterns suggestive of DCI included decreased alpha/delta ratio, relative alpha variability, and total power. All studies were subject to a high risk of bias concerning patient selection and cEEG methodology. cEEG monitoring following SAH detects an increased number of subclinical seizures and may predict DCI many hours in advance. NCSE is associated with high mortality and morbidity, whereas for DCI identified by cEEG this association is less clear. Prospective randomized controlled multicenter trials are needed to evaluate the benefits (or risks) of intensified treatment of seizures and DCI following SAH.
Drinkenburg, Wilhelmus H I M; Ahnaou, Abdallah; Ruigt, Gé S F
Current research on the effects of pharmacological agents on human neurophysiology finds its roots in animal research, which is also reflected in contemporary animal pharmaco-electroencephalography (p-EEG) applications. The contributions, present value and translational appreciation of animal p-EEG-based applications are strongly interlinked with progress in recording and neuroscience analysis methodology. After the pioneering years in the late 19th and early 20th century, animal p-EEG research flourished in the pharmaceutical industry in the early 1980s. However, around the turn of the millennium the emergence of structurally and functionally revealing imaging techniques and the increasing application of molecular biology caused a temporary reduction in the use of EEG as a window into the brain for the prediction of drug efficacy. Today, animal p-EEG is applied again for its biomarker potential - extensive databases of p-EEG and polysomnography studies in rats and mice hold EEG signatures of a broad collection of psychoactive reference and test compounds. A multitude of functional EEG measures has been investigated, ranging from simple spectral power and sleep-wake parameters to advanced neuronal connectivity and plasticity parameters. Compared to clinical p-EEG studies, where the level of vigilance can be well controlled, changes in sleep-waking behaviour are generally a prominent confounding variable in animal p-EEG studies and need to be dealt with. Contributions of rodent pharmaco-sleep EEG research are outlined to illustrate the value and limitations of such preclinical p-EEG data for pharmacodynamic and chronopharmacological drug profiling. Contemporary applications of p-EEG and pharmaco-sleep EEG recordings in animals provide a common and relatively inexpensive window into the functional brain early in the preclinical and clinical development of psychoactive drugs in comparison to other brain imaging techniques. They provide information on the impact of
Miraglia, Francesca; Vecchio, Fabrizio; Bramanti, Placido; Rossini, Paolo Maria
Applying graph theory, we investigated how cortical sources small worldness (SW) of resting EEG in eyes-closed/open (EC/EO) differs in amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) subjects with respect to normal elderly (Nold). EEG was recorded in 30 Nold, 30 aMCI, and 30 AD during EC and EO. Undirected and weighted cortical brain network was built to evaluate graph core measures. eLORETA lagged linear connectivity was used to weight the network. In Nold, in EO condition, the brain network is characterized by more SW (higher SW) in alpha bands and less SW (lower SW) in beta2 and gamma bands. In aMCI, SW has the same trend, except for delta and theta bands where the network shows less small worldness. AD shows a similar trend of Nold, but with less fluctuations between EO/EC conditions. Furthermore, in both conditions, aMCI SW architecture presents midway properties between AD and Nold. At low frequencies (delta e theta bands) in EC, aMCI group presents network's architecture similar to Nold, while in EO aMCI, SW is superimposable to AD ones. In resting state condition, aMCI small-world architecture presents midway topological properties between AD subjects and healthy controls, confirming the hypothesis that aMCI is an intermediate step along the disease progression. We proposed the application of graph theory to EEG in reactivity to EO in order to find a marker of diagnosis that - in association with other techniques of neuroimaging - could be sensitive to the progression of MCI or conversion into AD. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
Klein, Carina; Hänggi, Jürgen; Luechinger, Roger; Jäncke, Lutz
Besides the benefit of combining electroencephalography (EEG) and magnetic resonance imaging (MRI), much effort has been spent to develop algorithms aimed at successfully cleaning the EEG data from MRI-related gradient and ballistocardiological artifacts. However, there are also studies showing a negative influence of the EEG on MRI data quality. Therefore, in the present study, we focused for the first time on the influence of the EEG on morphometric measurements of T1-weighted MRI data (voxel- and surfaced-based morphometry). Here, we demonstrate a strong influence of the EEG on cortical thickness, surface area, and volume as well as subcortical volumes due to local EEG-related inhomogeneities of the static magnetic (B0) and the gradient field (B1). In a second step, we analyzed the signal-to-noise ratios for both the anatomical and the functional data when recorded simultaneously with EEG and MRI and compared them to the ratios of the MRI data without simultaneous EEG measurements. These analyses revealed consistently lower signal-to-noise ratios for anatomical as well as functional MRI data during simultaneous EEG registration. In contrast, further analyses of T2*-weighted images provided reliable results independent of whether including the individuals' T1-weighted image with or without the EEG cap in the fMRI preprocessing stream. Based on our findings, we strongly recommend against using the structural images obtained during simultaneous EEG-MRI recordings for further anatomical data analysis. Copyright © 2014 Elsevier Inc. All rights reserved.
Haleh Aghajani; Marc Garbey; Ahmet Omurtag
...). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM...
Sitnikova, E Iu; Grubov, V V; Khramov, A E; Koronovskiĭ, A A
It is known that sleep spindles are produced by thalamo-cortical system spontaneously during the slow-wave sleep; pathological processes in thalamo-cortical network might cause absence epilepsy. The aim of this study was to examine age-dependent changes in time-frequency structure of sleep spindles in parallel to a progressive increase in amount of absence seizures in WAG/Rij rat model. EEG was consistently recorded at the age of 5, 7 and 9 months by means of epidural electrodes implanted in the frontal cortex. Continuous wavelet transform was used for automatic identification and further time-frequency analysis of sleep spindles in EEG. It was found that the mean duration of epileptic discharges and total duration of epileptic activity increased with age, whereas the length of sleep spindles decreased. Mean frequency of oscillations within a spindle was used as a criterion for dividing sleep spindles in three categories: "slow" (9.3 Hz), "tr ansitional" (11.4 Hz) and "fast" (13.5 Hz). "Slow" and "transitional" spindles in 5-months animals displayed an increase in frequency from the beginning towards the end. It was shown that the higher incidence of epilepsy corresponded to the lower duration of sleep spindles (all types). Mean frequency of "transitional" and "fast" spindles was higher in rats with more intensive epileptic discharges. In general, high epileptic activity in WAG/Rij rats corresponded to the most substantial changes within "transitional" spindles, whereas changes within slow and fast spindles were moderate.
Davide Vito Moretti
Full Text Available Objective: temporo-parietal cortex thinning is associated to mild cognitive impairment (MCI due to Alzheimer disease (AD. The increase of EEG upper/low alpha power ratio has been associated with AD-converter MCI subjects. We investigated the association of alpha3/alpha2 ratio with patterns of cortical thickness in MCI.Methods: 74 adult subjects with MCI underwent clinical and neuropsychological evaluation, electroencephalogram (EEG recording and high resolution 3D magnetic resonance imaging (MRI. Alpha3/alpha2 power ratio as well as cortical thickness was computed for each subject. Three MCI groups were detected according to increasing tertile values of upper/low alpha power ratio . Difference of cortical thikness among the groups was estimated. Pearson’s r was used to assess the topography of the correlation between cortical thinning and memory impairment.Results: High upper/low alpha power ratio group had total cortical grey matter (CGM volume reduction of 471 mm2 than low upper/low alpha power ratio group (p
Lineu C. Fonseca
Full Text Available OBJECTIVE: To evaluate the relationship between specific cognitive aspects and quantitative EEG measures, in patients with mild or moderate Alzheimer's disease (AD. METHOD: Thirty-eight AD patients and 31 controls were assessed by CERAD neuropsychological battery (Consortium to Establish a Registry for AD and the electroencephalogram (EEG. The absolute power and coherences EEG measures were calculated at rest. The correlations between the cognitive variables and the EEG were evaluated. RESULTS: In the AD group there were significant correlations between different coherence EEG measures and Mini-Mental State Examination, verbal fluency, modified Boston naming, word list memory with repetition, word list recall and recognition, and constructional praxis (p<0.01. These correlations were all negative for the delta and theta bands and positive for alpha and beta. There were no correlations between cognitive aspects and absolute EEG power. CONCLUSION: The coherence EEG measures reflect different forms in the relationship between regions related to various cognitive dysfunctions.
Electroencephalographic data recorded on the human scalp can be modeled as a linear mixture of underlying dipolar source generators. The characterization of such generators is the aim of several families of signal processing methods. In this HDR we consider in several details three of such families, namely 1) EEG distributed inverse solutions, 2) diagonalization methods, including spatial filtering and blind source separation and 3) Riemannian geometry. We highlight our contributions in each ...
Shou, Guofa; Ding, Lei
The present study aimed to investigate the sensitivity of independent component analysis (ICA)- and channel-based methods in detecting electroencephalography (EEG) spatial-spectral-temporal signatures of performance errors. 128-channel EEG signals recorded from 18 subjects, who performed a color-word matching Stroop task, were analyzed. The spatial-spectral-temporal patterns in event-related potentials (ERPs) and oscillatory activities (i.e., power and phase) were measured at four selected channels, i.e., FCz, Pz, O1 and O2, from original EEG data after preprocessing, EEG data after additional current source density (CSD) transform, and back-projected EEG data from individual ICs after additional ICA analysis. Pair-wise correlation coefficient (CC) and mutual information (MI), calculated from three EEG data at four selected channels, were compared to examine mutual correlations in EEG signals obtained through three different means. Thereafter, EEG signatures of errors from these three means were statistically compared at multiple time windows in the contrast of error and correct responses. Significantly decreased CC and MI values were observed in CSD- and ICA-processed EEGs as compared with original EEG, with the smallest CC and MI in ICA EEG. Similar error patterns in ERPs and peri-response oscillatory activities were detected in all three EEGs, whereas the pre-stimulus and post-stimulus error-related oscillatory patterns identified in ICA EEG were either not or only partially detected in both original EEG and CSD EEGs in general. Both CSD and ICA processes can largely reduce signal correlations due to the volume conduction effect in original EEG, and EEG signatures of errors are better detected by ICA-based method than channel-based method (i.e., original and CSD EEGs). ICA provides the best sensitivity to detect EEG signatures linked to specific neural processes via disentangling superimposed channel-level EEG signals into distinct neurocognitive process-related
Předmětem této bakalářské práce je seznámení se signálem EEG. Jsou zde rozebrány jeho vlastnosti, použití a způsoby zpracování. Hlavní část se zabývá segmentací EEG signálu. Dvě metody segmentace jsou realizovány v programu Matlab, a to adaptivní segmentace na základě míry diference střední amplitudy a míry diference střední frekvence a adaptivní segmentace na základě míry diference odhadnuté z rychlé Fourierovy transformace. Funkčnost algoritmu je ověřena na reálných EEG signálech. Subjec...
Tato bakalářská práce se zabývá analýzou spánkových EEG, která je provedena pomocí výpočtu vybraných parametrů z časové a frekvenční oblasti. Parametry se počítají z jednotlivých úseků EEG signálů, které odpovídají jednotlivým spánkovým fázím. Na základě analýzy se rozhodne, které parametry EEG jsou vhodné pro automatickou detekci fází a která metoda je vhodnější pro hodnocení dat v hypnogramu. K analýze byl použit program MATLAB, ve kterém byla daná data porovnána. This thesis deals with ...
Kiroy, Valery N; Aslanyan, Elena V; Lazurenko, Dmitry M; Minyaeva, Nadezhda R; Bakhtin, Oleg M
Spectral power (SP) of EEG alpha and beta-2 frequencies in different cortical areas has been used for neurofeedback training to control a graphic interface in different scenarios. The results show that frequency range and brain cortical areas are associated with high or low efficiency of voluntary control. Overall, EEG phenomena observed in the course of training are largely general changes involving extensive brain areas and frequency bands. Finally, we have demonstrated EEG patterns that dynamically switch with a specific feature in different tasks within one training, after a relatively short period of training.
Tortella-Feliu, M; Morillas-Romero, A; Balle, M; Llabrés, J; Bornas, X; Putman, P
Variability in both frontal and parietal spontaneous EEG activity, using α and β band power and θ/β and δ/β ratios, was explored in a sample of 96 healthy volunteers as a potential correlate of individual differences in spontaneous emotion regulation (SER). Following a baseline EEG recording, participants were asked to continuously rate their discomfort while looking at affective pictures, as well as for a period of time after exposure. Greater spontaneous β band power in parietal locations, lower frontal and parietal δ/β ratios, and lower parietal θ/β ratio were associated with lower ratings of discomfort after the offset of unpleasant pictures. Moreover, lower parietal δ/β ratio was also related to less time needed to recover from discomfort after exposure to aversive pictures, while only a greater frontal and parietal α band power appeared to be associated with faster recovery from discomfort induced by normative-neutral pictures. However, parietal δ/β ratio was the only predictor of both minimum discomfort ratings and time needed to downregulate following exposure to unpleasant pictures, and frontal α band power the only spontaneous EEG index that predicted variability in spontaneous down-regulation after the exposure to normative-neutral pictures. Results are discussed focusing on the utility of diverse spontaneous EEG measures in several cortical regions when capturing trait-like individual differences in emotion regulation capabilities and processes. Copyright © 2014 Elsevier B.V. All rights reserved.
Park, Jin Young; Min, Byoung-Kyong; Jung, Young-Chul; Pak, Hyensou; Jeong, Yeon-Hong; Kim, Eosu
Illumination conditions appear to influence working efficacy in everyday life. In the present study, we obtained electroencephalogram (EEG) correlates of working-memory load, and investigated how these waveforms are modulated by illumination conditions. We hypothesized that illumination conditions may affect cognitive performance. We designed an EEG study to monitor and record participants' EEG during the Sternberg working memory task under four different illumination conditions. Illumination conditions were generated with a factorial design of two color-temperatures (3000 and 7100 K) by two illuminance levels (150 and 700 lx). During a working memory task, we observed that high illuminance led to significantly lower frontal EEG theta activity than did low illuminance. These differences persisted despite no significant difference in task performance between illumination conditions. We found that the latency of an early event-related potential component, such as N1, was significantly modulated by the illumination condition. The fact that the illumination condition affects brain activity but not behavioral performance suggests that the lighting conditions used in the present study did not influence the performance stage of behavioral processing. Nevertheless, our findings provide objective evidence that illumination conditions modulate brain activity. Further studies are necessary to refine the optimal lighting parameters for facilitating working memory. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.
David Hairston, W; Whitaker, Keith W; Ries, Anthony J; Vettel, Jean M; Cortney Bradford, J; Kerick, Scott E; McDowell, Kaleb
Electroencephalography (EEG) holds promise as a neuroimaging technology that can be used to understand how the human brain functions in real-world, operational settings while individuals move freely in perceptually-rich environments. In recent years, several EEG systems have been developed that aim to increase the usability of the neuroimaging technology in real-world settings. Here, the usability of three wireless EEG systems from different companies are compared to a conventional wired EEG system, BioSemi's ActiveTwo, which serves as an established laboratory-grade 'gold standard' baseline. The wireless systems compared include Advanced Brain Monitoring's B-Alert X10, Emotiv Systems' EPOC and the 2009 version of QUASAR's Dry Sensor Interface 10-20. The design of each wireless system is discussed in relation to its impact on the system's usability as a potential real-world neuroimaging system. Evaluations are based on having participants complete a series of cognitive tasks while wearing each of the EEG acquisition systems. This report focuses on the system design, usability factors and participant comfort issues that arise during the experimental sessions. In particular, the EEG systems are assessed on five design elements: adaptability of the system for differing head sizes, subject comfort and preference, variance in scalp locations for the recording electrodes, stability of the electrical connection between the scalp and electrode, and timing integration between the EEG system, the stimulus presentation computer and other external events.
Hairston, W. David; Whitaker, Keith W.; Ries, Anthony J.; Vettel, Jean M.; Cortney Bradford, J.; Kerick, Scott E.; McDowell, Kaleb
Electroencephalography (EEG) holds promise as a neuroimaging technology that can be used to understand how the human brain functions in real-world, operational settings while individuals move freely in perceptually-rich environments. In recent years, several EEG systems have been developed that aim to increase the usability of the neuroimaging technology in real-world settings. Here, the usability of three wireless EEG systems from different companies are compared to a conventional wired EEG system, BioSemi’s ActiveTwo, which serves as an established laboratory-grade ‘gold standard’ baseline. The wireless systems compared include Advanced Brain Monitoring’s B-Alert X10, Emotiv Systems’ EPOC and the 2009 version of QUASAR’s Dry Sensor Interface 10-20. The design of each wireless system is discussed in relation to its impact on the system’s usability as a potential real-world neuroimaging system. Evaluations are based on having participants complete a series of cognitive tasks while wearing each of the EEG acquisition systems. This report focuses on the system design, usability factors and participant comfort issues that arise during the experimental sessions. In particular, the EEG systems are assessed on five design elements: adaptability of the system for differing head sizes, subject comfort and preference, variance in scalp locations for the recording electrodes, stability of the electrical connection between the scalp and electrode, and timing integration between the EEG system, the stimulus presentation computer and other external events.
Hofmeijer, Jeannette; Beernink, Tim M J; Bosch, Frank H; Beishuizen, Albertus; Tjepkema-Cloostermans, Marleen C; van Putten, Michel J A M
Early identification of potential recovery of postanoxic coma is a major challenge. We studied the additional predictive value of EEG. Two hundred seventy-seven consecutive comatose patients after cardiac arrest were included in a prospective cohort study on 2 intensive care units. Continuous EEG was measured during the first 3 days. EEGs were classified as unfavorable (isoelectric, low-voltage, burst-suppression with identical bursts), intermediate, or favorable (continuous patterns), at 12, 24, 48, and 72 hours. Outcome was dichotomized as good or poor. Resuscitation, demographic, clinical, somatosensory evoked potential, and EEG measures were related to outcome at 6 months using logistic regression analysis. Analyses of diagnostic accuracy included receiver operating characteristics and calculation of predictive values. Poor outcome occurred in 149 patients (54%). Single measures unequivocally predicting poor outcome were an unfavorable EEG pattern at 24 hours, absent pupillary light responses at 48 hours, and absent somatosensory evoked potentials at 72 hours. Together, these had a specificity of 100% and a sensitivity of 50%. For the remaining 203 patients, who were still in the "gray zone" at 72 hours, a predictive model including unfavorable EEG patterns at 12 hours, absent or extensor motor response to pain at 72 hours, and higher age had an area under the curve of 0.90 (95% confidence interval 0.84-0.96). Favorable EEG patterns at 12 hours were strongly associated with good outcome. EEG beyond 24 hours had no additional predictive value. EEG within 24 hours is a robust contributor to prediction of poor or good outcome of comatose patients after cardiac arrest. © 2015 American Academy of Neurology.
Beernink, Tim M.J.; Bosch, Frank H.; Beishuizen, Albertus; Tjepkema-Cloostermans, Marleen C.; van Putten, Michel J.A.M.
Objectives: Early identification of potential recovery of postanoxic coma is a major challenge. We studied the additional predictive value of EEG. Methods: Two hundred seventy-seven consecutive comatose patients after cardiac arrest were included in a prospective cohort study on 2 intensive care units. Continuous EEG was measured during the first 3 days. EEGs were classified as unfavorable (isoelectric, low-voltage, burst-suppression with identical bursts), intermediate, or favorable (continuous patterns), at 12, 24, 48, and 72 hours. Outcome was dichotomized as good or poor. Resuscitation, demographic, clinical, somatosensory evoked potential, and EEG measures were related to outcome at 6 months using logistic regression analysis. Analyses of diagnostic accuracy included receiver operating characteristics and calculation of predictive values. Results: Poor outcome occurred in 149 patients (54%). Single measures unequivocally predicting poor outcome were an unfavorable EEG pattern at 24 hours, absent pupillary light responses at 48 hours, and absent somatosensory evoked potentials at 72 hours. Together, these had a specificity of 100% and a sensitivity of 50%. For the remaining 203 patients, who were still in the “gray zone” at 72 hours, a predictive model including unfavorable EEG patterns at 12 hours, absent or extensor motor response to pain at 72 hours, and higher age had an area under the curve of 0.90 (95% confidence interval 0.84–0.96). Favorable EEG patterns at 12 hours were strongly associated with good outcome. EEG beyond 24 hours had no additional predictive value. Conclusions: EEG within 24 hours is a robust contributor to prediction of poor or good outcome of comatose patients after cardiac arrest. PMID:26070341
Full Text Available Electrode arrays are sometimes implanted in the brains of patients with intractable epilepsy to better localize seizure foci before epilepsy surgery. Analysis of intracranial EEG (iEEG recordings is typically performed in the electrode channel domain without explicit separation of the sources that generate the signals. However, intracranial EEG signals, like scalp EEG signals, could be linear mixtures of local activity and volume conducted activity arising in multiple source areas. Independent component analysis (ICA has recently been applied to scalp EEG data, and shown to separate the signal mixtures into independently generated brain and non-brain source signals. Here, we applied ICA to un-mix source signals from intracranial EEG recordings from four epilepsy patients during a visually cued finger movement task in the presence of background pathological brain activity. This ICA decomposition demonstrated that the iEEG recordings were not maximally independent, but rather are linear mixtures of activity from multiple sources. Many of the independent component (IC projections to the iEEG recording grid were consistent with sources from single brain regions, including components exhibiting classic movement-related dynamics. Notably, the largest IC projection to each channel accounted for no more than 20%-80% of the channel signal variance, implying that in general intracranial recordings cannot be accurately interpreted as recordings of independent brain sources. These results suggest that ICA can be used to identify and monitor major field sources of local and distributed functional networks generating iEEG data. ICA decomposition methods are useful for improving the fidelity of source signals of interest, likely including distinguishing the sources of pathological brain activity.
Rogasch, Nigel C; Daskalakis, Zafiris J; Fitzgerald, Paul B
Paired-pulse transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is a method for studying cortical inhibition from the dorsolateral prefrontal cortex (DLPFC). However, little is known about the mechanisms underlying TMS-evoked cortical potentials (TEPs) from this region, let alone inhibition of these components. The aim of this study was to assess cortical inhibition of distinct TEPs and oscillations in the DLPFC using TMS-EEG and to investigate the relationship of these mechanisms to working memory. 30 healthy volunteers received single and paired (interstimulus interval = 100 msec) TMS to the left DLPFC. Variations in long-interval cortical inhibition (LICI) of different TEP peaks (N40, P60, N100) and different TMS-evoked oscillations (alpha, lower beta, upper beta, gamma) were compared between individuals. Variation in N100 slope following single pulse TMS, another putative marker of inhibition, was also compared with LICI of each measure. Finally, these measures were correlated with performance of a working memory task. LICI resulted in significant suppression of all TEP peaks and TMS-evoked oscillations (all p Variation in N100 slope correlated with LICI of N40 and beta oscillations. In addition, LICI of P60 and N100 were differentially correlated with working memory performance. The results suggest that both the LICI paradigm and N100 following single pulse TMS reflect complementary methods for assessing GABAB-mediated cortical inhibition in the DLPFC. Furthermore, these measures demonstrate the importance of prefrontal GABAB-mediated inhibitory control for working memory performance. Copyright © 2014 Elsevier Ltd. All rights reserved.
Pirini, Marco; Mancini, Martina; Farella, Elisabetta; Chiari, Lorenzo
The control of postural sway depends on the dynamic integration of multi-sensory information in the central nervous system. Augmentation of sensory information, such as during auditory biofeedback (ABF) of the trunk acceleration, has been shown to improve postural control. By means of quantitative electroencephalography (EEG), we examined the basic processes in the brain that are involved in the perception and cognition of auditory signals used for ABF. ABF and Fake ABF (FAKE) auditory stimulations were delivered to 10 healthy naive participants during quiet standing postural tasks, with eyes-open and closed. Trunk acceleration and 19-channels EEG were recorded at the same time. Advanced, state-of-the-art EEG analysis and modeling methods were employed to assess the possibly differential, functional activation, and localization of EEG spectral features (power in α, β, and γ bands) between the FAKE and the ABF conditions, for both the eyes-open and the eyes-closed tasks. Participants gained advantage by ABF in reducing their postural sway, as measured by a reduction of the root mean square of trunk acceleration during the ABF compared to the FAKE condition. Population-wise localization analysis performed on the comparison FAKE - ABF revealed: (i) a significant decrease of α power in the right inferior parietal cortex for the eyes-open task; (ii) a significant increase of γ power in left temporo-parietal areas for the eyes-closed task; (iii) a significant increase of γ power in the left temporo-occipital areas in the eyes-open task. EEG outcomes supported the idea that ABF for postural control heavily modulates (increases) the cortical activation in healthy participants. The sites showing the higher ABF-related modulation are among the known cortical areas associated with multi-sensory, perceptual integration, and sensorimotor integration, showing a differential activation between the eyes-open and eyes-closed conditions. Copyright © 2010 Elsevier B.V. All
Lopez-Gordo, M. A.; Sanchez-Morillo, D.; Valle, F. Pelayo
Electroencephalography (EEG) emerged in the second decade of the 20th century as a technique for recording the neurophysiological response. Since then, there has been little variation in the physical principles that sustain the signal acquisition probes, otherwise called electrodes. Currently, new advances in technology have brought new unexpected fields of applications apart from the clinical, for which new aspects such as usability and gel-free operation are first order priorities. Thanks to new advances in materials and integrated electronic systems technologies, a new generation of dry electrodes has been developed to fulfill the need. In this manuscript, we review current approaches to develop dry EEG electrodes for clinical and other applications, including information about measurement methods and evaluation reports. We conclude that, although a broad and non-homogeneous diversity of approaches has been evaluated without a consensus in procedures and methodology, their performances are not far from those obtained with wet electrodes, which are considered the gold standard, thus enabling the former to be a useful tool in a variety of novel applications. PMID:25046013
Full Text Available EEG involves recording, analysis, and interpretation of voltages recorded on the human scalp originating from brain grey matter. EEG is one of the favorite methods to study and understand processes that underlie behavior. This is so, because EEG is relatively cheap, easy to wear, light weight and has high temporal resolution. In terms of behavior, this encompasses actions, such as movements, that are performed in response to the environment. However, there are methodological difficulties when recording EEG during movement such as movement artifacts. Thus, most studies about the human brain have examined activations during static conditions. This article attempts to compile and describe relevant methodological solutions that emerged in order to measure body and brain dynamics during motion. These descriptions cover suggestions of how to avoid and reduce motion artifacts, hardware, software and techniques for synchronously recording EEG, EMG, kinematics, kinetics and eye movements during motion. Additionally, we present various recording systems, EEG electrodes, caps and methods for determination of real/custom electrode positions. In the end we will conclude that it is possible to record and analyze synchronized brain and body dynamics related to movement or exercise tasks.
Hu, Bin; Peng, Hong; Zhao, Qinglin; Hu, Bo; Majoe, Dennis; Zheng, Fang; Moore, Philip
Electroencephalogram (EEG) plays an important role in E-healthcare systems, especially in the mental healthcare area, where constant and unobtrusive monitoring is desirable. In the context of OPTIMI project, a novel, low cost, and light weight wearable EEG sensor has been designed and produced. In order to improve the performance and reliability of EEG sensors in real-life settings, we propose a method to evaluate the quality of EEG signals, based on which users can easily adjust the connection between electrodes and their skin. Our method helps to filter invalid EEG data from personal trials in both domestic and office settings. We then apply an algorithm based on Discrete Wavelet Transformation (DWT) and Adaptive Noise Cancellation (ANC) which has been designed to remove ocular artifacts (OA) from the EEG signal. DWT is applied to obtain a reconstructed OA signal as a reference while ANC, based on recursive least squares, is used to remove the OA from the original EEG data. The newly produced sensors were tested and deployed within the OPTIMI framework for chronic stress detection. EEG nonlinear dynamics features and frontal asymmetry of theta, alpha, and beta bands have been selected as biological indicators for chronic stress, showing relative greater right anterior EEG data activity in stressful individuals. Evaluation results demonstrate that our EEG sensor and data processing algorithms have successfully addressed the requirements and challenges of a portable system for patient monitoring, as envisioned by the EU OPTIMI project.
Pedreira, C; Vaudano, A E; Thornton, R C; Chaudhary, U J; Vulliemoz, S; Laufs, H; Rodionov, R; Carmichael, D W; Lhatoo, S D; Guye, M; Quian Quiroga, R; Lemieux, L
Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert
Foged, Mette Thrane; Lindberg, Ulrich; Vakamudi, Kishore
) related heating, the effect of EEG on cortical signal-to-noise ratio (SNR) in fMRI, and assess EEG data quality. MATERIALS AND METHODS: The study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors...
Wilson, John A; Nordal, Helge J
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.
Michel, V; Mazzola, L; Lemesle, M; Vercueil, L
Long-term EEG in adults includes three modalities: sleep deprived-EEG lasting 1 to 3 hours, 24 hours ambulatory-EEG and continuous prolonged video-EEG lasting from several hours to several days. The main indications of long-term EEG are: syndromic classification of epilepsy; search for interictal discharges when epilepsy is suspected or for the purpose of therapeutic evaluation; positive diagnosis of paroxysmal clinical events; and pre-surgical evaluation of drug-resistant epilepsy. Sleep deprived-EEG and ambulatory-EEG are indicated to detect interictal discharges in order to validate a syndromic classification of epilepsy when standard EEG is negative. These exams can help in evaluating treatment efficacy, especially when clinical evaluation is difficult. Long-term video EEG is indicated for drug-resistant epilepsy, to analyze electro-clinical correlations in a pre-surgical evaluation context, and to refine a positive diagnosis when paroxysmal clinical events are frequent. Copyright © 2015. Published by Elsevier SAS.
Electroencephalogy ( EEG ) Feedback In Decision- Making The goal of this project is to investigate whether Electroencephalogy ( EEG ) can provide useful...feedback when training rapid decision-making. More specifically, EEG will allow us to provide online feedback about the neural decision processes...Electroencephalogy ( EEG ) Feedback In Decision-Making Report Title The goal of this project is to investigate whether Electroencephalogy ( EEG ) can provide useful
Chevallier, Justyna A; Von Allmen, Gretchen K; Koenig, Mary Kay
Seizures constitute a frequent yet under-described manifestation of mitochondrial disorders (MDs). The aim of this study was to describe electroencephalography (EEG) findings and clinical seizure types in a population of children and adults with mitochondrial disease. Retrospective chart review of 165 records of children and adults with mitochondrial disease seen in the University of Texas Houston Mitochondrial Center between 2007 and 2012 was performed; all subjects were diagnosed with confirmed mitochondrial disease. EEG findings and clinical data, including seizure semiology and response to antiepileptic drugs (AEDs), were analyzed and categorized. Sixty-six percent (109/165) of subjects had a routine EEG performed. Sixty-one percent (67/109) of EEG studies were abnormal and 85% (56/67) had epileptiform discharges. The most common EEG finding was generalized slowing (40/67, 60%). The most frequent category of epileptiform activity seen was multifocal discharges (41%), followed by focal (39%) and generalized (39%) discharges. Clinical seizures were seen in 55% of subjects and the most common types of seizures observed were complex partial (37%) and generalized tonic-clonic (GTC; 37%). The most common seizure type in patients with mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) was GTC (33%), with generalized or focal discharges seen on EEG. In Leigh syndrome GTC (11%) and complex partial (11%) seizures were the most frequent types. Of 60 subjects with clinical seizures, 28% were intractable to medical treatment. Mitochondrial disorder should be included in the list of differential diagnosis in any child that presents with encephalopathy, seizures, and a fluctuating clinical course. Given the relatively high prevalence of EEG abnormalities in patients with MD, EEG should be performed during initial evaluation in all patients with MD, not only upon clinical suspicion of epilepsy. Wiley Periodicals, Inc. © 2014 International
Samdin, S Balqis; Ting, Chee-Ming; Salleh, Sh-Hussain; Ariff, A K; Mohd Noor, A B
This paper investigates the use of linear dynamic models (LDMs) to improve classification of single-trial EEG signals. Existing dynamic classification of EEG uses discrete-state hidden Markov models (HMMs) based on piecewise-stationary assumption, which is inadequate for modeling the highly non-stationary dynamics underlying EEG. The continuous hidden states of LDMs could better describe this continuously changing characteristic of EEG, and thus improve the classification performance. We consider two examples of LDM: a simple local level model (LLM) and a time-varying autoregressive (TVAR) state-space model. AR parameters and band power are used as features. Parameter estimation of the LDMs is performed by using expectation-maximization (EM) algorithm. We also investigate different covariance modeling of Gaussian noises in LDMs for EEG classification. The experimental results on two-class motor-imagery classification show that both types of LDMs outperform the HMM baseline, with the best relative accuracy improvement of 14.8% by LLM with full covariance for Gaussian noises. It may due to that LDMs offer more flexibility in fitting the underlying dynamics of EEG.
Pastena, Lucio; Formaggio, Emanuela; Storti, Silvia Francesca; Faralli, Fabio; Melucci, Massimo; Gagliardi, Riccardo; Ricciardi, Lucio; Ruffino, Giovanni
The aim was to investigate and define possible alterations in cerebral activity during prolonged hyperbaric oxygen exposure and decompression as compared to baseline activity. Thirty-two channel electroencephalography (EEG) was recorded with a Bluetooth EEG system in 11 subjects. A 20-min EEG recording was carried out under three different conditions: breathing air inside a hyperbaric chamber at sea level; breathing oxygen at a simulated depth of 18 msw; breathing air at sea level after decompression. Relative EEG power was estimated in frequency ranges. During oxygen breathing, brain activity showed an early fast delta decrease in the posterior regions, with a synchronous and significant increase in alpha in the same regions. After decompression, the delta relative power decrease was uniformly distributed over the cerebral cortex until minute 8, and the alpha relative power was maximal in the posterior regions during the first 2 min. These results may be relevant for establishing a reference point in future studies on oxygen-sensitive subjects who reported problems during oxygen diving. Significant changes in EEG relative power suggest that it may be possible to define and recognize landmarks of oxygen-induced brain activity, which would be useful in the medical treatment of subjects reporting "oxygen-toxicity diving-related problems". Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
Delorme, Arnaud; Palmer, Jason; Onton, Julie; Oostenveld, Robert; Makeig, Scott
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...
Borowska Marta; Białobłocka Natalia
This paper reports on a multiresolution analysis of EEG signals. The dominant frequency components of signals with and without observed epileptic discharges were compared. The study showed that there were significant differences in dominant frequency between the signals with epileptic discharges and the signals without discharges. This gives the ability to identify epilepsy during EEG examination. The frequency of the signals coming from the frontal, central, parietal and occipital channels a...
[The Influence of the Functional State of Brain Regulatory Structures on the Programming, Selective Regulation and Control of Cognitive Activity in Children. Report I: Neuropsychological and EEG Analysis of Age-Related Changes in Brain Regulatory Functions in Children Aged 9-12 Years].
Semenova, A; Machinskaya, R I; Lomakin, D I
Age-related changes in brain regulatory functions in children aged from 9 to 12 years with typical development were studied by means of neuropsychological and EEG analysis. The participants of the study were 107 children without learning difficulties and behavior deviations; they were devided into three groups (9-10, 10-11 and 11-12 years). The neuropsychological tests revealed nonlinear age-related changes in different executive brain functions. The group of 10-11-year-old children showed better results in programming, in- hibition of impulsive reactions and in the perception of socially relevant information than the group of 9-10- year-old children. At the same time, these children had more difficulties with selective activity regulation as compared with the younger group. The difficulties were mainly caused by switching from one element of the program to another and by retention of learned sequence of actions. These children also showed a lower level of motivation for task performance. The children aged 11-12 years had less difficulties with selective activity regulation; however, impulsive behavior was more frequent; these children also had a higher level of task performance motivation than in children aged 10-11 years. The analysis of resting state EEG revealed age-related differences in deviated EEG patterns associated with non-optimal functioning of fronto-thalamic system and hypothalamic structures. The incidence of these two types of EEG patterns was significantly higher in children aged 10-11 years as compared with children aged 9-10 years. The EEG of the groups of 10-11 and 11-12-years-old children did not show any significant differences.
Vecchio, Fabrizio; Babiloni, Claudio; Lizio, Roberta; Fallani, Fabrizio De Vico; Blinowska, Katarzyna; Verrienti, Giulio; Frisoni, Giovanni; Rossini, Paolo M
The human brain contains an intricate network of about 100 billion neurons. Aging of the brain is characterized by a combination of synaptic pruning, loss of cortico-cortical connections, and neuronal apoptosis that provoke an age-dependent decline of cognitive functions. Neural/synaptic redundancy and plastic remodeling of brain networking, also secondary to mental and physical training, promote maintenance of brain activity and cognitive status in healthy elderly subjects for everyday life. However, age is the main risk factor for neurodegenerative disorders such as Alzheimer's disease (AD) that impact on cognition. Growing evidence supports the idea that AD targets specific and functionally connected neuronal networks and that oscillatory electromagnetic brain activity might be a hallmark of the disease. In this line, digital electroencephalography (EEG) allows noninvasive analysis of cortical neuronal synchronization, as revealed by resting state brain rhythms. This review provides an overview of the studies on resting state eyes-closed EEG rhythms recorded in amnesic mild cognitive impairment (MCI) and AD subjects. Several studies support the idea that spectral markers of these EEG rhythms, such as power density, spectral coherence, and other quantitative features, differ among normal elderly, MCI, and AD subjects, at least at group level. Regarding the classification of these subjects at individual level, the most previous studies showed a moderate accuracy (70-80%) in the classification of EEG markers relative to normal and AD subjects. In conclusion, resting state EEG makers are promising for large-scale, low-cost, fully noninvasive screening of elderly subjects at risk of AD.
Full Text Available Electroencephalography (EEG can be a valuable technique to assess electrophysiological changes related to dementia. In patients suspected of having dementia, the EEG is often quite informative. The sensitivity of the EEG to detect correlates of psychiatric disorders has been enhanced by means of quantitative methods of analysis (quantitative EEG. Quantitative features are extracted from, at least, 2 minutes of artifact-free, eyes closed, resting EEG, log-transformed to obtain Gaussianity, age-regressed, and Z-transformed relative to population norms (Neurometrics database. Using a subset of quantitative EEG (qEEG features, forward stepwise discriminant analyses are used to construct classifier functions. Along this vein, the main objective of this experiment is to distinguish profiles of qEEG, which differentiate depressive from demented patients (n = 125. The results showed that demented patients present deviations above the control group in variables associated to slow rhythms: Normed Monopolar Relative Power Theta for Cz and Normed Bipolar Relative Power Theta for Head. On the other hand, the deviation below the control group occurs with the variable associated to alpha rhythm: Normed Monopolar Relative Power Alpha for P3, in dementia. Using this method, the present investigation demonstrated high discriminant accuracy in separating Primary Degenerative Dementia from Major Depressive Disorder (Depression.
Snyder, Adam C.
The development and refinement of noninvasive techniques for imaging neural activity is of paramount importance for human neuroscience. Currently, the most accessible and popular technique is electroencephalography (EEG). However, nearly all of what we know about the neural events that underlie EEG signals is based on inference, because of the dearth of studies that have simultaneously paired EEG recordings with direct recordings of single neurons. From the perspective of electrophysiologists there is growing interest in understanding how spiking activity coordinates with large-scale cortical networks. Evidence from recordings at both scales highlights that sensory neurons operate in very distinct states during spontaneous and visually evoked activity, which appear to form extremes in a continuum of coordination in neural networks. We hypothesized that individual neurons have idiosyncratic relationships to large-scale network activity indexed by EEG signals, owing to the neurons' distinct computational roles within the local circuitry. We tested this by recording neuronal populations in visual area V4 of rhesus macaques while we simultaneously recorded EEG. We found substantial heterogeneity in the timing and strength of spike-EEG relationships and that these relationships became more diverse during visual stimulation compared with the spontaneous state. The visual stimulus apparently shifts V4 neurons from a state in which they are relatively uniformly embedded in large-scale network activity to a state in which their distinct roles within the local population are more prominent, suggesting that the specific way in which individual neurons relate to EEG signals may hold clues regarding their computational roles. PMID:26108954
Foged, Mette Thrane; Lindberg, Ulrich; Vakamudi, Kishore
PURPOSE: Concurrent EEG and fMRI is increasingly used to characterize the spatial-temporal dynamics of brain activity. However, most studies to date have been limited to conventional echo-planar imaging (EPI). There is considerable interest in integrating recently developed high-speed fMRI methods...... with high-density EEG to increase temporal resolution and sensitivity for task-based and resting state fMRI, and for detecting interictal spikes in epilepsy. In the present study using concurrent high-density EEG and recently developed high-speed fMRI methods, we investigate safety of radiofrequency (RF......) related heating, the effect of EEG on cortical signal-to-noise ratio (SNR) in fMRI, and assess EEG data quality. MATERIALS AND METHODS: The study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors...
Aghajani, Haleh; Garbey, Marc; Omurtag, Ahmet
We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should
Bodizs, Robert; Gombos, Ferenc; Kovacs, Ilona
Sleep EEG alterations are emerging features of several developmental disabilities, but detailed quantitative EEG data on the sleep phenotype of patients with Williams syndrome (WS, 7q11.23 microdeletion) is still lacking. Based on laboratory (Study I) and home sleep records (Study II) here we report WS-related features of the patterns of…
Schaefer, R.S.; Vlek, R.J.; Desain, P.W.M.
Previous work has shown that mental imagination of sound generally elicits an increase of alpha band activity (8-12Hz) in the electroencephalogram (EEG). In addition, alpha activity has been shown to be related to music processing. In the current study, EEG signatures were investigated for
Umair J. Chaudhary
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.
Agapov, S. N.; V. A. Bulanov; Zakharov, A. V.; Sergeeva, M. S.
Since it was first used in 1926, EEG has been one of the most useful instruments of neuroscience. In order to start using EEG data we need not only EEG apparatus, but also some analytical tools and skills to understand what our data mean. This article describes several classical analytical tools and also new one which appeared only several years ago. We hope it will be useful for those researchers who have only started working in the field of cognitive EEG.
Li, Feng; Li, Jiang; McKenzie, Frederic; Zhang, Guangfan; Wang, Wei; Pepe, Aaron; Xu, Roger; Schnell, Thomas; Anderson, Nick; Heitkamp, Dean
In this paper, we present methods to analyze and improve an EEG-based engagement assessment approach, consisting of data preprocessing, feature extraction and engagement state classification. During data preprocessing, spikes, baseline drift and saturation caused by recording devices in EEG signals are identified and eliminated, and a wavelet based method is utilized to remove ocular and muscular artifacts in the EEG recordings. In feature extraction, power spectrum densities with 1 Hz bin are calculated as features, and these features are analyzed using the Fisher score and the one way ANOVA method. In the classification step, a committee classifier is trained based on the extracted features to assess engagement status. Finally, experiment results showed that there exist significant differences in the extracted features among different subjects, and we have implemented a feature normalization procedure to mitigate the differences and significantly improved the engagement assessment performance.
A DISTINGUISH METHOD OF EPILEPTIC EEG AND DEGLUTITION EEG BASED ON CHAOTIC NOISE-REDUCTION* Guanghua Ouyang, Chunyan Li, Guotai Jiang College of Life...EEG and deglutition EEG’s nonnoise trajectory and distinguishing these two waveforms is presented. The main aim of this paper is to introduce the...different parameters of dipole, a method of distinguishing epileptic EEG and deglutition EEG using the measurement of nonlinear dynamics is obtained. Key
Teli, Mohammad Nayeem; Anderson, Charles
Patterns in electroencephalogram (EEG) signals are analyzed for a Brain Computer Interface (BCI). An important aspect of this analysis is the work on transformations of high dimensional EEG data to low dimensional spaces in which we can classify the data according to mental tasks being performed. In this research we investigate how a Neural Network (NN) in an auto-encoder with bottleneck configuration can find such a transformation. We implemented two approximate second-order methods to optimize the weights of these networks, because the more common first-order methods are very slow to converge for networks like these with more than three layers of computational units. The resulting non-linear projections of time embedded EEG signals show interesting separations that are related to tasks. The bottleneck networks do indeed discover nonlinear transformations to low-dimensional spaces that capture much of the information present in EEG signals. However, the resulting low-dimensional representations do not improve classification rates beyond what is possible using Quadratic Discriminant Analysis (QDA) on the original time-lagged EEG.
Toresano, La Ode Husein Z.; Wijaya, Sastra Kusuma; Prawito, Sudarmaji, Arief; Syakura, Abdan; Badri, Cholid
An electroencephalogram (EEG) is a device for measuring and recording the electrical activity of brain. The EEG data of signal can be used as a source of analysis for human brain function. The purpose of this study was to design a portable multichannel EEG based on embedded system and ADS1299. The ADS1299 is an analog front-end to be used as an Analog to Digital Converter (ADC) to convert analog signal of electrical activity of brain, a filter of electrical signal to reduce the noise on low-frequency band and a data communication to the microcontroller. The system has been tested to capture brain signal within a range of 1-20 Hz using the NETECH EEG simulator 330. The developed system was relatively high accuracy of more than 82.5%. The EEG Instrument has been successfully implemented to acquire the brain signal activity using a PC (Personal Computer) connection for displaying the recorded data. The final result of data acquisition has been processed using OpenBCI GUI (Graphical User Interface) based through real-time process for 8-channel signal acquisition, brain-mapping and power spectral decomposition signal using the standard FFT (Fast Fourier Transform) algorithm.
Full Text Available The present mini-review was aimed at exploring the frontal EEG asymmetry of mood. With respect to emotion, interpreted as a discrete affective process, mood is more controllable, more nebulous, and more related to mind/cognition; in addition, causes are less well-defined than those eliciting emotion. Therefore, firstly, the rational for the distinction between emotion and mood was provided. Then, the main frontal EEG asymmetry models were presented, such as the motivational approach/withdrawal, valence/arousal, capability, and inhibition asymmetric models. Afterward, the frontal EEG asymmetry of mood was investigated following three research lines, that is considering studies involving different mood induction procedures, dispositional mood (positive and negative affect, and mood alterations in both healthy and clinical populations. In general, results were found to be contradictory, no model is unequivocally supported regardless the research line considered. Different methodological issues were raised, such as: the composition of samples used across studies, in particular, gender and age were found to be critical variables that should be better addressed in future studies; the importance of third variables that might mediate the relationship between frontal EEG asymmetries and mood, for example bodily states and hormonal responses; the role of cognition, namely the interplay between mood and executive functions. In light of these issues, future research directions were proposed. Amongst others, the need to explore the neural connectivity that underpins EEG asymmetries, and the need to include both positive and negative mood conditions in the experimental designs have been highlighted.
Full Text Available Sustained attention is a process that enables the maintenance of response persistence and continuous effort over extended periods of time. Performing attention-related tasks in real life involves the need to ignore a variety of distractions and inhibit attention shifts to irrelevant activities. This study investigates electroencephalography (EEG spectral changes during a sustained attention task within a real classroom environment. Eighteen healthy students were instructed to recognize as fast as possible special visual targets that were displayed during regular university lectures. Sorting their EEG spectra with respect to response times, which indicated the level of visual alertness to randomly introduced visual stimuli, revealed significant changes in the brain oscillation patterns. The results of power-frequency analysis demonstrated a relationship between variations in the EEG spectral dynamics and impaired performance in the sustained attention task. Across subjects and sessions, prolongation of the response time was preceded by an increase in the delta and theta EEG powers over the occipital region, and decrease in the beta power over the occipital and temporal regions. Meanwhile, implementation of the complex attention task paradigm into a real-world classroom setting makes it possible to investigate specific mutual links between brain activities and factors that cause impaired behavioral performance, such as development and manifestation of classroom mental fatigue. The findings of the study set a basis for developing a system capable of estimating the level of visual attention during real classroom activities by monitoring changes in the EEG spectra.
Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard
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.
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.
Horvath, Andras; Szucs, Anna; Csukly, Gabor; Sakovics, Anna; Stefanics, Gabor; Kamondi, Anita
Here we critically review studies that used electroencephalography (EEG) or event-related potential (ERP) indices as a biomarker of Alzheimer's disease. In the first part we overview studies that relied on visual inspection of EEG traces and spectral characteristics of EEG. Second, we survey analysis methods motivated by dynamical systems theory (DST) as well as more recent network connectivity approaches. In the third part we review studies of sleep. Next, we compare the utility of early and late ERP components in dementia research. In the section on mismatch negativity (MMN) studies we summarize their results and limitations and outline the emerging field of computational neurology. In the following we overview the use of EEG in the differential diagnosis of the most common neurocognitive disorders. Finally, we provide a summary of the state of the field and conclude that several promising EEG/ERP indices of synaptic neurotransmission are worth considering as potential biomarkers. Furthermore, we highlight some practical issues and discuss future challenges as well.
Full Text Available Abstract Background The EEG (Electroencephalogram is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. However, the human observer can not directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. This work discusses the effect on the EEG signal due to music and reflexological stimulation. Methods In this work, nonlinear parameters like Correlation Dimension (CD, Largest Lyapunov Exponent (LLE, Hurst Exponent (H and Approximate Entropy (ApEn are evaluated from the EEG signals under different mental states. Results The results obtained show that EEG to become less complex relative to the normal state with a confidence level of more than 85% due to stimulation. Conclusions It is found that the measures are significantly lower when the subjects are under sound or reflexologic stimulation as compared to the normal state. The dimension increases with the degree of the cognitive activity. This suggests that when the subjects are under sound or reflexologic stimuli, the number of parallel functional processes active in the brain is less and the brain goes to a more relaxed state
Rakshasbhuvankar, Abhijeet; Paul, Saritha; Nagarajan, Lakshmi; Ghosh, Soumya; Rao, Shripada
Amplitude-integrated electroencephalogram (aEEG) is being used increasingly for monitoring seizures in neonatal units. Its accuracy, compared with "the gold-standard" conventional elecroencephalogram (cEEG) is still not well established. We aimed to conduct a systematic review to evaluate the diagnostic accuracy of aEEG when compared with cEEG, for detection of neonatal seizures. A systematic review was conducted using the Cochrane methodology. EMBASE, CINAHL and PubMed databases were searched in September 2014. Studies comparing simultaneous recordings of cEEG and aEEG for detection of seizures in neonatal population were included. QUADAS 2 tool was used to examine "risk of bias" and "applicability". Ten studies (patient sample 433) were included. Risk of bias was high in five studies, unclear in one and low in four. For the detection of individual seizures, when "aEEG with raw trace" was used, median sensitivity was 76% (range: 71-85), and specificity 85% (range: 39-96). When "aEEG without raw trace" was used, median sensitivity was 39% (range: 25-80) and specificity 95% (range: 50-100). Detailed meta-analysis could not be done because of significant clinical/methodological heterogeneity. Seizure detection was better when interpreted by experienced clinicians. Seizures with low amplitude/brief duration and those occurring away from aEEG leads were less likely to be detected. Studies included in the systematic review showed aEEG to have relatively low and variable sensitivity and specificity. Based on the available evidence, aEEG cannot be recommended as the mainstay for diagnosis and management of neonatal seizures. There is an urgent need of well-designed studies to address this issue definitively. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.
Full Text Available Epilepsy is an electrophysiological disorder of the brain, the hallmark of which is recurrent and unprovoked seizures. Electroencephalogram (EEG measures electrical activity of the brain that is commonly applied as a non-invasive technique for seizure detection. Although a vast number of publications have been published on intelligent algorithms to classify interictal and ictal EEG, it remains an open question whether they can be detected using short-length EEG recordings. In this study, we proposed three protocols to select 5 s EEG segment for classifying interictal and ictal EEG from normal. We used the publicly-accessible Bonn database, which consists of normal, interical, and ictal EEG signals with a length of 4,097 sampling points (23.6 s per record. In this study, we selected three segments of 868 points (5 s length from each recordings and evaluated results for each of them separately. The well-studied irregularity measure – sample entropy (SampEn – and a more recently proposed complexity measure – distribution entropy (DistEn – were used as classification features. A total of 20 combinations of input parameters m and τ for the calculation of SampEn and DistEn were selected for compatibility. Results showed that SampEn was undefined for half of the used combinations of input parameters and indicated a large intra-class variance. Moreover, DistEn performed robustly for short-length EEG data indicating relative independence from input parameters and small intra-class fluctuations. In addition, it showed acceptable performance for all three classification problems (interictal EEG from normal, ictal EEG from normal, and ictal EEG from interictal compared to SampEn, which showed better results only for distinguishing normal EEG from interictal and ictal. Both SampEn and DistEn showed good reproducibility and consistency, as evidenced by the independence of results on analysing protocol.
Thorsten Oliver Zander
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.
Omurtag, Ahmet; Baki, Samah G Abdel; Chari, Geetha; Cracco, Roger Q; Zehtabchi, Shahriar; Fenton, Andr? A; Grant, Arthur C.
Background We describe and characterize the performance of microEEG compared to that of a commercially available and widely used clinical EEG machine. microEEG is a portable, battery-operated, wireless EEG device, developed by Bio-Signal Group to overcome the obstacles to routine use of EEG in emergency departments (EDs). Methods The microEEG was used to obtain EEGs from healthy volunteers in the EEG laboratory and ED. The standard system was used to obtain EEGs from healthy volunteers in the...
Full Text Available This paper reports on a multiresolution analysis of EEG signals. The dominant frequency components of signals with and without observed epileptic discharges were compared. The study showed that there were significant differences in dominant frequency between the signals with epileptic discharges and the signals without discharges. This gives the ability to identify epilepsy during EEG examination. The frequency of the signals coming from the frontal, central, parietal and occipital channels are similar. Multiresolution analysis can be used to describe the activity of brain waves and to try to predict epileptic seizures, thereby contributing to precise medical diagnoses.
Gath, I; Lehmann, D; Bar-On, E
An automatic method for classification of EEG data, based upon segmentation of the signal using the autoregressive model and decision making in fuzzy environment, is described. The classification is applied to explore the relations between EEG states during waking, and vigilance performance studied through auditory choice reaction time. The average auditory choice reaction time measured during occurrences of "alpha" segments was significantly shorter than that measured during occurrences of "nonalpha" signal segments. A significant negative correlation was also found between the segments auditory choice reaction time and the segments spectral power in the alpha or beta frequency band.
Jetha, Michelle K; Schmidt, Louis A; Goldberg, Joel O
We conducted a pilot study to examine the relations among the patterns of resting regional electroencephalogram (EEG) alpha activity, trait shyness and sociability, and positive and negative symptoms scores in 20 adults with schizophrenia, attending a community-based treatment and rehabilitation center. As predicted, patients' positive symptoms were related to greater relative resting left frontal EEG activity, replicating earlier work. When only adults with low to no positive symptoms were considered, trait shyness was related to greater relative resting right frontal EEG activity, whereas trait sociability was related to greater relative resting left frontal EEG activity. This finding is similar to what is consistently noted in healthy adults. These pilot data suggest that positive symptoms in patients with schizophrenia may obscure the relations between personality and frontal EEG asymmetry measures observed in healthy adults.
St John, Ashley M; Kao, Katie; Chita-Tegmark, Meia; Liederman, Jacqueline; Grieve, Philip G; Tarullo, Amanda R
Despite the importance of social interactions for infant brain development, little research has assessed functional neural activation while infants socially interact. Electroencephalography (EEG) power is an advantageous technique to assess infant functional neural activation. However, many studies record infant EEG only during one baseline condition. This protocol describes a paradigm that is designed to comprehensively assess infant EEG activity in both social and nonsocial contexts as well as tease apart how different types of social inputs differentially relate to infant EEG. The within-subjects paradigm includes four controlled conditions. In the nonsocial condition, infants view objects on computer screens. The joint attention condition involves an experimenter directing the infant's attention to pictures. The joint attention condition includes three types of social input: language, face-to-face interaction, and the presence of joint attention. Differences in infant EEG between the nonsocial and joint attention conditions could be due to any of these three types of input. Therefore, two additional conditions (one with language input while the experimenter is hidden behind a screen and one with face-to-face interaction) were included to assess the driving contextual factors in patterns of infant neural activation. Representative results demonstrate that infant EEG power varied by condition, both overall and differentially by brain region, supporting the functional nature of infant EEG power. This technique is advantageous in that it includes conditions that are clearly social or nonsocial and allows for examination of how specific types of social input relate to EEG power. This paradigm can be used to assess how individual differences in age, affect, socioeconomic status, and parent-infant interaction quality relate to the development of the social brain. Based on the demonstrated functional nature of infant EEG power, future studies should consider the role
Online detection of amplitude modulation of motor-related EEG desynchronization using a lock-in amplifier: Comparison with a fast Fourier transform, a continuous wavelet transform, and an autoregressive algorithm.
Kato, Kenji; Takahashi, Kensho; Mizuguchi, Nobuaki; Ushiba, Junichi
Neurofeedback of event-related desynchronization (ERD) in electroencephalograms (EEG) of the sensorimotor cortex (SM1) using a brain-computer interface (BCI) paradigm is a powerful tool to promote motor recovery from post-stroke hemiplegia. However, the feedback delay attenuates the degree of motor learning and neural plasticity. The present study aimed to shorten the delay time to estimate amplitude modulation of the motor-imagery-related alpha and beta SM1-ERD using a lock-in amplifier (LIA) algorithm. The delay time was evaluated by calculating the value of the maximal correlation coefficient (MCC) between the time-series trace of ERDs extracted by the online LIA algorithm and those identified by an offline algorithm with the Hilbert transform (HT). The MCC and delay values used to estimate the ERDs calculated by the LIA were 0.89±0.032 and 200±9.49ms, respectively. The delay time and MCC values were significantly improved compared with those calculated by the conventional fast Fourier transformation (FFT), continuous Wavelet transformation (CWT), and autoregressive (AR) algorithms. Moreover, the coefficients of variance of the delay time and MCC values across trials were significantly lower in the LIA compared with the FFT, CWT, and AR algorithms. These results indicate that the LIA improved the detection delay, accuracy, and stability for estimating amplitude modulation of motor-related SM1-ERD. This would be beneficial for BCI paradigms to facilitate neurorehabilitation in patients with motor deficits. Copyright © 2017 Elsevier B.V. All rights reserved.
This thesis presents electroencephalography (EEG) brain imaging by covering topics as empirical evaluation of source confusion, probabilistic inverse methods, and source analysis performed on infant EEG data. In terms of source confusion we inspect how current sources within the brain may be conf...... the principled computation of sparse spatial and smooth temporal EEG source reconstructions consistent with neurophysiological assumptions in a variety of event-related imaging paradigms....... topics in EEG source reconstruction, namely, the forward progation model (describing the mapping from the current sources within the brain to the sensors at the scalp) and the temporal patterns present in the EEG. As forward models may suffer from a number of errors including the geometrical...... distributions over the unknown sources given the observed data. Here, we propose a hierarchical Bayesian model that attempts to minimize the influence of uncertainties associated with the forward model on the source estimates. Similarly, we develop a hierarchical spatio-temporal Bayesian model that accommodates...
Yu, K.; Prasad, I.; Mir, H.; Thakor, N.; Al-Nashash, H.
Objective. Our experiments explored the effect of visual stimuli degradation on cognitive workload. Approach. We investigated the subjective assessment, event-related potentials (ERPs) as well as electroencephalogram (EEG) as measures of cognitive workload. Main results. These experiments confirm that degradation of visual stimuli increases cognitive workload as assessed by subjective NASA task load index and confirmed by the observed P300 amplitude attenuation. Furthermore, the single-trial multi-level classification using features extracted from ERPs and EEG is found to be promising. Specifically, the adopted single-trial oscillatory EEG/ERP detection method achieved an average accuracy of 85% for discriminating 4 workload levels. Additionally, we found from the spatial patterns obtained from EEG signals that the frontal parts carry information that can be used for differentiating workload levels. Significance. Our results show that visual stimuli can modulate cognitive workload, and the modulation can be measured by the single trial EEG/ERP detection method.
Zachery Ryan Hernandez
Full Text Available Although efforts to characterize human movement through EEG have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA dancers. Each dancer performed whole body functional movements of three Action types: movements devoid of expressive qualities ('Neutral', non-expressive movements while thinking about specific expressive qualities ('Think’, and enacted expressive movements ('Do'. The expressive movement qualities that were used in the 'Think' and 'Do' actions consisted of a sequence of eight Laban Efforts as defined by LMA - a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2 – 4 Hz EEG as input to a machine learning algorithm that computed locality-preserving Fisher’s discriminant analysis (LFDA for dimensionality reduction followed by Gaussian mixture models (GMMs to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort (giving a total of 17 classes. Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements.
Cruz-Garza, Jesus G.; Hernandez, Zachery R.; Nepaul, Sargoon; Bradley, Karen K.; Contreras-Vidal, Jose L.
Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities (“Neutral”), non-expressive movements while thinking about specific expressive qualities (“Think”), and enacted expressive movements (“Do”). The expressive movement qualities that were used in the “Think” and “Do” actions consisted of a sequence of eight Laban Effort qualities as defined by LMA—a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2–4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of
Cruz-Garza, Jesus G; Hernandez, Zachery R; Nepaul, Sargoon; Bradley, Karen K; Contreras-Vidal, Jose L
Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities ("Neutral"), non-expressive movements while thinking about specific expressive qualities ("Think"), and enacted expressive movements ("Do"). The expressive movement qualities that were used in the "Think" and "Do" actions consisted of a sequence of eight Laban Effort qualities as defined by LMA-a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2-4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore
Pontifex, Matthew B; Gwizdala, Kathryn L; Parks, Andrew C; Billinger, Martin; Brunner, Clemens
Despite the growing use of independent component analysis (ICA) algorithms for isolating and removing eyeblink-related activity from EEG data, we have limited understanding of how variability associated with ICA uncertainty may be influencing the reconstructed EEG signal after removing the eyeblink artifact components. To characterize the magnitude of this ICA uncertainty and to understand the extent to which it may influence findings within ERP and EEG investigations, ICA decompositions of EEG data from 32 college-aged young adults were repeated 30 times for three popular ICA algorithms. Following each decomposition, eyeblink components were identified and removed. The remaining components were back-projected, and the resulting clean EEG data were further used to analyze ERPs. Findings revealed that ICA uncertainty results in variation in P3 amplitude as well as variation across all EEG sampling points, but differs across ICA algorithms as a function of the spatial location of the EEG channel. This investigation highlights the potential of ICA uncertainty to introduce additional sources of variance when the data are back-projected without artifact components. Careful selection of ICA algorithms and parameters can reduce the extent to which ICA uncertainty may introduce an additional source of variance within ERP/EEG studies. © 2016 Society for Psychophysiological Research.
Namazi, Hamidreza; Khosrowabadi, Reza; Hussaini, Jamal; Habibi, Shaghayegh; Farid, Ali Akhavan; Vladimir V. Kulish
One of the major challenges in brain research is to relate the structural features of the auditory stimulus to structural features of Electroencephalogram (EEG) signal. Memory content is an important feature of EEG signal and accordingly the brain. On the other hand, the memory content can also be considered in case of stimulus. Beside all works done on analysis of the effect of stimuli on human EEG and brain memory, no work discussed about the stimulus memory and also the relationship that m...
Michael eWong; Woody, Erik Z.; Schmidt, Louis A; Michael eVan Ameringen; Noam eSoreni; Henry eSzechtman
Previous studies have shown that the resting electroencephalogram (EEG) alpha patterns of non-clinical participants who score high on measures of negative affect, such as depression and shyness, are different from those who score low. However, we know relatively little about patterns of resting EEG alpha patterns in a non-clinical sample of individuals with high levels of obsessive-compulsive behaviors indicative of obsessive-compulsive disorder (OCD). Here we measured resting EEG alpha activ...
Stevenson, N J; O'Toole, J M; Korotchikova, I; Boylan, G B
Artefact detection is an important component of any automated EEG analysis. It is of particular importance in analyses such as sleep state detection and EEG grading where there is no null state. We propose a general artefact detection system (GADS) based on the analysis of the neonatal EEG. This system aims to detect both major and minor artefacts (a distinction based primarily on amplitude). As a result, a two-stage system was constructed based on 14 features extracted from EEG epochs at multiple time scales: [2, 4, 16, 32]s. These features were combined in a support vector machine (SVM) in order to determine the presence of absence of artefact. The performance of the GADS was estimated using a leave-one-out cross-validation applied to a database of hour long recordings from 51 neonates. The median AUC was 1.00 (IQR: 0.95-1.00) for the detection of major artefacts and 0.89 (IQR: 0.83-0.95) for the detection of minor artefacts.
J Gordon Millichap
Computerized power spectral analysis (PSA), permitting topographic representation and statistical analysis of EEG, of 25 right-handed males, 9-12 years of age with attention deficit hyperactivity disorder was used in studies from the Departments of Psychology, Pediatrics (Neurology) and Computing Center, University of Tennessee and East Tennessee Children’s Hospital, Knoxville, TN.
Montavont, A; Kaminska, A; Soufflet, C; Taussig, D
Long-term video-EEG corresponds to a recording ranging from 1 to 24 h or even longer. It is indicated in the following situations: diagnosis of epileptic syndromes or unclassified epilepsy, pre-surgical evaluation for drug-resistant epilepsy, follow-up of epilepsy or in cases of paroxysmal symptoms whose etiology remains uncertain. There are some specificities related to paediatric care: a dedicated pediatric unit; continuous monitoring covering at least a full 24-hour period, especially in the context of pre-surgical evaluation; the requirement of presence by the parents, technician or nurse; and stronger attachment of electrodes (cup electrodes), the number of which is adapted to the age of the child. The chosen duration of the monitoring also depends on the frequency of seizures or paroxysmal events. The polygraphy must be adapted to the type and topography of movements. It is essential to have at least an electrocardiography (ECG) channel, respiratory sensor and electromyography (EMG) on both deltoids. There is no age limit for performing long-term video-EEG even in newborns and infants; nevertheless because of scalp fragility, strict surveillance of the baby's skin condition is required. In the specific context of pre-surgical evaluation, long-term video-EEG must record all types of seizures observed in the child. This monitoring is essential in order to develop hypotheses regarding the seizure onset zone, based on electroclinical correlations, which should be adapted to the child's age and the psychomotor development. Copyright © 2015. Published by Elsevier SAS.
Fichtner, Karl-Heinz; Fichtner, Lars; Inoue, Kei; Ohya, Masanori
Based on classical models of brain activities it seems to be difficult to explain the internal noise related to EEG-measurements. In this paper using a quantum model of the recognition process we consider the asymptotic behaviour of that internal noise.
Background: Autism is currently viewed as a genetically determined neurodevelopmental disorder although its defi nite underlying etiology remains to be established. Aim of the Study: Our purpose was to assess autism related morphological neuroimaging changes of the brain and EEG abnormalities in correlation to the ...
Lier, Hester van
Brain activity and behaviour are related to each other. Psychoactive drugs can influence both brain activity and behaviour. In order to be able to understand the interplay between brain activity as measured by the electroencephalogram (EEG), behaviour, and psychoactive drugs, it is not sufficient to
Lehtonen, J.; Jylänki, P.P.; Kauhanen, L.; Sams, M.
Many offline studies have explored the feasibility of EEG potentials related to single limb movements for a brain-computer interface (BCI) control signal. However, only few functional online single-trial BCI systems have been reported. We investigated whether inexperienced subjects could control a
Kline, John P; Blackhart, Ginette C; Williams, William C
The opponent process theory of emotion [Solomon, R.L., and Corbit, J.D. (1974). An opponent-process theory of motivation: I. Temporal dynamics of affect. Psychological Review, 81, 119-143.] predicts a temporary reversal of emotional valence during the recovery from emotional stimulation. We hypothesized that this affective contrast would be apparent in asymmetrical activity patterns in the frontal lobes, and would be more apparent for left frontally active individuals. The present study tested this prediction by examining EEG asymmetries during and after blocked presentations of aversive pictures selected from the International Affective Picture System (IAPS). 12 neutral images, 12 aversive images, and 24 neutral images were presented in blocks. Participants who were right frontally active at baseline did not show changes in EEG asymmetry while viewing aversive slides or after cessation. Participants left frontally active at baseline, however, exhibited greater relative left frontal activity after aversive stimulation than before stimulation. Asymmetrical activity patterns in the frontal lobes may relate to affect regulatory processes, including contrasting opponent after-reactions to aversive stimuli.
Kemp, Bob; van Beelen, Teunis; Stijl, Marion; van Someren, Paul; Roessen, Marco; van Dijk, J Gert
Traditional electroencephalogram (EEG) recorders reject low frequencies and DC and therefore cannot handle fullband EEG. Dedicated fullband recorders use non-standard file formats, because the standard format (EDF) cannot handle large DC electrode offset voltages. Both facts limit the development and use of fullband EEG. We developed a modification that allows conventional equipment to record fullband EEG, and adapts both types of recorders to EDF. The modification is a simple filter that attenuates the DC component and thus makes the EEG fit within traditional equipment limitations and EDF. The review software automatically 'de-attenuates' the DC component, without loss of information. DC attenuation by a factor of 10 made both types of recorders store DC attenuated fullband EEG into EDF files. Recordings were made during 0.5-24h in 46 subjects. The DC de-attenuator automatically reconstructed the original fullband EEG within an amplitude range of ±100mV and with a resolution of 0.3μV. Using sintered Ag-AgCl electrodes attached with common procedures, reconstructed DC EEG in spontaneously moving subjects ranged between ±32mV. The modification works. Fullband recordings can now be analyzed by independent software, archived and exchanged. Any EEG system can be made to record fullband EEG into standard EDF. Copyright © 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
Bao, Forrest Sheng; Liu, Xin; Zhang, Christina
Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.
Melnik, Andrew; Legkov, Petr; Izdebski, Krzysztof; Kärcher, Silke M.; Hairston, W. David; Ferris, Daniel P.; König, Peter
Lab-based electroencephalography (EEG) techniques have matured over decades of research and can produce high-quality scientific data. It is often assumed that the specific choice of EEG system has limited impact on the data and does not add variance to the results. However, many low cost and mobile EEG systems are now available, and there is some doubt as to the how EEG data vary across these newer systems. We sought to determine how variance across systems compares to variance across subjects or repeated sessions. We tested four EEG systems: two standard research-grade systems, one system designed for mobile use with dry electrodes, and an affordable mobile system with a lower channel count. We recorded four subjects three times with each of the four EEG systems. This setup allowed us to assess the influence of all three factors on the variance of data. Subjects performed a battery of six short standard EEG paradigms based on event-related potentials (ERPs) and steady-state visually evoked potential (SSVEP). Results demonstrated that subjects account for 32% of the variance, systems for 9% of the variance, and repeated sessions for each subject-system combination for 1% of the variance. In most lab-based EEG research, the number of subjects per study typically ranges from 10 to 20, and error of uncertainty in estimates of the mean (like ERP) will improve by the square root of the number of subjects. As a result, the variance due to EEG system (9%) is of the same order of magnitude as variance due to subjects (32%/sqrt(16) = 8%) with a pool of 16 subjects. The two standard research-grade EEG systems had no significantly different means from each other across all paradigms. However, the two other EEG systems demonstrated different mean values from one or both of the two standard research-grade EEG systems in at least half of the paradigms. In addition to providing specific estimates of the variability across EEG systems, subjects, and repeated sessions, we also
Authier, Simon; Accardi, Michael V; Paquette, Dominique; Pouliot, Mylène; Arezzo, Joseph; Stubbs, R John; Gerson, Ronald J; Friedhoff, Lawrence T; Weis, Holger
Continuous video-electroencephalographic (EEG) monitoring remains the gold standard for seizure liability assessments in preclinical drug safety assessments. EEG monitored by telemetry was used to assess the behavioral and EEG effects of noribogaine hydrochloride (noribogaine) in cynomolgus monkeys. Noribogaine is an iboga alkaloid being studied for the treatment of opioid dependence. Six cynomolgus monkeys (3 per gender) were instrumented with EEG telemetry transmitters. Noribogaine was administered to each monkey at both doses (i.e., 160 and 320mg/kg, PO) with an interval between dosing of at least 6days, and the resulting behavioral and EEG effects were evaluated. IV pentylenetetrazol (PTZ), served as a positive control for induced seizures. The administration of noribogaine at either of the doses evaluated was not associated with EEG evidence of seizure or with EEG signals known to be premonitory signs of increased seizure risk (e.g., sharp waves, unusual synchrony, shifts to high-frequency patterns). Noribogaine was associated with a mild reduction in activity levels, increased scratching, licking and chewing, and some degree of poor coordination and related clinical signs. A single monkey exhibited brief myoclonic movements that increased in frequency at the high dose, but which did not appear to generalize, cluster or to be linked with EEG abnormalities. Noribogaine was also associated with emesis and partial anorexia. In contrast, PTZ was associated with substantial pre-ictal EEG patterns including large amplitude, repetitive sharp waves leading to generalized seizures and to typical post-ictal EEG frequency attenuation. EEG patterns were within normal limits following administration of noribogaine at doses up to 320mg/kg with concurrent clinical signs that correlated with plasma exposures and resolved by the end of the monitoring period. PTZ was invariably associated with EEG paroxysmal activity leading to ictal EEG. In the current study, a noribogaine
Melnik, Andrew; Legkov, Petr; Izdebski, Krzysztof; Kärcher, Silke M; Hairston, W David; Ferris, Daniel P; König, Peter
Lab-based electroencephalography (EEG) techniques have matured over decades of research and can produce high-quality scientific data. It is often assumed that the specific choice of EEG system has limited impact on the data and does not add variance to the results. However, many low cost and mobile EEG systems are now available, and there is some doubt as to the how EEG data vary across these newer systems. We sought to determine how variance across systems compares to variance across subjects or repeated sessions. We tested four EEG systems: two standard research-grade systems, one system designed for mobile use with dry electrodes, and an affordable mobile system with a lower channel count. We recorded four subjects three times with each of the four EEG systems. This setup allowed us to assess the influence of all three factors on the variance of data. Subjects performed a battery of six short standard EEG paradigms based on event-related potentials (ERPs) and steady-state visually evoked potential (SSVEP). Results demonstrated that subjects account for 32% of the variance, systems for 9% of the variance, and repeated sessions for each subject-system combination for 1% of the variance. In most lab-based EEG research, the number of subjects per study typically ranges from 10 to 20, and error of uncertainty in estimates of the mean (like ERP) will improve by the square root of the number of subjects. As a result, the variance due to EEG system (9%) is of the same order of magnitude as variance due to subjects (32%/sqrt(16) = 8%) with a pool of 16 subjects. The two standard research-grade EEG systems had no significantly different means from each other across all paradigms. However, the two other EEG systems demonstrated different mean values from one or both of the two standard research-grade EEG systems in at least half of the paradigms. In addition to providing specific estimates of the variability across EEG systems, subjects, and repeated sessions, we also
Schröger, Erich; Grimm, Sabine
The recognition of sound patterns in speech or music (e.g., a melody that is played in different keys) requires knowledge about pitch relations between successive sounds. We investigated the formation of regularity representations for sound patterns in an event-related potential (ERP) study. A pattern, which consisted of six concatenated 50 ms tone segments differing in fundamental frequency, was presented 1, 2, 3, 6, or 12 times and then replaced by another pattern by randomly changing the pitch of the tonal segments (roving standard paradigm). In an absolute repetition condition, patterns were repeated identically, whereas in a transposed condition, only the pitch relations of the tonal segments of the patterns were repeated, while the entire patterns were shifted up or down in pitch. During ERP measurement participants were not informed about the pattern repetition rule, but were instructed to discriminate rarely occurring targets of lower or higher sound intensity. EPRs for pattern changes (mismatch negativity, MMN; and P3a) and for pattern repetitions (repetition positivity, RP) revealed that the auditory system is able to rapidly extract regularities from unfamiliar complex sound patterns even when absolute pitch varies. Yet, enhanced RP and P3a amplitudes, and improved behavioral performance measured in a post-hoc test, in the absolute as compared with the transposed condition suggest that it is more difficult to encode patterns without absolute pitch information. This is explained by dissociable processing of standards and deviants as well as a back propagation mechanism to early sensory processing stages, which is effective after less repetitions of a standard stimulus for absolute pitch. PMID:28472146
Bader, Maria; Schröger, Erich; Grimm, Sabine
The recognition of sound patterns in speech or music (e.g., a melody that is played in different keys) requires knowledge about pitch relations between successive sounds. We investigated the formation of regularity representations for sound patterns in an event-related potential (ERP) study. A pattern, which consisted of six concatenated 50 ms tone segments differing in fundamental frequency, was presented 1, 2, 3, 6, or 12 times and then replaced by another pattern by randomly changing the pitch of the tonal segments (roving standard paradigm). In an absolute repetition condition, patterns were repeated identically, whereas in a transposed condition, only the pitch relations of the tonal segments of the patterns were repeated, while the entire patterns were shifted up or down in pitch. During ERP measurement participants were not informed about the pattern repetition rule, but were instructed to discriminate rarely occurring targets of lower or higher sound intensity. EPRs for pattern changes (mismatch negativity, MMN; and P3a) and for pattern repetitions (repetition positivity, RP) revealed that the auditory system is able to rapidly extract regularities from unfamiliar complex sound patterns even when absolute pitch varies. Yet, enhanced RP and P3a amplitudes, and improved behavioral performance measured in a post-hoc test, in the absolute as compared with the transposed condition suggest that it is more difficult to encode patterns without absolute pitch information. This is explained by dissociable processing of standards and deviants as well as a back propagation mechanism to early sensory processing stages, which is effective after less repetitions of a standard stimulus for absolute pitch.
Full Text Available The recognition of sound patterns in speech or music (e.g., a melody that is played in different keys requires knowledge about pitch relations between successive sounds. We investigated the formation of regularity representations for sound patterns in an event-related potential (ERP study. A pattern, which consisted of six concatenated 50 ms tone segments differing in fundamental frequency, was presented 1, 2, 3, 6, or 12 times and then replaced by another pattern by randomly changing the pitch of the tonal segments (roving standard paradigm. In an absolute repetition condition, patterns were repeated identically, whereas in a transposed condition, only the pitch relations of the tonal segments of the patterns were repeated, while the entire patterns were shifted up or down in pitch. During ERP measurement participants were not informed about the pattern repetition rule, but were instructed to discriminate rarely occurring targets of lower or higher sound intensity. EPRs for pattern changes (mismatch negativity, MMN; and P3a and for pattern repetitions (repetition positivity, RP revealed that the auditory system is able to rapidly extract regularities from unfamiliar complex sound patterns even when absolute pitch varies. Yet, enhanced RP and P3a amplitudes, and improved behavioral performance measured in a post-hoc test, in the absolute as compared with the transposed condition suggest that it is more difficult to encode patterns without absolute pitch information. This is explained by dissociable processing of standards and deviants as well as a back propagation mechanism to early sensory processing stages, which is effective after less repetitions of a standard stimulus for absolute pitch.
Jesus G. Cruz-Garza
Full Text Available Electroencephalography (EEG has emerged as a powerful tool for quantitatively studying the brain that enables natural and mobile experiments. Recent advances in EEG have allowed for the use of dry electrodes that do not require a conductive medium between the recording electrode and the scalp. The overall goal of this research was to gain an understanding of the overall usability and signal quality of dry EEG headsets compared to traditional gel-based systems in an unconstrained environment. EEG was used to collect Mobile Brain-body Imaging (MoBI data from 432 people as they experienced an art exhibit in a public museum. The subjects were instrumented with either one of four dry electrode EEG systems or a conventional gel electrode EEG system. Each of the systems was evaluated based on the signal quality and usability in a real-world setting. First, we describe the various artifacts that were characteristic of each of the systems. Second, we report on each system's usability and their limitations in a mobile setting. Third, to evaluate signal quality for task discrimination and characterization, we employed a data driven clustering approach on the data from 134 of the 432 subjects (those with reliable location tracking information and usable EEG data to evaluate the power spectral density (PSD content of the EEG recordings. The experiment consisted of a baseline condition in which the subjects sat quietly facing a white wall for 1 min. Subsequently, the participants were encouraged to explore the exhibit for as long as they wished (piece-viewing. No constraints were placed upon the individual in relation to action, time, or navigation of the exhibit. In this freely-behaving approach, the EEG systems varied in their capacity to record characteristic modulations in the EEG data, with the gel-based system more clearly capturing stereotypical alpha and beta-band modulations.
Full Text Available Single photon emission computed tomography (SPECT and Electroencephalography (EEG have become established tools in routine diagnostics of dementia. We aimed to increase the diagnostic power by combining quantitative markers from SPECT and EEG for differential diagnosis of disorders with amnestic symptoms. We hypothesize that the combination of SPECT with measures of interaction (connectivity in the EEG yields higher diagnostic accuracy than the single modalities. We examined 39 patients with Alzheimer's dementia (AD, 69 patients with depressive cognitive impairment (DCI, 71 patients with amnestic mild cognitive impairment (aMCI, and 41 patients with amnestic subjective cognitive complaints (aSCC. We calculated 14 measures of interaction from a standard clinical EEG-recording and derived graph-theoretic network measures. From regional brain perfusion measured by 99mTc-hexamethyl-propylene-aminoxime (HMPAO-SPECT in 46 regions, we calculated relative cerebral perfusion in these patients. Patient groups were classified pairwise with a linear support vector machine. Classification was conducted separately for each biomarker, and then again for each EEG- biomarker combined with SPECT. Combination of SPECT with EEG-biomarkers outperformed single use of SPECT or EEG when classifying aSCC vs. AD (90%, aMCI vs. AD (70%, and AD vs. DCI (100%, while a selection of EEG measures performed best when classifying aSCC vs. aMCI (82% and aMCI vs. DCI (90%. Only the contrast between aSCC and DCI did not result in above-chance classification accuracy (60%. In general, accuracies were higher when measures of interaction (i.e., connectivity measures were applied directly than when graph-theoretical measures were derived. We suggest that quantitative analysis of EEG and machine-learning techniques can support differentiating AD, aMCI, aSCC, and DCC, especially when being combined with imaging methods such as SPECT. Quantitative analysis of EEG connectivity could become
Full Text Available A quantitative and objective assessment of background electroencephalograph (EEG in sick neonates remains an everyday clinical challenge. We studied whether long range temporal correlations quantified by detrended fluctuation analysis (DFA could be used in the neonatal EEG to distinguish different grades of abnormality in the background EEG activity.Long-term EEG records of 34 neonates were collected after perinatal asphyxia, and their background was scored in 1h epochs (8h in each neonate as mild, moderate or severe. We applied DFA on 15 min long, non-overlapping EEG epochs (n=1088 filtered from 3-8Hz. Our formal feasibility study suggested that DFA exponent can be reliably assessed in only part of the EEG epochs, and in only relatively short time scales (10-60sec, while it becomes ambiguous if wider time scale are considered. This prompted further exploration whether paradigm used for quantifying multifractal DFA (MF-DFA could be applied in a more efficient way, and whether metrics from MF-DFA paradigm could yield useful benchmark with existing clinical EEG gradings.Comparison of MF-DFA metrics showed a significant difference between three visually assessed background EEG grades. MF-DFA parameters were also significantly correlated to interburst intervals quantified with our previously developed automated detector. Finally, we piloted the intra-patient application of MF-DFA metrics and showed their evolution during patient recovery from asphyxia. Our exploratory study showed that neonatal EEG can be quantified using multifractal metrics, which might offer a suitable parameter to quantify the grade of EEG background, or to monitor changes in brain state that take place during long-term brain monitoring.
Matic, Vladimir; Cherian, Perumpillichira Joseph; Koolen, Ninah; Ansari, Amir H; Naulaers, Gunnar; Govaert, Paul; Van Huffel, Sabine; De Vos, Maarten; Vanhatalo, Sampsa
A quantitative and objective assessment of background electroencephalograph (EEG) in sick neonates remains an everyday clinical challenge. We studied whether long range temporal correlations quantified by detrended fluctuation analysis (DFA) could be used in the neonatal EEG to distinguish different grades of abnormality in the background EEG activity. Long-term EEG records of 34 neonates were collected after perinatal asphyxia, and their background was scored in 1 h epochs (8 h in each neonate) as mild, moderate or severe. We applied DFA on 15 min long, non-overlapping EEG epochs (n = 1088) filtered from 3 to 8 Hz. Our formal feasibility study suggested that DFA exponent can be reliably assessed in only part of the EEG epochs, and in only relatively short time scales (10-60 s), while it becomes ambiguous if longer time scales are considered. This prompted further exploration whether paradigm used for quantifying multifractal DFA (MF-DFA) could be applied in a more efficient way, and whether metrics from MF-DFA paradigm could yield useful benchmark with existing clinical EEG gradings. Comparison of MF-DFA metrics showed a significant difference between three visually assessed background EEG grades. MF-DFA parameters were also significantly correlated to interburst intervals quantified with our previously developed automated detector. Finally, we piloted a monitoring application of MF-DFA metrics and showed their evolution during patient recovery from asphyxia. Our exploratory study showed that neonatal EEG can be quantified using multifractal metrics, which might offer a suitable parameter to quantify the grade of EEG background, or to monitor changes in brain state that take place during long-term brain monitoring.
The aim of this study was to retrospectively audit all electroencephalograms (EEGs) done over a 2-month period in 2009 by the Neurophysiology Department at Cork University Hospital. There were 316 EEGs performed in total, of which 176\\/316 (56%) were done within 24 hours of request. Out of 316 EEGs, 208 (66%) were considered \\'appropriate\\' by SIGN and NICE guidelines; 79\\/208 (38%) had abnormal EEGs and 28 of these abnormal EEGs had epileptiform features. There were 108\\/316 (34%) \\'inappropriate\\' requests for EEG; of these 15\\/108 (14%) were abnormal. Of the 67\\/316 (21%) patients who had EEGs requested based on a history of syncope\\/funny turns: none of these patients had epileptiform abnormalities on their EEGs. Our audit demonstrates that EEGs are inappropriately over-requested in our institution in particular for cases with reported \\'funny turns\\' and syncope. The yield from EEGs in this cohort of patients was low as would be expected.
Cohen, Emily; Wong, Flora Y; Wallace, Euan M; Mockler, Joanne C; Odoi, Alexsandria; Hollis, Samantha; Horne, Rosemary S C; Yiallourou, Stephanie R
Power spectral analysis of the electroencephalogram (EEG) is a non-invasive method to examine infant brain maturation. Preterm fetal growth restricted (p-FGR) neonates display an altered EEG power spectrum compared to appropriate-for-gestational-age (AGA) peers, suggesting delayed brain maturation. Longitudinal studies investigating EEG power spectrum maturation in p-FGR infants are lacking, however. We thus aimed to investigate brain maturation using sleep EEG power spectral analysis in p-FGR infants compared to preterm and term AGA controls (p-AGA and t-AGA, respectively). EEG was recorded during spontaneous sleep in 13 p-FGR, 17 p-AGA and 19 t-AGA infants at 1 and 6 months post-term age. Infant sleep states (active and quiet sleep) were scored using standard criteria. Power spectral analysis of a single-channel EEG (C3-M2/C4-M1) was performed using Fast Fourier Transform. The EEG power spectrum was divided into delta (0.5-4Hz), theta (4-8Hz), alpha (8-12Hz), sigma (12-14Hz) and beta (14-30Hz) frequency bands. Relative (%) powers and the spectral edge frequency were calculated. The spectral edge frequency was significantly higher in p-FGR infants compared to p-AGA controls in quiet sleep at 1 month post-term age (p<0.01). This was due to significantly reduced%-delta and significantly increased%-theta,%-alpha and%-beta power (p<0.01 for all) compared to p-AGA infants. p-FGR infants also showed significantly increased%-beta power compared to t-AGA infants (p<0.05). No group differences were observed in active sleep or at 6 months post-term age. In conclusion, p-FGR infants show altered sleep EEG power spectrum maturation compared to AGA peers. However, changes resolved by 6 months post-term age. Copyright © 2017. Published by Elsevier B.V.
Lopez-Gordo, M A; Padilla, P; Pelayo Valle, F
Acquisition of event-related potentials (ERPs) requires a nearly perfect synchronization between the stimulus player and the EEG acquisition unit that clinical systems implement at hardware level by means of a wired link...
Kim, Min-Ki; Kim, Miyoung; Oh, Eunmi; Kim, Sung-Phil
A growing number of affective computing researches recently developed a computer system that can recognize an emotional state of the human user to establish affective human-computer interactions. Various measures have been used to estimate emotional states, including self-report, startle response, behavioral response, autonomic measurement, and neurophysiologic measurement. Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity. Yet, EEG-based emotional state estimation requires well-designed computational methods to extract information from complex and noisy multichannel EEG data. In this paper, we review the computational methods that have been developed to deduct EEG indices of emotion, to extract emotion-related features, or to classify EEG signals into one of many emotional states. We also propose using sequential Bayesian inference to estimate the continuous emotional state in real time. We present current challenges for building an EEG-based emotion recognition system and suggest some future directions.
Full Text Available A growing number of affective computing researches recently developed a computer system that can recognize an emotional state of the human user to establish affective human-computer interactions. Various measures have been used to estimate emotional states, including self-report, startle response, behavioral response, autonomic measurement, and neurophysiologic measurement. Among them, inferring emotional states from electroencephalography (EEG has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity. Yet, EEG-based emotional state estimation requires well-designed computational methods to extract information from complex and noisy multichannel EEG data. In this paper, we review the computational methods that have been developed to deduct EEG indices of emotion, to extract emotion-related features, or to classify EEG signals into one of many emotional states. We also propose using sequential Bayesian inference to estimate the continuous emotional state in real time. We present current challenges for building an EEG-based emotion recognition system and suggest some future directions.
Özerdem, Mehmet Siraç; Polat, Hasan
Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain-computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems. In this study, EEG signals related to positive and negative emotions have been classified with preprocessing of channel selection. Self-Assessment Manikins was used to determine emotional states. We have employed discrete wavelet transform and machine learning techniques such as multilayer perceptron neural network (MLPNN) and k-nearest neighborhood (kNN) algorithm to classify EEG signals. The classifier algorithms were initially used for channel selection. EEG channels for each participant were evaluated separately, and five EEG channels that offered the best classification performance were determined. Thus, final feature vectors were obtained by combining the features of EEG segments belonging to these channels. The final feature vectors with related positive and negative emotions were classified separately using MLPNN and kNN algorithms. The classification performance obtained with both the algorithms are computed and compared. The average overall accuracies were obtained as 77.14 and 72.92% by using MLPNN and kNN, respectively.
Hassan B. Hawsawi
Full Text Available Understanding the brain and its activity is one of the great challenges of modern science. Normal brain activity (cognitive processes, etc. has been extensively studied using electroencephalography (EEG since the 1930's, in the form of spontaneous fluctuations in rhythms, and patterns, and in a more experimentally-driven approach in the form of event-related potentials (ERPs allowing us to relate scalp voltage waveforms to brain states and behavior. The use of EEG recorded during functional magnetic resonance imaging (EEG-fMRI is a more recent development that has become an important tool in clinical neuroscience, for example for the study of epileptic activity. The purpose of this review is to explore the magnetic resonance imaging safety aspects specifically associated with the use of scalp EEG and other brain-implanted electrodes such as intracranial EEG electrodes when they are subjected to the MRI environment. We provide a theoretical overview of the mechanisms at play specifically associated with the presence of EEG equipment connected to the subject in the MR environment, and of the resulting health hazards. This is followed by a survey of the literature on the safety of scalp or invasive EEG-fMRI data acquisitions across field strengths, with emphasis on the practical implications for the safe application of the techniques; in particular, we attempt to summarize the findings in terms of acquisition protocols when possible.
Flasbeck, Vera; Popkirov, Stoyan; Brüne, Martin
Background Frontal EEG asymmetry is a widely studied correlate of emotion processing and psychopathology. Recent research suggests that frontal EEG asymmetry during resting state is related to approach/withdrawal motivation and is also found in affective disorders such as major depressive disorder. Patients with borderline personality disorder (BPD) show aberrant behavior in relation to both approach and withdrawal motivation, which may arguably be associated with their difficulties in emotio...
Kondziella, Daniel; Friberg, Christian Kærsmose; Wellwood, Ian
BACKGROUND: Continuous EEG (cEEG) may allow monitoring of patients with aneurysmal subarachnoid hemorrhage (SAH) for delayed cerebral ischemia (DCI) and seizures, including non-convulsive seizures (NCSz), and non-convulsive status epilepticus (NCSE). We aimed to evaluate: (a) the diagnostic...... accuracy of cEEG as a confirmatory test, (b) the prognostic value of EEG patterns suggestive of seizures and DCI, and (c) the effectiveness of intensified neuromonitoring using cEEG in terms of improved clinical outcome following SAH. METHODS: A systematic review was performed with eligible studies...... selected from multiple indexing databases through June 2014. The methodological quality of these studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. RESULTS: Eighteen studies were identified, including cEEG data from 481 patients with aneurysmal SAH. NCSz were diagnosed in 7...
Lenartowicz, Agatha; Loo, Sandra K.
Electroencephalography (EEG) has, historically, played a focal role in the assessment of neural function in children with attention deficit hyperactivity disorder (ADHD). We review here the most recent developments in the utility of EEG in the diagnosis of ADHD, with emphasis on the most commonly used and emerging EEG metrics and their reliability in diagnostic classification. Considering the clinical heterogeneity of ADHD and the complexity of information available from the EEG signals, we suggest that considerable benefits are to be gained from multivariate analyses and a focus towards understanding of the neural generators of EEG. We conclude that while EEG cannot currently be used as a diagnostic tool, vast developments in analytical and technological tools in its domain anticipate future progress in its utility in the clinical setting. PMID:25234074
Lenartowicz, Agatha; Loo, Sandra K
Electroencephalography (EEG) has, historically, played a focal role in the assessment of neural function in children with attention deficit hyperactivity disorder (ADHD). We review here the most recent developments in the utility of EEG in the diagnosis of ADHD, with emphasis on the most commonly used and emerging EEG metrics and their reliability in diagnostic classification. Considering the clinical heterogeneity of ADHD and the complexity of information available from the EEG signals, we suggest that considerable benefits are to be gained from multivariate analyses and a focus towards understanding of the neural generators of EEG. We conclude that while EEG cannot currently be used as a diagnostic tool, vast developments in analytical and technological tools in its domain anticipate future progress in its utility in the clinical setting.
Grant, Arthur C; Rho, Jong M
Band heterotopia (BH) or "double cortex" syndrome is a neuronal migration disorder resulting in a diffuse band of subcortical grey matter and variable abnormality of the overlying cortex. Patients with BH have a spectrum of psychomotor delay and seizures. Associated epileptic syndromes and interictal EEG findings have been described, but ictal EEG patterns are lacking. We describe the clinical, interictal, and ictal EEG findings in two girls with BH and intractable seizures. Ictal EEG patterns correlated well with clinical seizure types, and did not have features unique to BH. Similarly, seizure behaviors and interictal EEG findings were typical of those seen in symptomatic generalized epilepsies. Despite evidence implicating the ectopic grey matter in seizure discharges, we conclude that seizure semiology and associated ictal EEG patterns in BH are no different from those seen in other causes of symptomatic generalized epilepsies.
Yuvaraj, R; Murugappan, M; Omar, Mohd Iqbal; Ibrahim, Norlinah Mohamed; Sundaraj, Kenneth; Mohamad, Khairiyah; Satiyan, M
Although an emotional deficit is a common finding in Parkinson's disease (PD), its neurobiological mechanism on emotion recognition is still unknown. This study examined the emotion processing deficits in PD patients using electroencephalogram (EEG) signals in response to multimodal stimuli. EEG signals were investigated on both positive and negative emotions in 14 PD patients and 14 aged-matched normal controls (NCs). The relative power (i.e., ratio of EEG signal power in each frequency band compared to the total EEG power) was computed over three brain regions: the anterior (AF3, F7, F3, F4, F8 and AF4), central (FC5 and FC6) and posterior (T7, P7, O1, O2, P8 and T8) regions for theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz) and gamma (30-60 Hz) frequency sub-bands, respectively. Behaviorally, PD patients showed decreased performance in classifying emotional stimuli as measured by subjective ratings. EEG power at theta, alpha, beta, and gamma bands in all regions were significantly different between the NC and PD groups during both the emotional tasks, with p-values less than 0.05. Furthermore, an increase of relative spectral powers in the theta and gamma bands and a decrease of relative powers in the alpha and beta bands were observed for PD patients compared with NCs during emotional information processing. The results suggest the possibility of the existence of a distinctive neurobiological substrate of PD patients during emotional information processing. Also, these distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients.
Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R
Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels. The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (EEG frequency ranges. Effectivity is determined by visual inspection, as well as root-mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average mean-squared-distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best. In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI. Highlights We present a new and reusable reference layer cap prototype for simultaneous EEG-fMRI We introduce new algorithms for reducing EEG artifacts due to simultaneous fMRI The algorithms combine a reference layer and adaptive filtering Several
Pedersen, S B; Petersen, K A
We aimed to characterize the clinical profile and EEG features of 43 patients with juvenile myoclonic epilepsy. In a retrospective design we studied the records of, and re-interviewed, 43 patients diagnosed with JME from the epilepsy clinic data base. Furthermore, available EEGs were re...... were sleep deprivation (84%), stress (70%), and alcohol consumption (51%). EEG findings included rapid spike-wave and polyspike-wave....
Ogawa, Yutaro; Takeno, Shohei; Kotani, Kiyoshi; Jimbo, Yasuhiko
Event Related Potential (ERP) of brain EEG (Electroencephalogram) activity plays an important role in EEG phase synchronization and/or hemodynamic responses measured by fMRI (functional Magnetic Resonance Imaging). However, the specific mechanism of ERP generation is still unclear. In this study, pulse and noise type visual stimuli are administered to subjects. Then the phase response of the EEG in α-waves is analyzed. As a result, the magnitudes of the phase response are varied by the stimulus administered phase and no power increase is observed. These results indicate that the ERP in α-waves is generated by the phase resetting of brain activity.
Full Text Available It is difficult to diagnose non-convulsive status epilepticus (NCSE clinically because of the complicated etiology and various clinical and electroencephalographic features of NCSE without a universally accepted definition. Although the diagnosis of NCSE relies largely on electroencephalogram (EEG findings, the determination of NCSE on EEG is inevitably subjective, and the EEG changes of most patients is lack of specificity. As the diagnosis of NCSE is related to clinical and electroencephalographic manifestations, diagnostic criteria for NCSE should take into account both clinical and electroencephalographic features, and their response to antiepileptic drugs (AEDs. DOI: 10.3969/j.issn.1672-6731.2015.11.005
Kirov, Roumen; Weiss, Carsten; Siebner, Hartwig R
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...... in a marked and widespread increase in EEG theta (4-8 Hz) activity. During wake, tSOS did not enhance consolidation of memories when applied after learning, but improved encoding of hippocampus-dependent memories when applied during learning. We conclude that the EEG frequency and related memory processes...
Li, Junhua; Chen, Yu; Taya, Fumihiko; Lim, Julian; Wong, Kianfoong; Sun, Yu; Bezerianos, Anastasios
Artifacts cause distortion and fuzziness in electroencephalographic (EEG) signal and hamper EEG analysis, so it is necessary to remove them prior to the analysis. Particularly, artifact removal becomes a critical issue in experimental protocols with significant inherent recording noise, such as mobile EEG recordings and concurrent EEG-fMRI acquisitions. In this paper, we proposed a unified framework based on canonical correlation analysis for artifact removal. Raw signals were reorganized to construct a pair of matrices, based on which sources were sought through maximizing autocorrelation. Those sources related to artifacts were then removed by setting them as zeros, and the remaining sources were used to reconstruct artifact-free EEG. Both simulated and real recorded data were utilized to assess the proposed framework. Qualitative and quantitative results showed that the proposed framework was effective to remove artifacts from EEG signal. Specifically, the proposed method outperformed independent component analysis method for mitigating motion-related artifacts and had advantages for removing gradient artifact compared to the classical method (average artifacts subtraction) and the state-of-the-art method (optimal basis set) in terms of the combination of performance and computational complexity.
Menze, Julian [Erdgas Muenster GmbH, Muenster (Germany)
The Act for the reformation of the legal framework for the support of the power generation from renewable energy sources mainly consists of an amendment to the Renewable Energy Law (EEG) and becomes effective on 1st January, 2012. The author of the contribution under consideration reports on the most important new features of the EEG and gives an overview of the EEG 2012.
Henriksen, Jonas; Remvig, Line Sofie; Madsen, Rasmus Elsborg
of ictal and non-ictal iEEG were obtained. Characteristics of the seizures are represented by use of wavelet transformation (WT) features and classified by a support vector machine. When implementing a method used for sEEG on iEEG data, a great improvement in performance was obtained when the high...
Zhang, Meiyun; Zhang, Benshu; Chen, Ying
Wavelet entropy is a quantitative index to describe the complexity of signals. Continuous wavelet transform method was employed to analyze the spontaneous electroencephalogram (EEG) signals of mild, moderate and severe Alzheimer's disease (AD) patients and normal elderly control people in this study. Wavelet power spectrums of EEG signals were calculated based on wavelet coefficients. Wavelet entropies of mild, moderate and severe AD patients were compared with those of normal controls. The correlation analysis between wavelet entropy and MMSE score was carried out. There existed significant difference on wavelet entropy among mild, moderate, severe AD patients and normal controls (Pentropy for mild, moderate, severe AD patients was significantly lower than that for normal controls, which was related to the narrow distribution of their wavelet power spectrums. The statistical difference was significant (Pentropy of EEG and the MMSE score were significantly correlated (r= 0. 601-0. 799, Pentropy is a quantitative indicator describing the complexity of EEG signals. Wavelet entropy is likely to be an electrophysiological index for AD diagnosis and severity assessment.
Moore, Adrienne; Gorodnitsky, Irina; Pineda, Jaime
Simulation theories for the perceptual processing of emotional faces assert that observers recruit the neural circuitry involved in creating their own emotional facial expressions in order to recognize the emotions and infer the feelings of others. The EEG mu rhythm is a sensorimotor oscillation hypothesized to index simulation of some actions during perceptual processing of these actions. The purpose of this research was to extend the study of mu rhythm simulation responses during perceptual tasks to the domain of emotional face perception. Subjects viewed happy and disgusted face photos with empathy and non-empathy task instructions while EEG responses were measured. EEG components were isolated and analyzed using a blind source separation (BSS) method. Mu components were found to respond to the perception of happy and disgusted faces during both empathy and non-empathy tasks with an event-related desynchronization (ERD), activation that is consistent with face simulation. Significant differences were found between responses to happy and to disgusted faces across the right hemisphere mu components beginning about 500ms after stimulus presentation. These findings support a simulation account of perceptual face processing based on a sensorimotor mirroring mechanism, and are the first report of distinct EEG mu responses to observation of positively and negatively valenced emotional faces. Copyright © 2011 Elsevier B.V. All rights reserved.
Gärtner, Matthias; Brodbeck, Verena; Laufs, Helmut; Schneider, Gaby
The analysis of spontaneous resting state neuronal activity is assumed to give insight into the brain function. One noninvasive technique to study resting state activity is electroencephalography (EEG) with a subsequent microstate analysis. This technique reduces the recorded EEG signal to a sequence of prototypical topographical maps, which is hypothesized to capture important spatio-temporal properties of the signal. In a statistical EEG microstate analysis of healthy subjects in wakefulness and three stages of sleep, we observed a simple structure in the microstate transition matrix. It can be described with a first order Markov chain in which the transition probability from the current state (i.e., map) to a different map does not depend on the current map. The resulting transition matrix shows a high agreement with the observed transition matrix, requiring only about 2% of mass transport (1/2 L1-distance). In the second part, we introduce an extended framework in which the simple Markov chain is used to make inferences on a potential underlying time continuous process. This process cannot be directly observed and is therefore usually estimated from discrete sampling points of the EEG signal given by the local maxima of the global field power. Therefore, we propose a simple stochastic model called sampled marked intervals (SMI) model that relates the observed sequence of microstates to an assumed underlying process of background intervals and thus, complements approaches that focus on the analysis of observable microstate sequences. Copyright © 2014 Elsevier Inc. All rights reserved.
Beauregard, Mario; Paquette, Vincent
Mystical experiences relate to a fundamental dimension of human existence. These experiences, which are characterized by a sense of union with God, are commonly reported across all cultures. To date, no electroencephalography (EEG) study has been conducted to identify the neuroelectrical correlates of such experiences. The main objective of this study was to measure EEG spectral power and coherence in 14 Carmelite nuns during a mystical experience. EEG activity was recorded from 19 scalp locations during a resting state, a control condition and a mystical condition. In the mystical condition compared to control condition, electrode sites showed greater theta power at F3, C3, P3, Fz, Cz and Pz, and greater gamma1 power was detected at T4 and P4. Higher delta/beta ratio, theta/alpha ratio and theta/beta ratio were found for several electrode sites. In addition, FP1-C3 pair of electrodes displayed greater coherence for theta band while F4-P4, F4-T6, F8-T6 and C4-P4 pairs of electrodes showed greater coherence for alpha band. These results indicate that mystical experiences are mediated by marked changes in EEG power and coherence. These changes implicate several cortical areas of the brain in both hemispheres.
Haider H. Alwasiti
Full Text Available Almost all religions incorporate some form of meditation. Muslim prayer is the meditation of Islam. It is an obligatory prayer for all Muslims that is performed five times a day. Although a large body of literature exists on EEG changes in meditation, to date there has been no research published in a peer-reviewed journal on EEG changes during Muslim prayer. The purpose of this pilot study is to encourage further investigation on this type of meditation. Results of EEG analysis in twenty-five trials of Muslim prayer are reported. Some of the findings are consistent with the majority of the previous meditation studies (alpha rhythm slowing, increased alpha rhythm coherence. However, Muslim prayer does not show an increase in alpha and/or theta power like most of the results of other meditation studies. The possible cause of this discrepancy in meditation-related studies is highlighted and a systematic and standardised roadmap for future Muslim prayer EEG research is proposed.
Tarokh, Leila; Carskadon, Mary A; Achermann, Peter
Waking and sleep data in adults show high heritability and trait-like characteristics in EEG spectra. This phenomenon has not been examined in children and adolescents where brain development influences the EEG. The present study examines whether a trait-like sleep EEG pattern is detectable across adolescent development. Two consecutive nights of standard sleep recordings were performed in 19 9-10-year-old children and 26 15-16-year-old teens, and were repeated 1.5-3 years later. EEG spectra averaged across the night for non-rapid eye movement and rapid eye movement sleep separately were classified using hierarchical cluster analysis, which showed that all 4 nights of a participant clustered together for a majority of participants. Intraclass correlation coefficients were also very high (>0.7) across nights separated by several years, indicating a trait-like feature of the sleep EEG. In summary, our results, using two measures of stability, indicate that a "trait-like" aspect can be detected in the sleep EEG across adolescent development despite considerable neurodevelopmental changes. This finding indicates that the brain oscillators responsible for generating the sleep EEG signal remain relatively stable across adolescent development.
Asaduzzaman, K; Reaz, M B I; Mohd-Yasin, F; Sim, K S; Hussain, M S
Electroencephalogram (EEG) serves as an extremely valuable tool for clinicians and researchers to study the activity of the brain in a non-invasive manner. It has long been used for the diagnosis of various central nervous system disorders like seizures, epilepsy, and brain damage and for categorizing sleep stages in patients. The artifacts caused by various factors such as Electrooculogram (EOG), eye blink, and Electromyogram (EMG) in EEG signal increases the difficulty in analyzing them. Discrete wavelet transform has been applied in this research for removing noise from the EEG signal. The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Difference. This paper reports on the effectiveness of wavelet transform applied to the EEG signal as a means of removing noise to retrieve important information related to both healthy and epileptic patients. Wavelet-based noise removal on the EEG signal of both healthy and epileptic subjects was performed using four discrete wavelet functions. With the appropriate choice of the wavelet function (WF), it is possible to remove noise effectively to analyze EEG significantly. Result of this study shows that WF Daubechies 8 (db8) provides the best noise removal from the raw EEG signal of healthy patients, while WF orthogonal Meyer does the same for epileptic patients. This algorithm is intended for FPGA implementation of portable biomedical equipments to detect different brain state in different circumstances.
Ruijter, B J; Hofmeijer, J; Meijer, H G E; van Putten, M J A M
In postanoxic coma, EEG patterns indicate the severity of encephalopathy and typically evolve in time. We aim to improve the understanding of pathophysiological mechanisms underlying these EEG abnormalities. We used a mean field model comprising excitatory and inhibitory neurons, local synaptic connections, and input from thalamic afferents. Anoxic damage is modeled as aggravated short-term synaptic depression, with gradual recovery over many hours. Additionally, excitatory neurotransmission is potentiated, scaling with the severity of anoxic encephalopathy. Simulations were compared with continuous EEG recordings of 155 comatose patients after cardiac arrest. The simulations agree well with six common categories of EEG rhythms in postanoxic encephalopathy, including typical transitions in time. Plausible results were only obtained if excitatory synapses were more severely affected by short-term synaptic depression than inhibitory synapses. In postanoxic encephalopathy, the evolution of EEG patterns presumably results from gradual improvement of complete synaptic failure, where excitatory synapses are more severely affected than inhibitory synapses. The range of EEG patterns depends on the excitation-inhibition imbalance, probably resulting from long-term potentiation of excitatory neurotransmission. Our study is the first to relate microscopic synaptic dynamics in anoxic brain injury to both typical EEG observations and their evolution in time. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
Cichocki, Andrzej; Shishkin, Sergei L; Musha, Toshimitsu; Leonowicz, Zbigniew; Asada, Takashi; Kurachi, Takayoshi
Development of an EEG preprocessing technique for improvement of detection of Alzheimer's disease (AD). The technique is based on filtering of EEG data using blind source separation (BSS) and projection of components which are possibly sensitive to cortical neuronal impairment found in early stages of AD. Artifact-free 20s intervals of raw resting EEG recordings from 22 patients with Mild Cognitive Impairment (MCI) who later proceeded to AD and 38 age-matched normal controls were decomposed into spatio-temporally decorrelated components using BSS algorithm 'AMUSE'. Filtered EEG was obtained by back projection of components with the highest linear predictability. Relative power of filtered data in delta, theta, alpha 1, alpha 2, beta 1, and beta 2 bands were processed with Linear Discriminant Analysis (LDA). Preprocessing improved the percentage of correctly classified patients and controls computed with jack-knifing cross-validation from 59 to 73% and from 76 to 84%, correspondingly. The proposed approach can significantly improve the sensitivity and specificity of EEG based diagnosis. Filtering based on BSS can improve the performance of the existing EEG approaches to early diagnosis of Alzheimer's disease. It may also have potential for improvement of EEG classification in other clinical areas or fundamental research. The developed method is quite general and flexible, allowing for various extensions and improvements.
Bono, Valentina; Das, Saptarshi; Jamal, Wasifa; Maharatna, Koushik
Electroencephalogram (EEG) signals are often corrupted with unintended artifacts which need to be removed for extracting meaningful clinical information from them. Typically a priori knowledge of the nature of the artifacts is needed for such purpose. Artifact contamination of EEG is even more prominent for pervasive EEG systems where the subjects are free to move and thereby introducing a wide variety of motion-related artifacts. This makes hard to get a priori knowledge about their characteristics rendering conventional artifact removal techniques often ineffective. In this paper, we explore the performance of two hybrid artifact removal algorithms: Wavelet Packet Transform followed by Independent Component Analysis (WPTICA) and Wavelet Packet Transform followed by Empirical Mode Decomposition (WPTEMD) in pervasive EEG recording scenario, assuming existence of no a priori knowledge about the artifacts and compare their performance with two existing artifact removal algorithms. Artifact cleaning performance has been measured using Root Mean Square Error (RMSE) and Artifact to Signal Ratio (ASR)-an index similar to traditional Signal to Noise Ratio (SNR), and also by observing normalized power distribution topography over the scalp. Comparison has been made first using semi-simulated signals and then with real experimentally acquired EEG data with commercially available 19-channel pervasive EEG system Enobio corrupted by eight types of artifact. Our explorations show that WPTEMD consistently gives best artifact cleaning performance not only in semi-simulated scenario but also in the case of real EEG data containing artifacts. Copyright © 2016 Elsevier B.V. All rights reserved.
Liang, Zhenhu; Duan, Xuejing; Su, Cui; Voss, Logan; Sleigh, Jamie; Li, Xiaoli
Modeling the effects of anesthetic drugs on brain activity is very helpful in understanding anesthesia mechanisms. The aim of this study was to set up a combined model to relate actual drug levels to EEG dynamics and behavioral states during propofol-induced anesthesia. We proposed a new combined theoretical model based on a pharmacokinetics (PK) model and a neural mass model (NMM), which we termed PK-NMM--with the aim of simulating electroencephalogram (EEG) activity during propofol-induced general anesthesia. The PK model was used to derive propofol effect-site drug concentrations (C(eff)) based on the actual drug infusion regimen. The NMM model took C(eff) as the control parameter to produce simulated EEG-like (sEEG) data. For comparison, we used real prefrontal EEG (rEEG) data of nine volunteers undergoing propofol anesthesia from a previous experiment. To see how well the sEEG could describe the dynamic changes of neural activity during anesthesia, the rEEG data and the sEEG data were compared with respect to: power-frequency plots; nonlinear exponent (permutation entropy (PE)); and bispectral SynchFastSlow (SFS) parameters. We found that the PK-NMM model was able to reproduce anesthesia EEG-like signals based on the estimated drug concentration and patients' condition. The frequency spectrum indicated that the frequency power peak of the sEEG moved towards the low frequency band as anesthesia deepened. Different anesthetic states could be differentiated by the PE index. The correlation coefficient of PE was 0.80 ± 0.13 (mean ± standard deviation) between rEEG and sEEG for all subjects. Additionally, SFS could track the depth of anesthesia and the SFS of rEEG and sEEG were highly correlated with a correlation coefficient of 0.77 ± 0.13. The PK-NMM model could simulate EEG activity and might be a useful tool for understanding the action of propofol on brain activity.
Feyissa, Anteneh M; Britton, Jeffrey W; Van Gompel, Jamie; Lagerlund, Terrance L; So, Elson; Wong-Kisiel, Lilly C; Cascino, Gregory C; Brinkman, Benjamin H; Nelson, Cindy L; Watson, Robert; Worrell, Gregory A
Localization of seizures in frontal lobe epilepsy using the 10-20 system scalp EEG is often challenging because neocortical seizure can spread rapidly, significant muscle artifact, and the suboptimal spatial resolution for seizure generators involving mesial frontal lobe cortex. Our aim in this study was to determine the value of visual interpretation of 76 channel high density EEG (hdEEG) monitoring (10-10 system) in patients with suspected frontal lobe epilepsy, and to evaluate concordance with MRI, subtraction ictal SPECT co-registered to MRI (SISCOM), conventional EEG, and intracranial EEG (iEEG). We performed a retrospective cohort study of 14 consecutive patients who underwent hdEEG monitoring for suspected frontal lobe seizures. The gold standard for localization was considered to be iEEG. Concordance of hdEEG findings with MRI, subtraction ictal SPECT co-registered to MRI (SISCOM), conventional 10-20 EEG, and iEEG as well as correlation of hdEEG localization with surgical outcome were examined. hdEEG localization was concordant with iEEG in 12/14 and was superior to conventional EEG 3/14 (pfrontal epilepsy requiring localization of epileptogenic brain. hdEEG may assist in developing a hypothesis for iEEG monitoring and could potentially augment EEG source localization. Published by Elsevier B.V.
Grozea, Cristian; Voinescu, Catalin D.; Fazli, Siamac
In this paper, we present a new, low-cost dry electrode for EEG that is made of flexible metal-coated polymer bristles. We examine various standard EEG paradigms, such as capturing occipital alpha rhythms, testing for event-related potentials in an auditory oddball paradigm and performing a sensory motor rhythm-based event-related (de-) synchronization paradigm to validate the performance of the novel electrodes in terms of signal quality. Our findings suggest that the dry electrodes that we developed result in high-quality EEG recordings and are thus suitable for a wide range of EEG studies and BCI applications. Furthermore, due to the flexibility of the novel electrodes, greater comfort is achieved in some subjects, this being essential for long-term use.
Koren, J; Herta, J; Draschtak, S; Pötzl, G; Pirker, S; Fürbass, F; Hartmann, M; Kluge, T; Baumgartner, C
Continuous EEG (cEEG) is necessary to document nonconvulsive seizures (NCS), nonconvulsive status epilepticus (NCSE), as well as rhythmic and periodic EEG patterns of 'ictal-interictal uncertainty' (RPPIIU) including periodic discharges, rhythmic delta activity, and spike-and-wave complexes in neurological intensive care patients. However, cEEG is associated with significant recording and analysis efforts. Therefore, predictors from short-term routine EEG with a reasonably high yield are urgently needed in order to select patients for evaluation with cEEG. The aim of this study was to assess the prognostic significance of early epileptiform discharges (i.e., within the first 30 min of EEG recording) on the following: (1) incidence of ictal EEG patterns and RPPIIU on subsequent cEEG, (2) occurrence of acute convulsive seizures during the ICU stay, and (3) functional outcome after 6 months of follow-up. We conducted a separate analysis of the first 30 min and the remaining segments of prospective cEEG recordings according to the ACNS Standardized Critical Care EEG Terminology as well as NCS criteria and review of clinical data of 32 neurological critical care patients. In 17 patients with epileptiform discharges within the first 30 min of EEG (group 1), electrographic seizures were observed in 23.5% (n = 4), rhythmic or periodic EEG patterns of 'ictal-interictal uncertainty' in 64.7% (n = 11), and neither electrographic seizures nor RPPIIU in 11.8% (n = 2). In 15 patients with no epileptiform discharges in the first 30 min of EEG (group 2), no electrographic seizures were recorded on subsequent cEEG, RPPIIU were seen in 26.7% (n = 4), and neither electrographic seizures nor RPPIIU in 73.3% (n = 11). The incidence of EEG patterns on cEEG was significantly different between the two groups (p = 0.008). Patients with early epileptiform discharges developed acute seizures more frequently than patients without early epileptiform discharges (p = 0.009). Finally, functional
Loo, Sandra K; Makeig, Scott
Psychiatric research applications of electroencephalography (EEG), the earliest approach to imaging human cortical brain activity, are attracting increasing scientific and clinical interest. For more than 40 years, EEG research has attempted to characterize and quantify the neurophysiology of attention-deficit/hyperactivity disorder (ADHD), most consistently associating it with increased frontocentral theta band activity and increased theta to beta (θ/β) power ratio during rest compared to non-ADHD controls. Recent reports suggest that while these EEG measures demonstrate strong discriminant validity for ADHD, significant EEG heterogeneity also exists across ADHD-diagnosed individuals. In particular, additional studies validating the use of the θ/β power ratio measure appear to be needed before it can be used for clinical diagnosis. In recent years, the number and the scientific quality of research reports on EEG-based neurofeedback (NF) for ADHD have grown considerably, although the studies reviewed here do not yet support NF training as a first-line, stand-alone treatment modality. In particular, more research is needed comparing NF to placebo control and other effective treatments for ADHD. Currently, after a long period of relative stasis, the neurophysiological specificity of measures used in EEG research is rapidly increasing. It is likely, therefore, that new EEG studies of ADHD using higher density recordings and new measures drawn from viewing EEG as a 3-dimensional functional imaging modality, as well as intensive re-analyses of existing EEG study data, can better characterize the neurophysiological differences between and within ADHD and non-ADHD subjects, and lead to more precise diagnostic measures and effective NF approaches.
Full Text Available The electroencephalogram (EEG signal represents a subject’s specific brain activity patterns and is considered as an ideal biometric given its superior forgery prevention. However, the accuracy and stability of the current EEG-based person authentication systems are still unsatisfactory in practical application. In this paper, a multi-task EEG-based person authentication system combining eye blinking is proposed, which can achieve high precision and robustness. Firstly, we design a novel EEG-based biometric evoked paradigm using self- or non-self-face rapid serial visual presentation (RSVP. The designed paradigm could obtain a distinct and stable biometric trait from EEG with a lower time cost. Secondly, the event-related potential (ERP features and morphological features are extracted from EEG signals and eye blinking signals, respectively. Thirdly, convolutional neural network and back propagation neural network are severally designed to gain the score estimation of EEG features and eye blinking features. Finally, a score fusion technology based on least square method is proposed to get the final estimation score. The performance of multi-task authentication system is improved significantly compared to the system using EEG only, with an increasing average accuracy from 92.4% to 97.6%. Moreover, open-set authentication tests for additional imposters and permanence tests for users are conducted to simulate the practical scenarios, which have never been employed in previous EEG-based person authentication systems. A mean false accepted rate (FAR of 3.90% and a mean false rejected rate (FRR of 3.87% are accomplished in open-set authentication tests and permanence tests, respectively, which illustrate the open-set authentication and permanence capability of our systems.
Campbell, Ian G; Bromberger, Joyce T; Buysse, Daniel J; Hall, Martica H; Hardin, Kimberly A; Kravitz, Howard M; Matthews, Karen A; Rasor, Marianne O'Neill; Utts, Jessica; Gold, Ellen
Women report increasing sleep difficulties during menopause, but polysomnographic measures do not detect sleep disturbances. We examined whether two spectral analysis sleep measures, delta and beta power, were related to menopausal status. The Study of Women's Health Across the Nation (SWAN) Sleep Study compared cross-sectionally spectral sleep measures in women in different stages of menopause. Sleep EEG was recorded in the participants' homes with ambulatory recorders. A multi-ethnic cohort of premenopausal and early perimenopausal (n = 189), late perimenopausal (n = 73), and postmenopausal (n = 59) women. EEG power in the delta and beta frequency bands was calculated for all night NREM and all night REM sleep. Physical, medical, psychological, and socioeconomic data were collected from questionnaires and diaries. Beta EEG power in NREM and REM sleep in late perimenopausal and postmenopausal women exceeded that in pre- and early perimenopausal women. Neither all night delta power nor the trend in delta power across the night differed by menopausal status. In a multivariate model that controlled for the physical, demographic, behavioral, psychological, and health-related changes that accompany menopause, beta power in both NREM and REM sleep EEG was significantly related to menopausal status. The frequency of hot flashes explained part but not all of the relation of beta power to menopausal status. Elevated beta EEG power in late perimenopausal and postmenopausal women provides an objective measure of disturbed sleep quality in these women. Elevated beta EEG activity suggests that arousal level during sleep is higher in these women.
Full Text Available Medical studies have shown that EEG of Alzheimer's disease (AD patients is “slower” (i.e., contains more low-frequency power and is less complex compared to age-matched healthy subjects. The relation between those two phenomena has not yet been studied, and they are often silently assumed to be independent. In this paper, it is shown that both phenomena are strongly related. Strong correlation between slowing and loss of complexity is observed in two independent EEG datasets: (1 EEG of predementia patients (a.k.a. Mild Cognitive Impairment; MCI and control subjects; (2 EEG of mild AD patients and control subjects. The two data sets are from different patients, different hospitals and obtained through different recording systems. The paper also investigates the potential of EEG slowing and loss of EEG complexity as indicators of AD onset. In particular, relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age-matched control subjects. When combined with two synchrony measures (Granger causality and stochastic event synchrony, classification rates of 83% (MCI and 98% (MiAD are obtained. By including the compression ratios as features, slightly better classification rates are obtained than with relative power and synchrony measures alone.
Meng, X; Xu, J; Gu, F
The generalized dimension defined by [Mandelbrot (1995) J Fourier Anal Appl special J.P. Kahane issue: 409-432] was applied to studying the interrelationship between various parts of human cerebral cortex in different functional conditions. Taking EEG signals from different brain areas as different sets, the generalized dimensions of their intersections were calculated to describe the interrelationship between them. The results showed that the generalized dimensions of intersections in different brain states decreased according to the following order: rest with eyes open, closed, light sleep, and deep sleep. The generalized dimensions of intersections related to the left or right temporal lobe were higher than the others when the subjects was doing mental arithmetic, and there was a decrease when the subjects listened to soft classical music. In addition, it was found that there was a noticeable difference in singular spectra between epileptic patients and normal subjects, irrespective of whether the epileptic patient was experiencing a seizure or not.
Zhang, Jing; Liu, Weifang; Chen, Hui; Xia, Hong; Zhou, Zhen; Wang, Lei; Mei, Shanshan; Liu, Qingzhu; Li, Yunlin
Simultaneous EEG-fMRI is a non-invasive investigation technique developed to localize the generators of interictal epileptiform discharges (IED) in patients with epilepsy. Although the value of EEG-fMRI in epilepsy presurgical evaluation is being assessed clinically, its utility is still controversial. In this review, we considered EEG-fMRI applications in epilepsy presurgical evaluation with a focus on validation studies that compared the results of EEG-fMRI with those of the current "gold standard" intracranial EEG (icEEG) in order to assess its utility of seizure focus localization and the possibility for EEG-fMRI to reduce the need for invasive techniques such as icEEG. Since the advances of EEG-fMRI partially rely on the maturation of its data analysis, we also reviewed the methodological developments in EEG-fMRI analysis. It is possible that combining with other neuroimaging modalities such as MEG/MSI and ESI, EEG-fMRI may play a greater role in epilepsy presurgical evaluation. Copyright © 2012 Elsevier B.V. All rights reserved.
Full Text Available Michelle Case,1 Sina Shirinpour,1 Huishi Zhang,1 Yvonne H Datta,2 Stephen C Nelson,3 Karim T Sadak,4 Kalpna Gupta,2 Bin He1,5 1Department of Biomedical Engineering, 2Department of Medicine, University of Minnesota, 3Pediatric Hematology-Oncology, Children’s Hospitals and Clinics of Minnesota, 4Pediatric Hematology-Oncology, University of Minnesota Masonic Children’s Hospital, 5Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN, USA Objective: Pain is a major issue in the care of patients with sickle cell disease (SCD. The mechanisms behind pain and the best way to treat it are not well understood. We studied how electroencephalography (EEG is altered in SCD patients. Methods: We recruited 20 SCD patients and compared their resting state EEG to that of 14 healthy controls. EEG power was found across frequency bands using Welch’s method. Electrophysiological source imaging was assessed for each frequency band using the eLORETA algorithm. Results: SCD patients had increased theta power and decreased beta2 power compared to controls. Source localization revealed that areas of greater theta band activity were in areas related to pain processing. Imaging parameters were significantly correlated to emergency department visits, which indicate disease severity and chronic pain intensity. Conclusion: The present results support the pain mechanism referred to as thalamocortical dysrhythmia. This mechanism causes increased theta power in patients. Significance: Our findings show that EEG can be used to quantitatively evaluate differences between controls and SCD patients. Our results show the potential of EEG to differentiate between different levels of pain in an unbiased setting, where specific frequency bands could be used as biomarkers for chronic pain. Keywords: sickle cell disease, electroencephalography, chronic pain, electrophysiological source imaging, thalamocortical dysrhythmia
Petrantonakis, Panagiotis C; Hadjileontiadis, Leontios J
Electroencephalogram (EEG)-based emotion recognition is a relatively new field in the affective computing area with challenging issues regarding the induction of the emotional states and the extraction of the features in order to achieve optimum classification performance. In this paper, a novel emotion evocation and EEG-based feature extraction technique is presented. In particular, the mirror neuron system concept was adapted to efficiently foster emotion induction by the process of imitation. In addition, higher order crossings (HOC) analysis was employed for the feature extraction scheme and a robust classification method, namely HOC-emotion classifier (HOC-EC), was implemented testing four different classifiers [quadratic discriminant analysis (QDA), k-nearest neighbor, Mahalanobis distance, and support vector machines (SVMs)], in order to accomplish efficient emotion recognition. Through a series of facial expression image projection, EEG data have been collected by 16 healthy subjects using only 3 EEG channels, namely Fp1, Fp2, and a bipolar channel of F3 and F4 positions according to 10-20 system. Two scenarios were examined using EEG data from a single-channel and from combined-channels, respectively. Compared with other feature extraction methods, HOC-EC appears to outperform them, achieving a 62.3% (using QDA) and 83.33% (using SVM) classification accuracy for the single-channel and combined-channel cases, respectively, differentiating among the six basic emotions, i.e., happiness, surprise, anger, fear, disgust, and sadness. As the emotion class-set reduces its dimension, the HOC-EC converges toward maximum classification rate (100% for five or less emotions), justifying the efficiency of the proposed approach. This could facilitate the integration of HOC-EC in human machine interfaces, such as pervasive healthcare systems, enhancing their affective character and providing information about the user's emotional status (e.g., identifying user's emotion
Manuela eGander; Anna eBuchheim
In recent years research on physiological response and frontal electroencephalographic (EEG) asymmetry in different patterns of infant and adult attachment has increased. We review research findings regarding associations between attachment classifications and frontal EEG asymmetry, the autonomic nervous system (ANS) and the hypothalamic-pituitary-adrenocortical axis (HPA). Studies indicate that insecure attachment is related to a heightened adrenocortical activity, heart rate and skin conduc...
Lopes da Silva, F.
To understand dynamic cognitive processes, the high time resolution of EEG/MEG is invaluable. EEG/MEG signals can play an important role in providing measures of functional and effective connectivity in the brain. After a brief description of the foundations and basic methodological aspects of
Christensen, Christian Bech; Kidmose, Preben
life. Ear-EEG may therefore be an enabling technology for objective audiometry out of the clinic, allowing regularly fitting of the hearing aids to be made by the users in their everyday life environment. In this study we investigate the application of ear-EEG in objective audiometry....
Full Text Available Objective: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs’ effect is lacking. In this study, we compare the capability of twelve entropy indices for monitoring depth of anesthesia (DoA and detecting the burst suppression pattern (BSP, in anesthesia induced by GA-BAergic agents.Methods: Twelve indices were investigated, namely Response Entropy (RE and State entropy (SE, three wavelet entropy (WE measures (Shannon WE (SWE, Tsallis WE (TWE and Renyi WE (RWE, Hilbert-Huang spectral entropy (HHSE, approximate entropy (ApEn, sample entropy (SampEn, Fuzzy entropy, and three permutation entropy (PE measures (Shannon PE (SPE, Tsallis PE (TPE and Renyi PE (RPE. Two EEG data sets from sevoflurane-induced and isoflu-rane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, phar-macokinetic / pharmacodynamic (PK/PD modeling and prediction probability analysis were applied. The multifractal detrended fluctuation analysis (MDFA as a non-entropy measure was compared.Results: All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline vari-ability, higher coefficient of determination and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an ad-vantage in computation efficiency compared with MDFA.Conclusion: Each entropy index has its advantages and disadvantages in estimating DoA. Overall, it is suggested that the RPE index was a superior measure.Significance: Investigating the advantages and disadvantages of these entropy indices could help improve current clinical indices for monitoring DoA.
Lobo, Isabela; Portugal, Liana Catarina; Figueira, Ivan; Volchan, Eliane; David, Isabel; Garcia Pereira, Mirtes; de Oliveira, Leticia
Considering the Research Domain Criteria (RDoC) framework, it is crucial to investigate posttraumatic stress disorder (PTSD) as a spectrum that ranges from normal to pathological. This dimensional approach is especially important to aid early PTSD detection and to guide better treatment options. In recent years, electroencephalography (EEG) has been used to investigate PTSD; however, reviews regarding EEG data related to PTSD are lacking, especially considering the dimensional approach. This systematic review examined the literature regarding EEG alterations in trauma-exposed people with posttraumatic stress symptoms (PTSS) to identify putative EEG biomarkers of PTSS severity. A systematic review of EEG studies of trauma-exposed participants with PTSS that reported dimensional analyses (e.g., correlations or regressions) between PTSS and EEG measures was performed. The literature search yielded 1178 references, of which 34 studies were eligible for inclusion. Despite variability among the reviewed studies, the PTSS severity was often associated with P2, P3-family event-related potentials (ERPs) and alpha rhythms. The search was limited to articles published in English; no information about non-published studies or studies reported in other languages was obtained. Another limitation was the heterogeneity of studies, which made meta-analysis challenging. EEG provides promising candidates to act as biomarkers, although further studies are required to confirm the findings. Thus, EEG, in addition to being cheaper and easier to implement than other central techniques, has the potential to reveal biomarkers of PTSS severity. Copyright © 2015 Elsevier B.V. All rights reserved.
Liang, Zhenhu; Wang, Yinghua; Sun, Xue; Li, Duan; Voss, Logan J; Sleigh, Jamie W; Hagihira, Satoshi; Li, Xiaoli
► Twelve entropy indices were systematically compared in monitoring depth of anesthesia and detecting burst suppression.► Renyi permutation entropy performed best in tracking EEG changes associated with different anesthesia states.► Approximate Entropy and Sample Entropy performed best in detecting burst suppression. Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs' effect is lacking. In this study, we compare the capability of 12 entropy indices for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GABAergic agents. Twelve indices were investigated, namely Response Entropy (RE) and State entropy (SE), three wavelet entropy (WE) measures [Shannon WE (SWE), Tsallis WE (TWE), and Renyi WE (RWE)], Hilbert-Huang spectral entropy (HHSE), approximate entropy (ApEn), sample entropy (SampEn), Fuzzy entropy, and three permutation entropy (PE) measures [Shannon PE (SPE), Tsallis PE (TPE) and Renyi PE (RPE)]. Two EEG data sets from sevoflurane-induced and isoflurane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability (Pk) analysis were applied. The multifractal detrended fluctuation analysis (MDFA) as a non-entropy measure was compared. All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline variability, higher coefficient of determination (R (2)) and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an advantage in computation efficiency compared with MDFA. Each
Liang, Zhenhu; Wang, Yinghua; Sun, Xue; Li, Duan; Voss, Logan J.; Sleigh, Jamie W.; Hagihira, Satoshi; Li, Xiaoli
Highlights: ► Twelve entropy indices were systematically compared in monitoring depth of anesthesia and detecting burst suppression.► Renyi permutation entropy performed best in tracking EEG changes associated with different anesthesia states.► Approximate Entropy and Sample Entropy performed best in detecting burst suppression. Objective: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs' effect is lacking. In this study, we compare the capability of 12 entropy indices for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GABAergic agents. Methods: Twelve indices were investigated, namely Response Entropy (RE) and State entropy (SE), three wavelet entropy (WE) measures [Shannon WE (SWE), Tsallis WE (TWE), and Renyi WE (RWE)], Hilbert-Huang spectral entropy (HHSE), approximate entropy (ApEn), sample entropy (SampEn), Fuzzy entropy, and three permutation entropy (PE) measures [Shannon PE (SPE), Tsallis PE (TPE) and Renyi PE (RPE)]. Two EEG data sets from sevoflurane-induced and isoflurane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability (Pk) analysis were applied. The multifractal detrended fluctuation analysis (MDFA) as a non-entropy measure was compared. Results: All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline variability, higher coefficient of determination (R2) and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an advantage in computation
Jacobson, S A; Leuchter, A F; Walter, D O
This study was performed to determine whether an admission quantitative EEG (QEEG) could assist in the differential diagnosis of encephalopathy among a group of elderly subjects with delirium, dementia, and delirium coexistent with dementia. Thirty four subjects from 57 to 93 years had standard 17-channel EEG and quantitative EEG studies, using a linked-ear reference. EEGs were independently rated by two electroencephalographers blind to clinical diagnosis, using conventional criteria to assess the degree of encephalopathy. Brain maps were scored by a scale developed by the authors. Numerical data examined included mean posterior dominant frequency, absolute and relative power in the delta, theta and alpha bands, and slow-wave ratios. The grouping of experimental subjects was by the discharge diagnosis, made using DSM-III-R criteria. Stepwise discriminant analysis was performed to determine which EEG and QEEG variables were best able to distinguish cases. Variables which collectively distinguished normal from encephalopathic records were Mini-Mental State Examination scores and relative power in the alpha frequency band. Variables which collectively distinguished delirium from dementia were EEG theta activity, relative power in delta, and brain map rating. The results suggest that cross-sectional QEEG study is potentially useful in the early differential diagnosis of encephalopathy, and that the variables which distinguish normal from encephalopathic patients might differ from the variables which distinguish delirium from dementia.
Full Text Available With the advancements in modern signal processing techniques, the field of brain-computer interface (BCI is progressing fast towards noninvasiveness. One challenge still impeding these developments is the limited number of features, especially movement-related features, available to generate control signals for noninvasive BCIs. A few recent studies investigated several movement-related features, such as spectral features in electrocorticography (ECoG data obtained through a spectral principal component analysis (PCA and direct use of EEG temporal data, and demonstrated the decoding of individual fingers. The present paper evaluated multiple movement-related features under the same task, that is, discriminating individual fingers from one hand using noninvasive EEG. The present results demonstrate the existence of a broadband feature in EEG to discriminate individual fingers, which has only been identified previously in ECoG. It further shows that multiple spectral features obtained from the spectral PCA yield an average decoding accuracy of 45.2%, which is significantly higher than the guess level (P<0.05 and other features investigated (P<0.05, including EEG spectral power changes in alpha and beta bands and EEG temporal data. The decoding of individual fingers using noninvasive EEG is promising to improve number of features for control, which can facilitate the development of noninvasive BCI applications with rich complexity.
van der Zaag, J.; Naeije, M.; Wicks, D.J.; Hamburger, H.L.; Lobbezoo, F.
Objective Sleep bruxism (SB) and periodic limb movements during sleep (PLMS) may have a common underlying neurophysiologic mechanism, especially in relation to the occurrence of sleep-related electroencephalographic (EEG) arousals. To test this hypothesis, three research questions were assessed.
Feltane, Amal; Faye Boudreaux-Bartels, G; Besio, Walter
Automated detection of seizures is still a challenging problem. This study presents an approach to detect seizure segments in Laplacian electroencephalography (tEEG) recorded from rats using the tripolar concentric ring electrode (TCRE) configuration. Three features, namely, median absolute deviation, approximate entropy, and maximum singular value were calculated and used as inputs into two different classifiers: support vector machines and adaptive boosting. The relative performance of the extracted features on TCRE tEEG was examined. Results are obtained with an overall accuracy between 84.81 and 96.51%. In addition to using TCRE tEEG data, the seizure detection algorithm was also applied to the recorded EEG signals from Andrzejak et al. database to show the efficiency of the proposed method for seizure detection.
Prat, Chantel S; Yamasaki, Brianna L; Kluender, Reina A; Stocco, Andrea
Understanding the neurobiological basis of individual differences in second language acquisition (SLA) is important for research on bilingualism, learning, and neural plasticity. The current study used quantitative electroencephalography (qEEG) to predict SLA in college-aged individuals. Baseline, eyes-closed resting-state qEEG was used to predict language learning rate during eight weeks of French exposure using an immersive, virtual scenario software. Individual qEEG indices predicted up to 60% of the variability in SLA, whereas behavioral indices of fluid intelligence, executive functioning, and working-memory capacity were not correlated with learning rate. Specifically, power in beta and low-gamma frequency ranges over right temporoparietal regions were strongly positively correlated with SLA. These results highlight the utility of resting-state EEG for studying the neurobiological basis of SLA in a relatively construct-free, paradigm-independent manner. Published by Elsevier Inc.
Iinuma, Kazuie; Haginoya, Kazuhiro; Yanai, Kazuhiko (Tohoku Univ., Sendai (Japan). School of Medicine); Hatazawa, Jun; Ito, Masatoshi
Epileptic foci were determined by scalp EEG and positron emission tomography (PET) with fluorine 18 in 22 children with partial epilepsy (PE, n=13) and Lennoxy-Gastaut syndrome (LGS, n=9). The patients ranged in age from 6 to 18 years. The pattern of hypometabolism was classified into the following 4 categories: non-focal, localized, hemispheric, and diffuse. In the group of PE patients, 11 showed a relative agreement between the EEG foci and region of a low cerebral metabolic rate for glucose (CMRglc) determined by PET. A decreased CMRglc was matched with the EEG foci in 4 patients with LGS. A tendency of a higher relationship between the EEG foci and PET images was significant in PE than LGS. (N.K.).
Full Text Available Electroencephalogram (EEG recordings signal provide an important function of brain-computer communication, but the accuracy of their classification is very limited in unforeseeable signal variations relating to artifacts. In this paper, we propose a classification method entailing time-series EEG-P300 signals using backpropagation neural networks to predict the qualitative properties of a subject’s mental tasks by extracting useful information from the highly multivariate non-invasive recordings of brain activity. To test the improvement in the EEG-P300 classification performance (i.e., classification accuracy and transfer rate with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis (BLDA. Finally, the result of the experiment showed that the average of the classification accuracy was 97% and the maximum improvement of the average transfer rate is 42.4%, indicating the considerable potential of the using of EEG-P300 for the continuous classification of mental tasks.
Tiago da Silveira
Full Text Available Introduction In this paper we propose a promising new technique for drowsiness detection. It consists of applying the best m-term approximation on a single-channel electroencephalography (EEG signal preprocessed through a discrete wavelet transform. Methods In order to classify EEG epochs as awake or drowsy states, the most significant m terms from the wavelet expansion of an EEG signal are selected according to the magnitude of their coefficients related to the alpha and beta rhythms. Results By using a simple thresholding strategy it provides hit rates comparable to those using more complex techniques. It was tested on a set of 6 hours and 50 minutes EEG drowsiness signals from PhysioNet Sleep Database yielding an overall sensitivity (TPR of 84.98% and 98.65% of precision (PPV. Conclusion The method has proved itself efficient at separating data from different brain rhythms, thus alleviating the requirement for complex post-processing classification algorithms.
Hu, Guoying; Fan, Wenqiang; Liu, Yunxue; Hu, Junming
A fatigue detection system based on EEG and it's realization on FPGA were described in this paper, the basic components and the related principles of each module were introduced. The difficulties and innovations in realization on FPGA were given in details as well as some simulation results. This system can be applied to the real-time detecting of drivers' fatigue which can provide forewarning in time. EEG is a kind of weak bioelectricity which is easily interfered by strong noise. The acquired signal passed through a three-stage amplifier before AD converter, then the digital signal entered FPGA. There EEG was decomposed using DWT, some coefficients were deposed as random noise, and the signal was reconstructed to the de-noised one. Then according to the relationship between EEG and tiredness, FPGA calculated the fatigue criterion and gave out forewarning when the value was too large.
Isotani, T; Lehmann, D; Pascual-Marqui, R D; Kochi, K; Wackermann, J; Saito, N; Yagyu, T; Kinoshita, T; Sasada, K
Individuals differ in hypnotizability. Information on hypnotizability-related EEG characteristics is controversial and incomplete, particularly on intracerebral source localization and EEG dimensionality. 19-channel, eyes-closed resting EEGs from right-handed, healthy, 8 high- and 4 low-hynotizable subjects (age: 26.7 +/- 7.3 years) were analyzed. Hypnotizability was rated after the subjects' ability to attain a deep hypnotic stage (amnesia). FFT Dipole Approximation analysis in seven EEG frequency bands showed significant differences (p Power spectral analysis of Global Field Power time series (curves) showed no overall power differences in any band. Full-band Global Dimensional Complexity was higher in high-hypnotizable subjects (p < 0.02). Thus, before hypnosis, high and low hypnotizables were in different brain electric states, with more posterior brain activity gravity centers (excitatory right, routine or relaxation left) and higher dimensional complexity (higher arousal) in high than low hypnotizables. Copyright 2001 S. Karger AG, Basel
Gander, Manuela; Buchheim, Anna
In recent years research on physiological response and frontal electroencephalographic (EEG) asymmetry in different patterns of infant and adult attachment has increased. We review research findings regarding associations between attachment classifications and frontal EEG asymmetry, the autonomic nervous system (ANS) and the hypothalamic-pituitary-adrenocortical axis (HPA). Studies indicate that insecure attachment is related to a heightened adrenocortical activity, heart rate and skin conductance in response to stress, which is consistent with the hypothesis that attachment insecurity leads to impaired emotion regulation. Research on frontal EEG asymmetry also shows a clear difference in the emotional arousal between the attachment groups evidenced by specific frontal asymmetry changes. Furthermore, we discuss neurophysiological evidence of attachment organization and present up-to-date findings of EEG-research with adults. Based on the overall patterns of results presented in this article we identify some major areas of interest and directions for future research. PMID:25745393
Rösler, F; Bajrić, J; Heil, M; Hennighausen, E; Niedeggen, M; Pechmann, T; Röder, B; Rüsseler, J; Streb, J
The paper gives a brief overview of five experimental approaches in which memory processes were studied by means of event-related brain potentials (ERPs). Some of the results were already published in English (Study 1), while others are new and will be reported in greater length as full paper elsewhere (Studies 2, 3, 4, and 5). Study 1 revealed that retrieval of information from episodic long-term memory is accompanied by a systematic slow negative potential. The topography of this slow wave depends on the quality of the reactivated information (spatial vs. verbal), and its amplitude reflects the difficulty of the retrieval process. In experiment 2 ERPs were recorded while subjects acquired either explicit or implicit knowledge about a sequential stimulus-response pattern. The data suggest that explicit learners who posses verbalizable knowledge about sequential dependencies have formed both perceptual and motor representations, while implicit learners have formed motor representations only. In study 3 fact retrieval in mental arithmetic was activated by a verification task. Incongruent solutions evoked an arithmetic N400-effect whose amplitude varied with the associative distance between an expected and an actually perceived solution to a multiplication problem. In study 4 ERPs were recorded during mental rotation tasks. A set of experiments revealed that mental rotation is always accompanied by a systematic negative variation over the parietal cortex. The amplitude of this "rotation specific negativity" increases with an increasing angular disparity between a perceived sign and its normal upright template. It was shown that this negativity is functionally distinct from a P300-complex which is often superimposed on it within the same latency window. Finally, study 5 examined ERPs in a sentence reading task in which grammatically legal but infrequent sentence constructions had to be processed. A left-anterior negativity was observed whenever an explicit case marker
Jausovec, N; Jausovec, K
This study investigated differences in cognitive processes related to problem complexity. It was assumed that these differences would be reflected in respondents' EEG activity--spectral power and coherence. A second issue of the study was to compare differences between the lower (alpha(1) = 7.9-10.0 Hz), and upper alpha band (alpha(2) = 10.1-12.9 Hz). In the first experiment two well-defined problems with two levels of complexity were used. Only minor differences in EEG power and coherence measures related to problem complexity were observed. In the second experiment divergent production problems resembling tasks on creativity tests were compared with dialectic problems calling for creative solutions. Differences in EEG power measures were mainly related to the form of problem presentation (figural/verbal). In contrast, coherence was related to the level of creativity needed to solve a problem. Noticeable increased intra- and interhemispheric cooperation between mainly the far distant brain regions was observed in the EEG activity of respondents while solving the dialectic problems. These results are explained by the more intense involvement of the long cortico-cortical fiber system in creative thinking. Differences between the lower and upper alpha band were significant for the power and coherence measures. In Experiment 2, fewer differences were observed in power measures in the upper alpha band than in the lower alpha band. A reverse pattern was observed for the coherence measures. These results hint to a functional independence of the two alpha bands, however, they do not allow to draw firm conclusions about their functional meanings. The study showed that it is unlikely that individuals solve well- and ill-defined problems by employing similar cognitive strategies.
Sieu, Lim-Anna; Bergel, Antoine; Tiran, Elodie; Deffieux, Thomas; Pernot, Mathieu; Gennisson, Jean-Luc; Tanter, Mickaël; Cohen, Ivan
We developed an integrated experimental framework that extends the brain exploration capabilities of functional ultrasound imaging to awake and mobile rats. In addition to acquiring hemodynamic data, this method further allows parallel access to electroencephalography (EEG) recordings of neuronal activity. We illustrate this approach with two proofs of concept: a behavioral study on theta rhythm activation in a maze running task and a disease-related study on spontaneous epileptic seizures.
Leiser, Steven C; Dunlop, John; Bowlby, Mark R; Devilbiss, David M
Electroencephalography (EEG) and related methodologies offer the promise of predicting the likelihood that novel therapies and compounds will exhibit clinical efficacy early in preclinical development. These analyses, including quantitative EEG (e.g. brain mapping) and evoked/event-related potentials (EP/ERP), can provide a physiological endpoint that may be used to facilitate drug discovery, optimize lead or candidate compound selection, as well as afford patient stratification and Go/No-Go decisions in clinical trials. Currently, the degree to which these different methodologies hold promise for translatability between preclinical models and the clinic have not been well summarized. To address this need, we review well-established and emerging EEG analytic approaches that are currently being integrated into drug discovery programs throughout preclinical development and clinical research. Furthermore, we present the use of EEG in the drug development process in the context of a number of major central nervous system disorders including Alzheimer's disease, schizophrenia, depression, attention deficit hyperactivity disorder, and pain. Lastly, we discuss the requirements necessary to consider EEG technologies as a biomarker. Many of these analyses show considerable translatability between species and are used to predict clinical efficacy from preclinical data. Nonetheless, the next challenge faced is the selection and validation of EEG endpoints that provide a set of robust and translatable biomarkers bridging preclinical and clinical programs. 2010 Elsevier Inc. All rights reserved.
Dahal, Nabaraj; (Nanda Nandagopal, D.; Cocks, Bernadine; Vijayalakshmi, Ramasamy; Dasari, Naga; Gaertner, Paul
Objective. The objective of our current study was to look for the EEG correlates that can reveal the engaged state of the brain while undertaking cognitive tasks. Specifically, we aimed to identify EEG features that could detect audio distraction during simulated driving. Approach. Time varying autoregressive (TVAR) analysis using Kalman smoother was carried out on short time epochs of EEG data collected from participants as they undertook two simulated driving tasks. TVAR coefficients were then used to construct all pole model enabling the identification of EEG features that could differentiate normal driving from audio distracted driving. Main results. Pole analysis of the TVAR model led to the visualization of event related synchronization/desynchronization (ERS/ERD) patterns in the form of pole displacements in pole plots of the temporal EEG channels in the z plane enabling the differentiation of the two driving conditions. ERS in the EEG data has been demonstrated during audio distraction as an associated phenomenon. Significance. Visualizing the ERD/ERS phenomenon in terms of pole displacement is a novel approach. Although ERS/ERD has previously been demonstrated as reliable when applied to motor related tasks, it is believed to be the first time that it has been applied to investigate human cognitive phenomena such as attention and distraction. Results confirmed that distracted/non-distracted driving states can be identified using this approach supporting its applicability to cognition research.
Amin, Hafeez Ullah; Malik, Aamir Saeed; Ahmad, Rana Fayyaz; Badruddin, Nasreen; Kamel, Nidal; Hussain, Muhammad; Chooi, Weng-Tink
This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task--Raven's advance progressive metric test and (2) the EEG signals recorded in rest condition--eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53-3.06 and 3.06-6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.
Oliveira Filho, Florêncio Mendes; Leyva Cruz, Juan Alberto
In this paper we analyzed, by the FDFA root mean square fluctuation (rms) function, the motor/imaginary human activity produced by a 64-channel electroencephalography (EEG). We utilized the Physionet on-line databank, a publicly available database of human EEG signals, as a standardized reference database for this study. Herein, we report the use of detrended fluctuation analysis (DFA) method for EEG analysis. We show that the complex time series of the EEG exhibits characteristic fluctuations depending on the analyzed channel in the scalp-recorded EEG. In order to demonstrate the effectiveness of the proposed technique, we analyzed four distinct channels represented here by F332, F637 (frontal region of the head) and P349, P654 (parietal region of the head). We verified that the amplitude of the FDFA rms function is greater for the frontal channels than for the parietal. To tabulate this information in a better way, we define and calculate the difference between FDFA (in log scale) for the channels, thus defining a new path for analysis of EEG signals. Finally, related to the studied EEG signals, we obtain the auto-correlation exponent, αDFA by DFA method, that reveals self-affinity at specific time scale. Our results shows that this strategy can be applied to study the human brain activity in EEG processing. PMID:28910294
Chen, Yu-Chieh; Duann, Jeng-Ren; Chuang, Shang-Wen; Lin, Chun-Ling; Ko, Li-Wei; Jung, Tzyy-Ping; Lin, Chin-Teng
This study investigates motion-sickness-related brain responses using a VR-based driving simulator on a motion platform with six degrees of freedom, which provides both visual and vestibular stimulations to induce motion sickness in a manner that is close to that in daily life. Subjects' brain dynamics associated with motion sickness were measured using a 32-channel EEG system. Their degree of motion sickness was simultaneously and continuously reported using an onsite joystick, providing non-stop behavioral references to the recorded EEG changes. The acquired EEG signals were parsed by independent component analysis (ICA) into maximally independent processes. The decomposition enables the brain dynamics that are induced by the motion of the platform and motion sickness to be disassociated. Five MS-related brain processes with equivalent dipoles located in the left motor, the parietal, the right motor, the occipital and the occipital midline areas were consistently identified across all subjects. The parietal and motor components exhibited significant alpha power suppression in response to vestibular stimuli, while the occipital components exhibited MS-related power augmentation in mainly theta and delta bands; the occipital midline components exhibited a broadband power increase. Further, time series cross-correlation analysis was employed to evaluate relationships between the spectral changes associated with different brain processes and the degree of motion sickness. According to our results, it is suggested both visual and vestibular stimulations should be used to induce motion sickness in brain dynamic studies. Copyright (c) 2009 Elsevier Inc. All rights reserved.
Perronnet, Lorraine; Lécuyer, Anatole; Mano, Marsel; Bannier, Elise; Lotte, Fabien; Clerc, Maureen; Barillot, Christian
International audience; EEG-fMRI-neurofeedback(NF) has been introduced for the first time by Zotev et al . The authors hypothesized that bimodal EEG-fMRI-NF could be more efficient than unimodal EEG-NF or fMRI-NF performed alone. A recent study identified the fMRI signature of motor imagery during EEG-NF . However to our knowledge EEG-fMRI-NF, EEG-NF and fMRI-NF have never been compared before. In the present work, we propose an EEG-fMRI-NF protocol of a motor imagery (MI) task and comp...
Bilgin Topçuoğlu, Özgür; Kavas, Murat; Öztaş, Selahattin; Arınç, Sibel; Afşar, Gülgün; Saraç, Sema; Midi, İpek
Sarcoidosis is a multisystem granulomatous disease affecting nervous system in 5% to 10% of patients. Magnetic resonance imaging (MRI) is accepted as the most sensitive method for detecting neurosarcoidosis. However, the most common findings in MRI are the nonspecific white matter lesions, which may be unrelated to sarcoidosis and can occur because of hypertension, diabetes mellitus, smoking, and other inflammatory or infectious disorders, as well. Autopsy studies report more frequent neurological involvement than the ante mortem studies. The aim of this study is to assess electroencephalography (EEG) in sarcoidosis patients without neurological findings in order to display asymptomatic neurological dysfunction. We performed EEG on 30 sarcoidosis patients without diagnosis of neurosarcoidosis or prior neurological comorbidities. Fourteen patients (46.7%) showed intermittant focal and/or generalized slowings while awake and not mentally activated. Seven (50%) of these 14 patients with EEG slowings had nonspecific white matter changes while the other half showed EEG slowings in the absence of MRI changes. We conclude that EEG slowings, when normal variants (psychomotor variant, temporal theta of elderly, frontal theta waves) are eliminated, may be an indicator of dysfunction in brain activity even in the absence of MRI findings. Hence, EEG may contribute toward detecting asymptomatic neurological dysfunction or probable future neurological involvement in sarcoidosis patients. © EEG and Clinical Neuroscience Society (ECNS) 2016.
Li, Peng; Yan, Chang; Karmakar, Chandan; Liu, Changchun
It is an open-ended challenge to accurately detect the epileptic seizures through electroencephalogram (EEG) signals. Recently published studies have made elaborate attempts to distinguish between the normal and epileptic EEG signals by advanced nonlinear entropy methods, such as the approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with the conventional entropy methods for especially short length data. We thus aimed, in the present study, to show the potential of DistEn in the analysis of epileptic EEG signals. The publicly-accessible Bonn database which consisted of normal, interictal, and ictal EEG signals was used in this study. Three different measurement protocols were set for better understanding the performance of DistEn, which are: i) calculate the DistEn of a specific EEG signal using the full recording; ii) calculate the DistEn by averaging the results for all its possible non-overlapped 5 second segments; and iii) calculate it by averaging the DistEn values for all the possible non-overlapped segments of 1 second length, respectively. Results for all three protocols indicated a statistically significantly increased DistEn for the ictal class compared with both the normal and interictal classes. Besides, the results obtained under the third protocol, which only used very short segments (1 s) of EEG recordings showed a significantly (p entropy algorithm. The capability of discriminating between the normal and interictal EEG signals is of great clinical relevance since it may provide helpful tools for the detection of a seizure onset. Therefore, our study suggests that the DistEn analysis of EEG signals is very promising for clinical and even portable EEG monitoring.
Forrest Sheng Bao
Full Text Available Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.
Jackson, Alice F; Bolger, Donald J
A thorough understanding of the EEG signal and its measurement is necessary to produce high quality data and to draw accurate conclusions from those data. However, publications that discuss relevant topics are written for divergent audiences with specific levels of expertise: explanations are either at an abstract level that leaves readers with a fuzzy understanding of the electrophysiology involved, or are at a technical level that requires mastery of the relevant physics to understand. A clear, comprehensive review of the origin and measurement of EEG that bridges these high and low levels of explanation fills a critical gap in the literature and is necessary for promoting better research practices and peer review. The present paper addresses the neurophysiological source of EEG, propagation of the EEG signal, technical aspects of EEG measurement, and implications for interpretation of EEG data. Copyright © 2014 Society for Psychophysiological Research.
... Saunders; 2013:chap E. Hahn CD, Emerson RG. Electroencephalography and evoked potentials. In: Daroff RB, Jankovic J, Mazziotta JC, Pomeroy SL, eds. Bradley's Neurology in Clinical Practice . 7th ed. Philadelphia, PA: Elsevier; 2016:chap 34. Review Date 2/27/2016 Updated by: Amit M. ...
Sep 3, 2017 ... Epilepsia 2014; 55 (3):442-447. Doi: 10.1111/ epi.12531. 16. Ahmed MH, Obembe A. Electroencephalographic abnormalities in 351 Nigerians with epilepsy. West Afr J. Med. 1991; 10(3-4): 216-21. 17. Shrestha R, Pradhan SN, Sharma SC, Shakya KN,. Karki DB, Rana BB, et al. A study of of the first 350.
Bauer, Anna-Katharina R; Kreutz, Gunter; Herrmann, Christoph S
Every individual has a preferred musical tempo, which peaks slightly above 120 beats per minute and is subject to interindividual variation. The preferred tempo is believed to be associated with rhythmic body movements as well as motor cortex activity. However, a long-standing question is whether preferred tempo is determined biologically. To uncover the neural correlates of preferred tempo, we first determined an individual's preferred tempo using a multistep procedure. Subsequently, we correlated the preferred tempo with a general EEG timing parameter as well as perceptual and motor EEG correlates-namely, individual alpha frequency, auditory evoked gamma band response, and motor beta activity. Results showed a significant relation between preferred tempo and the frequency of motor beta activity. These findings suggest that individual tempo preferences result from neural activity in the motor cortex, explaining the interindividual variation. Copyright © 2014 Society for Psychophysiological Research.
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.
Mousikou, Petroula; Mahajan, Yatin; de Lissa, Peter; Thie, Johnson; McArthur, Genevieve
Background. Auditory event-related potentials (ERPs) have proved useful in investigating the role of auditory processing in cognitive disorders such as developmental dyslexia, specific language impairment (SLI), attention deficit hyperactivity disorder (ADHD), schizophrenia, and autism. However, laboratory recordings of auditory ERPs can be lengthy, uncomfortable, or threatening for some participants – particularly children. Recently, a commercial gaming electroencephalography (EEG) system has been developed that is portable, inexpensive, and easy to set up. In this study we tested if auditory ERPs measured using a gaming EEG system (Emotiv EPOC®, www.emotiv.com) were equivalent to those measured by a widely-used, laboratory-based, research EEG system (Neuroscan). Methods. We simultaneously recorded EEGs with the research and gaming EEG systems, whilst presenting 21 adults with 566 standard (1000 Hz) and 100 deviant (1200 Hz) tones under passive (non-attended) and active (attended) conditions. The onset of each tone was marked in the EEGs using a parallel port pulse (Neuroscan) or a stimulus-generated electrical pulse injected into the O1 and O2 channels (Emotiv EPOC®). These markers were used to calculate research and gaming EEG system late auditory ERPs (P1, N1, P2, N2, and P3 peaks) and the mismatch negativity (MMN) in active and passive listening conditions for each participant. Results. Analyses were restricted to frontal sites as these are most commonly reported in auditory ERP research. Intra-class correlations (ICCs) indicated that the morphology of the research and gaming EEG system late auditory ERP waveforms were similar across all participants, but that the research and gaming EEG system MMN waveforms were only similar for participants with non-noisy MMN waveforms (N = 11 out of 21). Peak amplitude and latency measures revealed no significant differences between the size or the timing of the auditory P1, N1, P2, N2, P3, and MMN peaks. Conclusions
Badcock, Nicholas A; Mousikou, Petroula; Mahajan, Yatin; de Lissa, Peter; Thie, Johnson; McArthur, Genevieve
Background. Auditory event-related potentials (ERPs) have proved useful in investigating the role of auditory processing in cognitive disorders such as developmental dyslexia, specific language impairment (SLI), attention deficit hyperactivity disorder (ADHD), schizophrenia, and autism. However, laboratory recordings of auditory ERPs can be lengthy, uncomfortable, or threatening for some participants - particularly children. Recently, a commercial gaming electroencephalography (EEG) system has been developed that is portable, inexpensive, and easy to set up. In this study we tested if auditory ERPs measured using a gaming EEG system (Emotiv EPOC(®), www.emotiv.com) were equivalent to those measured by a widely-used, laboratory-based, research EEG system (Neuroscan). Methods. We simultaneously recorded EEGs with the research and gaming EEG systems, whilst presenting 21 adults with 566 standard (1000 Hz) and 100 deviant (1200 Hz) tones under passive (non-attended) and active (attended) conditions. The onset of each tone was marked in the EEGs using a parallel port pulse (Neuroscan) or a stimulus-generated electrical pulse injected into the O1 and O2 channels (Emotiv EPOC(®)). These markers were used to calculate research and gaming EEG system late auditory ERPs (P1, N1, P2, N2, and P3 peaks) and the mismatch negativity (MMN) in active and passive listening conditions for each participant. Results. Analyses were restricted to frontal sites as these are most commonly reported in auditory ERP research. Intra-class correlations (ICCs) indicated that the morphology of the research and gaming EEG system late auditory ERP waveforms were similar across all participants, but that the research and gaming EEG system MMN waveforms were only similar for participants with non-noisy MMN waveforms (N = 11 out of 21). Peak amplitude and latency measures revealed no significant differences between the size or the timing of the auditory P1, N1, P2, N2, P3, and MMN peaks
Nicholas A. Badcock
Full Text Available Background. Auditory event-related potentials (ERPs have proved useful in investigating the role of auditory processing in cognitive disorders such as developmental dyslexia, specific language impairment (SLI, attention deficit hyperactivity disorder (ADHD, schizophrenia, and autism. However, laboratory recordings of auditory ERPs can be lengthy, uncomfortable, or threatening for some participants – particularly children. Recently, a commercial gaming electroencephalography (EEG system has been developed that is portable, inexpensive, and easy to set up. In this study we tested if auditory ERPs measured using a gaming EEG system (Emotiv EPOC®, www.emotiv.com were equivalent to those measured by a widely-used, laboratory-based, research EEG system (Neuroscan.Methods. We simultaneously recorded EEGs with the research and gaming EEG systems, whilst presenting 21 adults with 566 standard (1000 Hz and 100 deviant (1200 Hz tones under passive (non-attended and active (attended conditions. The onset of each tone was marked in the EEGs using a parallel port pulse (Neuroscan or a stimulus-generated electrical pulse injected into the O1 and O2 channels (Emotiv EPOC®. These markers were used to calculate research and gaming EEG system late auditory ERPs (P1, N1, P2, N2, and P3 peaks and the mismatch negativity (MMN in active and passive listening conditions for each participant.Results. Analyses were restricted to frontal sites as these are most commonly reported in auditory ERP research. Intra-class correlations (ICCs indicated that the morphology of the research and gaming EEG system late auditory ERP waveforms were similar across all participants, but that the research and gaming EEG system MMN waveforms were only similar for participants with non-noisy MMN waveforms (N = 11 out of 21. Peak amplitude and latency measures revealed no significant differences between the size or the timing of the auditory P1, N1, P2, N2, P3, and MMN peaks
Loibl, Helmut; Maslaton, Martin; Bredow, Hartwig von; Walter, Rene (eds.)
EEG 2012 is a complete revision for new EEG plants whereby the previous requirements of the EEG 2009 can be maintained for the existing plants. The authors of the book under consideration fully focus on the splitting into two different legal systems and the implications. It describes possibilities of solution for problems from the daily practice. The book provides a complete commentation of the biomass ordinance as well as the statements on the connection to the gas grid of biomethane plants.
Ulrich, G; Fürstenberg, U
Up until now, no subclassification of affective psychoses has been validated biologically. This follows unavoidably from a research practice of defining diagnostic subtypes in consensus conferences and only thereafter allowing their validation. There is evidence that electroencephalograms (EEG) may be a useful tool in psychiatry, provided that the relevant information is extracted. Our EEG quantification procedure aims at an assessment of both the amount and range of variation of spontaneous changes of topographical alpha-power distribution, developing within a certain period of recording under resting conditions. Our measures were designed to characterize the dynamic organization of the EEG. This is quite obviously an eyeball evaluation but it has nevertheless been neglected in research. The study design was done retrospectively. Included were inpatients with a primary depressive disorder. Main exclusion criteria were an age older than 62 years and psychotropic drugs other than antidepressants. The psychopathology and other clinical data were routinely assessed within three days after admission by the AMDP documentation. An EEG was also routinely performed at admission. We made use of robust, generally known non-parametric statistics. Those patients who exhibited a dynamically rigid EEG are especially prone to recurrences, have a relative late onset of their illness, and show an acute symptomatology characterized by organic-like features. The findings lend support to our contention that the quantitative assessment of the dynamics of the EEG-Gestalt allows the delimitation of a clinically important subtype that is characterized both cross-sectionally and in long-term respects.
Yasenkov, Roman; Deboer, Tom
Sleep is regulated by homeostatic and circadian processes. Slow wave activity (SWA; 1-4 Hz) in the NREM sleep electroencephalogram (EEG) reflects sleep homeostasis. Activity of faster EEG frequencies (10-25 Hz) is thought to be under influence of circadian factors. The relative contribution of both processes to the distribution of sleep and wakefulness and EEG activity in rodents remains uncertain. Continuous EEG recording in rats in constant dark conditions (DD) were performed and a sleep deprivation protocol consisting of 2 h sleep deprivation followed by 2 h of rest (2h/2h) was applied for 48 h to obtain a constant sleep pressure. Basic sleep research laboratory. Adult male Wistar rats. Sleep deprivation. Under the 2h/2h protocol, the circadian modulation of waking, NREM and REM sleep was markedly reduced compared to the baseline, affecting the frequency of vigilance state episodes and the duration of REM sleep and waking episodes. In contrast, NREM sleep episode duration still showed a daily modulation. Consecutive 2h values of SWA in NREM sleep were stabile during the 2h\\2h protocol, while NREM sleep EEG activity within the higher frequencies (7-25 Hz) still demonstrated strong circadian modulation, which did not differ from baseline. In rats, the daily modulation of REM sleep is less pronounced compared to NREM sleep and waking. In contrast to SWA, activity in higher frequencies (7-25 Hz) in the NREM sleep EEG have an endogenous circadian origin and are not influenced by sleep homeostatic mechanisms.
Smit, Dirk J A; Boomsma, Dorret I; Schnack, Hugo G; Hulshoff Pol, Hilleke E; de Geus, Eco J C
The human electroencephalogram (EEG) consists of oscillations that reflect the summation of postsynaptic potentials at the dendritic tree of cortical neurons. The strength of the oscillations (EEG power) is a highly genetic trait that has been related to individual differences in many phenotypes, including intelligence and liability for psychopathology. Here, we investigated whether brain anatomy underlies these EEG power differences by correlating it to gray and white matter volumes (GMV, WMV), and additionally investigated whether this association can be attributed to genes or environmental factors. EEG was measured in a sample of 405 young adult twins and their siblings, and power in the theta (~4 Hz), alpha (~10 Hz), and beta (~20 Hz) frequency bands determined. A subset of 121 subjects were also scanned in a 1.5 T MRI scanner, and gray and white matter volumes defined as the total of cortical and subcortical volumes, excluding cerebellum. Both MRI-based volumes and EEG power spectra were highly heritable. GMV and WMV correlated .25 to .29 with EEG power for the slower oscillations (theta, alpha). Moreover, these phenotypic correlations largely reflected genetic covariation, irrespective of oscillation frequency and volume type. Genetic correlations (.31 genetic sources of variation, which may reflect such processes as myelination, synaptic density, and dendritic outgrowth.
Thul, Alexander; Lechinger, Julia; Donis, Johann; Michitsch, Gabriele; Pichler, Gerald; Kochs, Eberhard F; Jordan, Denis; Ilg, Rüdiger; Schabus, Manuel
Clinical assessments that rely on behavioral responses to differentiate Disorders of Consciousness are at times inapt because of some patients' motor disabilities. To objectify patients' conditions of reduced consciousness the present study evaluated the use of electroencephalography to measure residual brain activity. We analyzed entropy values of 18 scalp EEG channels of 15 severely brain-damaged patients with clinically diagnosed Minimally-Conscious-State (MCS) or Unresponsive-Wakefulness-Syndrome (UWS) and compared the results to a sample of 24 control subjects. Permutation entropy (PeEn) and symbolic transfer entropy (STEn), reflecting information processes in the EEG, were calculated for all subjects. Participants were tested on a modified active own-name paradigm to identify correlates of active instruction following. PeEn showed reduced local information content in the EEG in patients, that was most pronounced in UWS. STEn analysis revealed altered directed information flow in the EEG of patients, indicating impaired feed-backward connectivity. Responses to auditory stimulation yielded differences in entropy measures, indicating reduced information processing in MCS and UWS. Local EEG information content and information flow are affected in Disorders of Consciousness. This suggests local cortical information capacity and feedback information transfer as neural correlates of consciousness. The utilized EEG entropy analyses were able to relate to patient groups with different Disorders of Consciousness. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
Horton, Cort; Srinivasan, Ramesh; D'Zmura, Michael
Recent studies have shown that auditory cortex better encodes the envelope of attended speech than that of unattended speech during multi-speaker ('cocktail party') situations. We investigated whether these differences were sufficiently robust within single-trial electroencephalographic (EEG) data to accurately determine where subjects attended. Additionally, we compared this measure to other established EEG markers of attention. High-resolution EEG was recorded while subjects engaged in a two-speaker 'cocktail party' task. Cortical responses to speech envelopes were extracted by cross-correlating the envelopes with each EEG channel. We also measured steady-state responses (elicited via high-frequency amplitude modulation of the speech) and alpha-band power, both of which have been sensitive to attention in previous studies. Using linear classifiers, we then examined how well each of these features could be used to predict the subjects' side of attention at various epoch lengths. We found that the attended speaker could be determined reliably from the envelope responses calculated from short periods of EEG, with accuracy improving as a function of sample length. Furthermore, envelope responses were far better indicators of attention than changes in either alpha power or steady-state responses. These results suggest that envelope-related signals recorded in EEG data can be used to form robust auditory BCI's that do not require artificial manipulation (e.g., amplitude modulation) of stimuli to function.
Zeng, Hong; Song, Aiguo
An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. The proposed approach first conducts the blind source separation on the raw EEG recording by the stationary subspace analysis (SSA) algorithm. Unlike the classic blind source separation algorithms, SSA is explicitly tailored to the understanding of distribution changes, where both the mean and the covariance matrix are taken into account. In addition, neither independency nor uncorrelation is required among the sources by SSA. Thereby, it can concentrate artifacts in fewer components than the representative blind source separation methods. Next, the components that are determined to be related to the ocular artifacts are projected back to be subtracted from EEG signals, producing the clean EEG data eventually. The experimental results on both the artificially contaminated EEG data and real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly nonstationary and the underlying sources cannot be assumed to be independent or uncorrelated.
Full Text Available An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. The proposed approach first conducts the blind source separation on the raw EEG recording by the stationary subspace analysis (SSA algorithm. Unlike the classic blind source separation algorithms, SSA is explicitly tailored to the understanding of distribution changes, where both the mean and the covariance matrix are taken into account. In addition, neither independency nor uncorrelation is required among the sources by SSA. Thereby, it can concentrate artifacts in fewer components than the representative blind source separation methods. Next, the components that are determined to be related to the ocular artifacts are projected back to be subtracted from EEG signals, producing the clean EEG data eventually. The experimental results on both the artificially contaminated EEG data and real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly nonstationary and the underlying sources cannot be assumed to be independent or uncorrelated.
Wang, Haowen; Qian, Zhiyu; Li, Hongjing; Chen, Chunxiao; Ding, Shangwen
In order to choose a fast and efficient real-time method in beta wave information extraction, we compared the result and the efficiency of the information separation of both fast Fourier transform (FFT) and wavelet transform of EEG beta band in the present paper. Our work provides the basis for the EEG data come from the real-time health assessment of 3DTV. We took the EEGs of 5 healthy volunteers before, after and during the process of watching 3DTV and meanwhile recorded the results. The trends of the relative energy and the time cost of two methods were compared by using both the FFT and wavelet packet transform (WPT) which was to extract the feature of EEG beta wave. It demonstrated that (1) Results of the two methods were consistent in the trends of watching 3DTV; (2) Results of the differences in two methods were consistent before and after watching 3DTV; (3) FFT took less time than the wavelet transform in the same case. It is concluded that the results of both FFT and Wavelet transform are consistent in feature extraction of EEG, and a fast method to work with the large quantities of EEG data obtained in the experiments can be offered in the future.
Jeong, J; Kim, D J; Kim, S Y; Chae, J H; Go, H J; Kim, K S
Sleep deprivation can affect the waking EEG that may reflect information processing of the brain. We examined the effect of total sleep deprivation (TSD) on nonlinear dynamics of the waking EEG. Paired-group design. A sleep disorders laboratory in a hospital. Twenty healthy male volunteers. Waking EEG data were recorded from subjects with eyes closed after (a) an 8-hour night's sleep and (b) TSD for 24 hours. The dimensional complexity (D2), as a nonlinear measure of complexity, of the EEG after a full night sleep were compared with those of the EEG after TSD. The sleep-deprived states had lower D2 values at three channels (P4, O2, and C3) than normal states. TSD results in the decrease of complexity in the brain, which may imply sub-optimal information processing of the cerebral cortex. We suggest that the investigation of the relation between nonlinear dynamics of the waking EEG induced by TSD and cognitive performance may offer fruitful clues for understanding the role of sleep and the effects of sleep deprivation on brain function.
Fatima Zahra Taoufiqi
Full Text Available Objective. This study aims to evaluate the incidence of pathological cerebral activity responses to intermittent rhythmic photic stimulation (IPS after a single epileptic seizure. Patients and Methods. One hundred and thirty-seven EEGs were performed at the Neurophysiology Department of Mohamed V Teaching Military Hospital in Rabat. Clinical and EEG data was collected. Results. 9.5% of our patients had photoparoxysmal discharges (PPD. Incidence was higher in males than in females, but p value was not significant (p=0.34, and it was higher in children compared to adults with significant p value (p=0.08. The most epileptogenic frequencies were within the range 15–20 Hz. 63 patients had an EEG after 72 hours; among them 11 were photosensitive (p=0.001. The frequency of the PPR was significantly higher in patients with generalized abnormalities than in focal abnormalities (p=0.001. EEG confirmed a genetic generalized epilepsy in 8 cases among 13 photosensitive patients. Conclusion. PPR is age related. The frequencies within the range 15–20 Hz should inevitably be included in EEG protocols. The presence of PPR after a first seizure is probably more in favor of generalized seizure rather than the other type of seizure. PPR seems independent from the delay Seizure-EEG. Our study did not show an association between sex and photosensitivity.
Miao, Tiejun; Oyama-Higa, Mayumi; Sato, Sadaka; Kojima, Junji; Lin, Juan; Reika, Sato
We studied a relation of chaotic dynamics of finger plethysmogram to complexity of high cerebral center in both theoretical and experimental approaches. We proposed a mathematical model to describe emergence of chaos in finger tip pulse wave, which gave a theoretical prediction indicating increased chaoticity in higher cerebral center leading to an increase of chaos dynamics in plethysmograms. We designed an experiment to observe scalp-EEG and finger plethysmogram using two mental tasks to validate the relationship. We found that scalp-EEG showed an increase of the largest Lyapunov exponents (LLE) during speaking certain voices. Topographical scalp map of LLE showed enhanced arise around occipital and right cerebral area. Whereas there was decreasing tendency during listening music, where LLE scalp map revealed a drop around center cerebral area. The same tendency was found for LLE obtained from finger plethysmograms as ones of EEG under either speaking or listening tasks. The experiment gave results that agreed well with the theoretical relation derived from our proposed model.
Fitzgibbon, S P; Powers, D M W; Pope, K J; Clark, C R
A study was performed to investigate and compare the relative performance of blind signal separation (BSS) algorithms at separating common types of contamination from EEG. The study develops a novel framework for investigating and comparing the relative performance of BSS algorithms that incorporates a realistic EEG simulation with a known mixture of known signals and an objective performance metric. The key finding is that although BSS is an effective and powerful tool for separating and removing contamination from EEG, the quality of the separation is highly dependant on the type of contamination, the degree of contamination, and the choice of BSS algorithm. BSS appears to be most effective at separating muscle and blink contamination and less effective at saccadic and tracking contamination. For all types of contamination, principal components analysis is a strong performer when the contamination is greater in amplitude than the brain signal whereas other algorithms such as second-order blind inference and Infomax are generally better for specific types of contamination of lower amplitude.
Schier, M A
The aim was to assess the suitability of EEG-based techniques to recording activity during a driving simulation task. To achieve this, an inexpensive driving simulator (comprising a steering wheel, pedals and gear shift) were made to function with a personal computer running 'Need for Speed' simulation software. Simulators of this type are both inexpensive and relatively realistic. The EEG was recorded from four sites on the scalp (P3, P4, F3, F4) for two laps during the driving task, and during a replay task. The driving task involved participants driving a vehicle on a simulated undulating, sealed surface circuit, without any other vehicles present. Two men were participants in this experiment. Power spectra were computed and integrated to produce values of relative alpha activity for each channel and recording epoch, a time-series of alpha activity during each recorded segment. Overall values for alpha activity indicated an increase for replay compared to driving, and also driving on lap 5 compared to driving on lap 2. The EEG changes are consistent with the notion of overall reduction of attention during the later laps and the replay task and indicate the potential of such measures for complex motor behaviour.
Full Text Available This proof-of-concept study investigated whether a time-frequency EEG approach could be used to examine vection (i.e., illusions of self-motion. In the main experiment, we compared the event-related spectral perturbation (ERSP data of 10 observers during and directly after repeated exposures to two different types of optic flow display (each was 35° wide by 29° high and provided 20 s of motion stimulation. Displays consisted of either a vection display (which simulated constant velocity forward self-motion in depth or a control display (a spatially scrambled version of the vection display. ERSP data were decomposed using time-frequency Principal Components Analysis (t-f PCA. We found an increase in 10 Hz alpha activity, peaking some 14 s after display motion commenced, which was positively associated with stronger vection ratings. This followed decreases in beta activity, and was also followed by a decrease in delta activity; these decreases in EEG amplitudes were negatively related to the intensity of the vection experience. After display motion ceased, a series of increases in the alpha band also correlated with vection intensity, and appear to reflect vection- and/or motion-aftereffects, as well as later cognitive preparation for reporting the strength of the vection experience. Overall, these findings provide support for the notion that EEG can be used to provide objective markers of changes in both vection status (i.e., vection/no vection and vection strength.
Haufe, Stefan; Treder, Matthias S.; Gugler, Manfred F.; Sagebaum, Max; Curio, Gabriel; Blankertz, Benjamin
Emergency braking assistance has the potential to prevent a large number of car crashes. State-of-the-art systems operate in two stages. Basic safety measures are adopted once external sensors indicate a potential upcoming crash. If further activity at the brake pedal is detected, the system automatically performs emergency braking. Here, we present the results of a driving simulator study indicating that the driver's intention to perform emergency braking can be detected based on muscle activation and cerebral activity prior to the behavioural response. Identical levels of predictive accuracy were attained using electroencephalography (EEG), which worked more quickly than electromyography (EMG), and using EMG, which worked more quickly than pedal dynamics. A simulated assistance system using EEG and EMG was found to detect emergency brakings 130 ms earlier than a system relying only on pedal responses. At 100 km h-1 driving speed, this amounts to reducing the braking distance by 3.66 m. This result motivates a neuroergonomic approach to driving assistance. Our EEG analysis yielded a characteristic event-related potential signature that comprised components related to the sensory registration of a critical traffic situation, mental evaluation of the sensory percept and motor preparation. While all these components should occur often during normal driving, we conjecture that it is their characteristic spatio-temporal superposition in emergency braking situations that leads to the considerable prediction performance we observed.
Full Text Available BACKGROUND: There is compelling evidence indicating that sleep plays a crucial role in the consolidation of new declarative, hippocampus-dependent memories. Given the increasing interest in the spatiotemporal relationships between cortical and hippocampal activity during sleep, this study aimed to shed more light on the basic features of human sleep in the hippocampus. METHODOLOGY/PRINCIPAL FINDINGS: We recorded intracerebral stereo-EEG directly from the hippocampus and neocortical sites in five epileptic patients undergoing presurgical evaluations. The time course of classical EEG frequency bands during the first three NREM-REM sleep cycles of the night was evaluated. We found that delta power shows, also in the hippocampus, the progressive decrease across sleep cycles, indicating that a form of homeostatic regulation of delta activity is present also in this subcortical structure. Hippocampal sleep was also characterized by: i a lower relative power in the slow oscillation range during NREM sleep compared to the scalp EEG; ii a flattening of the time course of the very low frequencies (up to 1 Hz across sleep cycles, with relatively high levels of power even during REM sleep; iii a decrease of power in the beta band during REM sleep, at odds with the typical increase of power in the cortical recordings. CONCLUSIONS/SIGNIFICANCE: Our data imply that cortical slow oscillation is attenuated in the hippocampal structures during NREM sleep. The most peculiar feature of hippocampal sleep is the increased synchronization of the EEG rhythms during REM periods. This state of resonance may have a supportive role for the processing/consolidation of memory.
Amantini, A; Fossi, S; Grippo, A; Innocenti, P; Amadori, A; Bucciardini, L; Cossu, C; Nardini, C; Scarpelli, S; Roma, V; Pinto, F
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.
Abend, Nicholas S; Massey, Shavonne L; Fitzgerald, Mark; Fung, France; Atkin, Natalie J; Xiao, Rui; Topjian, Alexis A
We evaluated interrater agreement of EEG interpretation in a cohort of critically ill children resuscitated after cardiac arrest using standardized EEG terminology. Four pediatric electroencephalographers scored 10-minute EEG segments from 72 consecutive children obtained 24 hours after return of circulation using the American Clinical Neurophysiology Society's (ACNS) Standardized Critical Care EEG terminology. The percent of perfect agreement and the kappa coefficient were calculated for each of the standardized EEG variables and a predetermined composite EEG background category. The overall background category (normal, slow-disorganized, discontinuous, or attenuated-featureless) had almost perfect agreement (kappa 0.89).The ACNS Standardized Critical Care EEG variables had agreement that was (1) almost perfect for the seizures variable (kappa 0.93), (2) substantial for the continuity (kappa 0.79), voltage (kappa 0.70), and sleep transient (kappa 0.65) variables, (3) moderate for the rhythmic or periodic patterns (kappa 0.55) and interictal epileptiform discharge (kappa 0.60) variables, and (4) fair for the predominant frequency (kappa 0.23) and symmetry (kappa 0.31) variables. Condensing variable options led to improved agreement for the continuity and voltage variables. These data support the use of the standardized terminology and the composite overall background category as a basis for standardized EEG interpretation for subsequent studies assessing EEG background for neuroprognostication after pediatric cardiac arrest.
Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R.
Objective. Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. Approach. To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels. Main results. The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (<35 Hz) frequency ranges. MBRLAF is computationally more demanding than RLAF, but highly effective in all EEG frequency ranges. Effectivity is determined by visual inspection, as well as root-mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average mean-squared-distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best. Significance. In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI. Highlights We present a new and reusable reference layer cap prototype for simultaneous EEG-fMRI We
Mideksa, Kidist Gebremariam; Anwar, Abdul Rauf; Stephani, Ulrich; Deuschl, Günther; Freitag, Christine M.; Siniatchkin, Michael
At the sensor level many aspects, such as spectral power, functional and effective connectivity as well as relative-power-ratio ratio (RPR) and spatial resolution have been comprehensively investigated through both electroencephalography (EEG) and magnetoencephalography (MEG). Despite this, differences between both modalities have not yet been systematically studied by direct comparison. It remains an open question as to whether the integration of EEG and MEG data would improve the information obtained from the above mentioned parameters. Here, EEG (64-channel system) and MEG (275 sensor system) were recorded simultaneously in conditions with eyes open (EO) and eyes closed (EC) in 29 healthy adults. Spectral power, functional and effective connectivity, RPR, and spatial resolution were analyzed at five different frequency bands (delta, theta, alpha, beta and gamma). Networks of functional and effective connectivity were described using a spatial filter approach called the dynamic imaging of coherent sources (DICS) followed by the renormalized partial directed coherence (RPDC). Absolute mean power at the sensor level was significantly higher in EEG than in MEG data in both EO and EC conditions. At the source level, there was a trend towards a better performance of the combined EEG+MEG analysis compared with separate EEG or MEG analyses for the source mean power, functional correlation, effective connectivity for both EO and EC. The network of coherent sources and the spatial resolution were similar for both the EEG and MEG data if they were analyzed separately. Results indicate that the combined approach has several advantages over the separate analyses of both EEG and MEG. Moreover, by a direct comparison of EEG and MEG, EEG was characterized by significantly higher values in all measured parameters in both sensor and source level. All the above conclusions are specific to the resting state task and the specific analysis used in this study to have general
Full Text Available There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform and a parametric, signal modeling technique (ARMA are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.
de Barros, J Acacio; de Mendonça, J P R F; Suppes, P
Recent works on the relationship between the electro-encephalogram (EEG) data and psychological stimuli show that EEG recordings can be used to recognize an auditory stimulus presented to a subject. The recognition rate is, however, strongly affected by technical and physiological artifacts. In this work, subjects were presented seven auditory simuli in the form of English words (first, second, third, left, right, yes, and no), and the time-locked electric field was recorded with a 64 channel Neuroscan EEG system. We used the surface Laplacian operator to eliminate artifacts due to sources located at regions far from the electrode. Our intent with the Laplacian was to improve the recognition rates of auditory stimuli from the electric field. To compute the Laplacian, we used a spline interpolation from spherical harmonics. The EEG Laplacian of the electric field were average over trials for the same auditory stimulus, and with those averages we constructed prototypes and test samples. In addition to the Lapla...
Duun-Henriksen, Jonas; Kjaer, Troels Wesenberg; Madsen, Rasmus Elsborg
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...
Derya Ubeyli, Elif
This paper presented the usage of statistics over the set of the features representing the electroencephalogram (EEG) signals. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents, wavelet coefficients and the power levels of power spectral density (PSD) values obtained by eigenvector methods of the EEG signals were used as inputs of the MLPNN trained with Levenberg-Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.
J Gordon Millichap
Full Text Available Th value of amplitude-integrated electroencephalography (aEEG in the newborn is explored by researchers at Washington University, St Louis; Wilhelmina Children’s Hospital, Utrecht, Netherlands; and Uppsala University Hospital, Sweden.
The monitoring of sleep patterns without patient’s inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording the electroencephalogram (ear-EEG). The selected features for sleep pattern classification from a single ear-EEG channel include the spectral edge frequency and multi-scale fuzzy...
Hu, Xin; Yu, Jianwen; Song, Mengdi; Yu, Chun; Wang, Fei; Sun, Pei; Wang, Daifa; Zhang, Dan
Compared with the well documented neurophysiological findings on negative emotions, much less is known about positive emotions. In the present study, we explored the EEG correlates of ten different positive emotions (joy, gratitude, serenity, interest, hope, pride, amusement, inspiration, awe, and love). A group of 20 participants were invited to watch 30 short film clips with their EEGs simultaneously recorded. Distinct topographical patterns for different positive emotions were found for th...
A. Stancak; P. Sovka; J. Stastny
The contribution describes the design, optimization and verification of the off-line single-trial movement classification system. Four types of movements are used for the classification: the right index finger extension vs. flexion as well as the right shoulder (proximal) vs. right index finger (distal) movement. The classification system utilizes hidden information stored in the characteristic shapes of human brain activity (EEG signal). The great variability of EEG potentials requires using...
Full Text Available Brain computer interface (BCI is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG signals, while it remains unclear whether noninvasive electroencephalography (EEG signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA. These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26% in all subjects (p<0.05. The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.
Gong, Jinnan; Luo, Cheng; Chang, Xuebin; Zhang, Rui; Klugah-Brown, Benjamin; Guo, Lanjin; Xu, Peng; Yao, Dezhong
The rhythm of electroencephalogram (EEG) depends on the neuroanatomical-based parameters such as white matter (WM) connectivity. However, the impacts of these parameters on the specific characteristics of EEG have not been clearly understood. Previous studies demonstrated that, these parameters contribute the inter-subject differences of EEG during performance of specific task such as motor imagery (MI). Though researchers have worked on this phenomenon, the idea is yet to be understood in terms of the mechanism that underlies such differences. Here, to tackle this issue, we began our investigations by first examining the structural features related to scalp EEG characteristics, which are event-related desynchronizations (ERDs), during MI using diffusion MRI. Twenty-four right-handed subjects were recruited to accomplish MI tasks and MRI scans. Based on the high spatial resolution of the structural and diffusion images, the motor-related WM links, such as basal ganglia (BG)-primary somatosensory cortex (SM1) pathway and supplementary motor area (SMA)-SM1 connection, were reconstructed by using probabilistic white matter tractography. Subsequently, the relationships of WM characteristics with EEG signals were investigated. These analyses demonstrated that WM pathway characteristics, including the connectivity strength and the positional characteristics of WM connectivity on SM1 (defined by the gyrus-sulcus ratio of connectivity, GSR), have a significant impact on ERDs when doing MI. Interestingly, the high GSR of WM connections between SM1 and BG were linked to the better ERDs. These results therefore, indicated that the connectivity in the gyrus of SM1 interacted with MI network which played the critical role for the scalp EEG signal extraction of MI to a great extent. The study provided the coupling mechanism between structural and dynamic physiological features of human brain, which would also contribute to understanding individual differences of EEG in MI
Toth, A; Balatoni, B; Hajnik, T; Detari, L
Orexin A and orexin B are neuropeptides produced by a group of neurons located in the lateral hypothalamus which send widespread projections virtually to the whole neuraxis. Several studies indicated that orexins play a crucial role in the sleep-wake regulation and in the pathomechanism of the sleep disorder narcolepsy. As no data are available related to the EEG effects of orexin A in healthy, freely moving rats, the aim of the present experiments was to analyze EEG power changes in the generally used frequency bands after intracerebroventricular orexin A administration.Orexin A administration (0.84 and 2.8 nM/rat) differently affected fronto-occipital EEG waves in the different frequency bands recorded for 24 hours. Delta (1-4 Hz) and alpha (10-16 Hz) power decreased, while theta (4-10 Hz) and beta (16-48 Hz) power increased. Decrease of the delta power was followed by a rebound in case of the higher orexin A dose. This complex picture might be explained by the activation of several systems by the orexin A administration. Among these systems, cortical and thalamic circuits as well as the role of the neurons containing corticotrophin-releasing factor might be of significant importance.
Lopez-Gordo, M A; Grima Murcia, M D; Padilla, Pablo; Pelayo, F; Fernandez, E
Clinical processing of event-related potentials (ERPs) requires a precise synchrony between the stimulation and the acquisition units that are guaranteed by means of a physical link between them. This precise synchrony is needed since temporal misalignments during trial averaging can lead to high deviations of peak times, thus causing error in diagnosis or inefficiency in classification in brain-computer interfaces (BCIs). Out of the laboratory, mobile EEG systems and BCI headsets are not provided with the physical link, thus being inadequate for acquisition of ERPs. In this study, we propose a method for the asynchronous detection of trials onset from raw EEG without physical links. We validate it with a BCI application based on the dichotic listening task. The user goal was to attend the cued auditory message and to report three keywords contained in it while ignoring the other message. The BCI goal was to detect the attended message from the analysis of auditory ERPs. The rate of successful onset detection in both synchronous (using the real onset) and asynchronous (blind detection of trial onset from raw EEG) was 73% with a synchronization error of less than 1[Formula: see text]ms. The level of synchronization provided by this proposal would allow home-based acquisition of ERPs with low cost BCI headsets and any media player unit without physical links between them.
Uusberg, Andero; Thiruchselvam, Ravi; Gross, James J
Distraction is a powerful and widely-used emotion regulation strategy. Although distraction regulates emotion sooner than other cognitive strategies (Thiruchselvam, Blechert, Sheppes, Rydstrom, & Gross, 2011), it is not yet clear whether it is capable of blocking the earliest stages of emotion generation. To address this issue, we capitalized on the excellent temporal resolution of EEG by focusing on occipital theta dynamics which were associated with distinct stages of visual processing of emotional stimuli. Individually defined theta band dynamics were extracted from a previously published EEG dataset (Thiruchselvam et al., 2011) in which participants attended to unpleasant (and neutral) images or regulated emotion using distraction and reappraisal. Results revealed two peaks within early theta power increase, both of which were increased by emotional stimuli. Distraction did not affect theta power during an early peak (150-350 ms), but did successfully decrease activity in a second peak (350-550 ms). These results suggest that although distraction acts relatively early in the emotion-generative trajectory, it does not block fast detection of emotional significance. Given that theta dynamics were uncorrelated with Late Positive Potential activity, the present results also encourage researchers to add the occipital theta to the growing toolkit of EEG-based measures of emotion regulation. Copyright © 2014 Elsevier B.V. All rights reserved.
Ng, Kwun Kei; Penney, Trevor B
Humans, and other animals, are able to easily learn the durations of events and the temporal relationships among them in spite of the absence of a dedicated sensory organ for time. This chapter summarizes the investigation of timing and time perception using scalp-recorded electroencephalography (EEG), a non-invasive technique that measures brain electrical potentials on a millisecond time scale. Over the past several decades, much has been learned about interval timing through the examination of the characteristic features of averaged EEG signals (i.e., event-related potentials, ERPs) elicited in timing paradigms. For example, the mismatch negativity (MMN) and omission potential (OP) have been used to study implicit and explicit timing, respectively, the P300 has been used to investigate temporal memory updating, and the contingent negative variation (CNV) has been used as an index of temporal decision making. In sum, EEG measures provide biomarkers of temporal processing that allow researchers to probe the cognitive and neural substrates underlying time perception.
Bocharov, Andrey V; Knyazev, Gennady G; Savostyanov, Alexander N
Depression is one of the most prevalent mental illnesses and is associated with changes in emotion processing. The aim of this study was to determine the influence of depressive symptoms on EEG oscillatory dynamics accompanying implicit processing of angry and happy facial expressions in 46 healthy subjects. The Beck Depression Inventory was used to assess the presence of depressive symptoms in normal subjects. During the experiment, they were told to categorize the gender of angry, neutral, or happy faces presented to them, while high-resolution EEG was recorded. Analysis of the event-related spectral perturbations and the analysis of dipoles were carried out on EEG recordings using the EEGLAB toolbox. High depression (HD) and low depression (LD) groups did not differ on error rate and reaction time during categorization of gender. The perception of happy faces was accompanied by higher theta synchronization in the LD than the HD group. In contrast, theta synchronization was higher in the HD than the LD group during perception of angry faces. These findings imply that even at preclinical stages, HD scorers evidence increased emotional arousal to negative and decreased emotional arousal to positive stimuli during implicit emotion processing. Copyright © 2017 Elsevier Masson SAS. All rights reserved.
MARINA eDE TOMMASO
Full Text Available In previous studies migraine patients showed some abnormalities of pain related evoked responses, as reduced habituation to repetitive stimulation. In this study we aimed to apply a novel analysis of EEG bands synchronization and directed dynamical influences under painful stimuli in migraine patients compared to non migraine healthy volunteers. Thirty-one migraine without aura outpatients (MIGR were evaluated and compared to 19 controls (CONT. The right hand was stimulated by means of 30 consecutive CO2 laser stimuli. EEG signal was examined by means of Morlet wavelet, synchronization entropy and Granger causality, and the statistic results embedded into a scalp model. The vertex complex of averaged laser evoked responses (LEPs showed reduced habituation compared to controls. In the pre-stimulus phase enhanced synchronization entropy in the 0, 5-30 Hz range was present in MIGR and CONT between the bilateral temporal parietal and the frontal regions around the midline. Migraine patients showed an anticipation of EEG changes preceding the painful stimulation compared to controls. In the post-stimulus phase, the same cortical areas were more connected in MIGR vs CONT. In the totality of patients and controls, the habituation index was negatively correlated with the Granger Causality scores. A different pattern of cortical activation after painful stimulation was present in migraine. The increase in cortical connections during repetitive painful stimulation may subtend the phenomenon of LEPs reduced habituation. Brain network analysis may give an aid in understanding subtle changes of pain processing under laser stimuli in migraine patients.
Dong, Yue; Raif, Kaan E; Determan, Sarah C; Gai, Yan
Decoding spatial attention based on brain signals has wide applications in brain-computer interface (BCI). Previous BCI systems mostly relied on visual patterns or auditory stimulation (e.g., loudspeakers) to evoke synchronous brain signals. There would be difficulties to cover a large range of spatial locations with such a stimulation protocol. The present study explored the possibility of using virtual acoustic space and a visual-auditory matching paradigm to overcome this issue. The technique has the flexibility of generating sound stimulation from virtually any spatial location. Brain signals of eight human subjects were obtained with a 32-channel Electroencephalogram (EEG). Two amplitude-modulated noise or speech sentences carrying distinct spatial information were presented concurrently. Each sound source was tagged with a unique modulation phase so that the phase of the recorded EEG signals indicated the sound being attended to. The phase-tagged sound was further filtered with head-related transfer functions to create the sense of virtual space. Subjects were required to pay attention to the sound source that best matched the location of a visual target. For all the subjects, the phase of a single sound could be accurately reflected over the majority of electrodes based on EEG responses of 90 s or less. The electrodes providing significant decoding performance on auditory attention were fewer and may require longer EEG responses. The reliability and efficiency of decoding with a single electrode varied with subjects. Overall, the virtual acoustic space protocol has the potential of being used in practical BCI systems. © 2017 Saint Louis University. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society.
Tarokh, L; Carskadon, M A; Achermann, P
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 cross-sectional cohort (adults) were analyzed. The children and teen cohorts were 9/10 and 15/16 years at the initial assessment; ages of the adults were 20 to 23 years. Intrahemispheric, interhemispheric, and diagonal coherence was measured between all six possible pairings of two central (C3/A2 and C4/A1) and two occipital (O2/A1 and O1/A2) derivations during slow wave, stage 2, and, REM sleep. Within-subjects analyses were performed for the children and teen cohorts, and a linear regression analysis was performed across every assessment of all cohorts. Within-subject analyses revealed a maturational increase in coherence for both age cohorts, though the frequencies, sleep states, and regions differed between cohorts. Regression analysis across all age cohorts showed an overall linear increase in left and right intrahemispheric coherence for all sleep states across frequencies. Furthermore, coherence between diagonal electrode pairs also increased in a linear manner for stage 2 and REM sleep. No age-related trend was found in interhemispheric coherence. Our results indicate that sleep EEG coherence increases with age and that these increases are confined to specific brain regions. This analysis highlights the utility of the sleep EEG to measure developmental changes in brain maturation. Copyright © 2010 IBRO. Published by Elsevier Ltd. All rights reserved.
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
Classify rhythmic EEG patterns in extremely preterm infants and relate these to brain injury and outcome. Retrospective analysis of 77 infants born position. No relation was found between the median total duration of each pattern and injury on cUS and MRI or cognition at 2 and 5 years. Clear ictal discharges are rare in extremely preterm infants. PEDs are common but their significance is unclear. Rhythmic 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.
Full Text Available In humans, theta band (5-7 Hz power typically increases when performing cognitively demanding working memory (WM tasks, and simultaneous EEG-fMRI recordings have revealed an inverse relationship between theta power and the BOLD (blood oxygen level dependent signal in the default mode network during WM. However, synchronization also plays a fundamental role in cognitive processing, and the level of theta and higher frequency band synchronization is modulated during WM. Yet, little is known about the link between BOLD, EEG power, and EEG synchronization during WM, and how these measures develop with human brain maturation or relate to behavioral changes. We examined EEG-BOLD signal correlations from 18 young adults and 15 school-aged children for age-dependent effects during a load-modulated Sternberg WM task. Frontal load (in-dependent EEG theta power was significantly enhanced in children compared to adults, while adults showed stronger fMRI load effects. Children demonstrated a stronger negative correlation between global theta power and the BOLD signal in the default mode network relative to adults. Therefore, we conclude that theta power mediates the suppression of a task-irrelevant network. We further conclude that children suppress this network even more than adults, probably from an increased level of task-preparedness to compensate for not fully mature cognitive functions, reflected in lower response accuracy and increased reaction time. In contrast to power, correlations between instantaneous theta global field synchronization and the BOLD signal were exclusively positive in both age groups but only significant in adults in the frontal-parietal and posterior cingulate cortices. Furthermore, theta synchronization was weaker in children and was--in contrast to EEG power--positively correlated with response accuracy in both age groups. In summary we conclude that theta EEG-BOLD signal correlations differ between spectral power and
Gonzalez, Jania; Ding, Lei
Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (pEEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies. PMID:24416360
Jäncke, Lutz; Alahmadi, Nsreen
In this study, the neurophysiological underpinnings of learning disabilities (LD) in children are examined using resting state EEG. We were particularly interested in the neurophysiological differences between children with learning disabilities not otherwise specified (LD-NOS), learning disabilities with verbal disabilities (LD-Verbal), and healthy control (HC) children. We applied 2 different approaches to examine the differences between the different groups. First, we calculated theta/beta and theta/alpha ratios in order to quantify the relationship between slow and fast EEG oscillations. Second, we used a recently developed method for analyzing spectral EEG, namely the group independent component analysis (gICA) model. Using these measures, we identified substantial differences between LD and HC children and between LD-NOS and LD-Verbal children in terms of their spectral EEG profiles. We obtained the following findings: (a) theta/beta and theta/alpha ratios were substantially larger in LD than in HC children, with no difference between LD-NOS and LD-Verbal children; (b) there was substantial slowing of EEG oscillations, especially for gICs located in frontal scalp positions, with LD-NOS children demonstrating the strongest slowing; (c) the estimated intracortical sources of these gICs were mostly located in brain areas involved in the control of executive functions, attention, planning, and language; and (d) the LD-Verbal children demonstrated substantial differences in EEG oscillations compared with LD-NOS children, and these differences were localized in language-related brain areas. The general pattern of atypical neurophysiological activation found in LD children suggests that they suffer from neurophysiological dysfunction in brain areas involved with the control of attention, executive functions, planning, and language functions. LD-Verbal children also demonstrate atypical activation, especially in language-related brain areas. These atypical
de Vries, Nathalie K. S.; ter Horst, Hendrik J.; Bos, Arend F.
Amplitude-integrated electroencephalograms (aEEGs) recorded by cerebral function monitors (CFMs) are used increasingly to monitor the cerebral activity of newborn infants with encephalopathy. Recently, new CFM devices became available which also reveal the original EEG signals from the same leads.
Bai, Yu; Bai, Jia-Ming; Li, Jing; Li, Min; Yu, Ran; Pan, Qun-Wan
The purpose of the present study is to analyze the relationship between the telemetry electroencephalogram (EEG) changes of the prelimbic (PL) cortex and the drug-seeking behavior of morphine-induced conditioned place preference (CPP) rats by using the wavelet packet extraction and entropy measurement. The recording electrode was stereotactically implanted into the PL cortex of rats. The animals were then divided randomly into operation-only control and morphine-induced CPP groups, respectively. A CPP video system in combination with an EEG wireless telemetry device was used for recording EEG of PL cortex when the rats shuttled between black-white or white-black chambers. The telemetry recorded EEGs were analyzed by wavelet packet extraction, Welch power spectrum estimate, normalized amplitude and Shannon entropy algorithm. The results showed that, compared with operation-only control group, the left PL cortex's EEG of morphine-induced CPP group during black-white chamber shuttling exhibited the following changes: (1) the amplitude of average EEG for each frequency bands extracted by wavelet packet was reduced; (2) the Welch power intensity was increased significantly in 10-50 Hz EEG band (P EEG (P EEG changes in morphine-induced CPP group rat may be related to animals' drug-seeking motivation and behavior launching.
Schellenberger Costa, Michael; Weigenand, Arne; Ngo, Hong-Viet V; Marshall, Lisa; Born, Jan; Martinetz, Thomas; Claussen, Jens Christian
Few models exist that accurately reproduce the complex rhythms of the thalamocortical system that are apparent in measured scalp EEG and at the same time, are suitable for large-scale simulations of brain activity. Here, we present a neural mass model of the thalamocortical system during natural non-REM sleep, which is able to generate fast sleep spindles (12-15 Hz), slow oscillations (EEG-data from a recent sleep study in humans, where closed-loop auditory stimulation was applied. The model output relates directly to the EEG, which makes it a useful basis to develop new stimulation protocols.
Diego, Miguel A; Jones, Nancy Aaron; Field, Tiffany
EEGs were examined in data collected from 348 1-week, 1-month and 3-month-old infants of depressed and non-depressed mothers across several studies. Both the percentage of infants exhibiting spectral peaks and the frequency in Hz at which those peaks were exhibited increased with age. Consistent with previous studies, infants of depressed mothers exhibited greater left frontal EEG power, suggesting greater relative right frontal EEG activity than infants of non-depressed mothers. This profile was apparent across a narrow frequency range, which shifted from 3-9Hz at 1 week of age to 4-9Hz by 3 months of age.
Gasser, T; Verleger, R; Bächer, P; Sroka, L
Development in quantitative EEG parameters is studied for a sample of 158 normal children and adolescents aged 6-17 years. This is of interest both for increasing basic knowledge of human neurophysiology and for obtaining age standardized norms, useful in clinical research and applications. After selecting an appropriate epoch and correcting for EOG artifacts, the EEG at 8 derivations was submitted to spectral analysis in order to extract broad-band parameters in absolute and relative power. Change in EEG band power across age was quantified by polynomial regression analysis. This opened automatically the possibility to obtain age-standardized EEG norms. Development was for most EEG parameters non-linear, with more pronounced changes for absolute than for relative power. No sex differences and no pubertal spurt could be identified in contrast to most somatic quantities. A detailed statistical analysis revealed, however, that this might be due to using cross-sectional data. All bands except for alpha 2 decreased in absolute power, whereas the fast bands increased and the slow bands decreased in relative power. Strong evidence was found for a substituting process between theta activity and fast alpha activity.
Full Text Available Previous studies have shown that the resting electroencephalogram (EEG alpha patterns of nonclinical participants who score high on measures of negative affect, such as depression and shyness, are different from those who score low. However, we know relatively little about patterns of resting EEG alpha patterns in a nonclinical sample of individuals with high levels of obsessive-compulsive behaviors indicative of OCD. Here we measured resting EEG alpha activity in frontal and parietal regions of nonclinical participants who scored high and low on the Padua-R, a measure of the severity of OCD-related behaviors. We found that participants who scored high on the Padua-R exhibited decreased overall activity in frontal regions relative to individuals who scored low on the measure. We speculate that frontal hypoactivity may be a possible marker and/or index of risk for OCD.
Frenz, Walter (ed.) [Rheinisch-Westfaelische Technische Hochschule Aachen (Germany). Berg-, Umwelt- und Europarecht
Berlin commentary EEG II: safe through the paraphernalia Like hardly any other law, the Renewable Energies Act (EEG) is subject to constant changes. With the 2014 amendment, the EEG was fundamentally redesigned. This makes the application of the complex rules a challenge even for experts. In addition, the sub-rules contain important statements in the form of numerous annexes and regulations - with the EEG amendment 2014, this has become even more detailed. In it, many calculations are only defined in detail and the legal provisions of the EEG are made more definite and supplemented. The Berlin commentary EEG II accompanies you expertly through this complex matter. Experts explain the widely divergent rules in practice. If necessary for a better understanding, the provisions of the EEG 2014 are also explained. Consistently designed for your practice As a buyer of the work, you also benefit from access to an extensive, regularly updated database. This contains important legal energy regulations of the EU, the federal government and the countries. Even earlier legal positions remain searchable and can be conveniently compared with current versions. So you can see at a glance what has changed. [German] Berliner Kommentar EEG II: sicher durch den Paragrafengeflecht Wie kaum ein anderes Gesetz ist das Erneuerbare-Energien-Gesetz (EEG) staendigen Aenderungen unterworfen. Mit der Novelle 2014 wurde das EEG grundlegend umgestaltet. Dies macht die Anwendung der komplexen Regeln selbst fuer Experten zu einer Herausforderung. Zudem enthaelt auch das untergesetzliche Regelwerk wichtige Aussagen in Form zahlreicher Anlagen und Verordnungen - mit der EEG-Novelle 2014 ist dieses noch ausfuehrlicher geworden. In ihm werden viele Berechnungen erst naeher festgelegt und gesetzliche Bestimmungen des EEG entscheidend konkretisiert und ergaenzt. Der Berliner Kommentar EEG II begleitet Sie fachkundig durch diese komplexe Materie. Experten erlaeutern Ihnen praxisorientiert die
Full Text Available Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability, arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18 and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40% as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing neither achieved the cross-day classification (the accuracy was around chance level nor replicated the encouraging improvement due to the inter-day EEG variability. This result
Lin, Yuan-Pin; Jao, Ping-Keng; Yang, Yi-Hsuan
Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG) signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability), arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA)-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness) using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S) enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18) and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40%) as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing) neither achieved the cross-day classification (the accuracy was around chance level) nor replicated the encouraging improvement due to the inter-day EEG variability. This result demonstrated the
Lin, Yuan-Pin; Jao, Ping-Keng; Yang, Yi-Hsuan
Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG) signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability), arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA)-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness) using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S) enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18) and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40%) as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing) neither achieved the cross-day classification (the accuracy was around chance level) nor replicated the encouraging improvement due to the inter-day EEG variability. This result demonstrated the
Samantha J Broyd
Full Text Available BACKGROUND: The default-mode network (DMN is characterised by coherent very low frequency (VLF brain oscillations. The cognitive significance of this VLF profile remains unclear, partly because of the temporally constrained nature of the blood oxygen-level dependent (BOLD signal. Previously we have identified a VLF EEG network of scalp locations that shares many features of the DMN. Here we explore the intracranial sources of VLF EEG and examine their overlap with the DMN in adults with high and low ADHD ratings. METHODOLOGY/PRINCIPAL FINDINGS: DC-EEG was recorded using an equidistant 66 channel electrode montage in 25 adult participants with high- and 25 participants with low-ratings of ADHD symptoms during a rest condition and an attention demanding Eriksen task. VLF EEG power was calculated in the VLF band (0.02 to 0.2 Hz for the rest and task condition and compared for high and low ADHD participants. sLORETA was used to identify brain sources associated with the attention-induced deactivation of VLF EEG power, and to examine these sources in relation to ADHD symptoms. There was significant deactivation of VLF EEG power between the rest and task condition for the whole sample. Using s-LORETA the sources of this deactivation were localised to medial prefrontal regions, posterior cingulate cortex/precuneus and temporal regions. However, deactivation sources were different for high and low ADHD groups: In the low ADHD group attention-induced VLF EEG deactivation was most significant in medial prefrontal regions while for the high ADHD group this deactivation was predominantly localised to the temporal lobes. CONCLUSIONS/SIGNIFICANCE: Attention-induced VLF EEG deactivations have intracranial sources that appear to overlap with those of the DMN. Furthermore, these seem to be related to ADHD symptom status, with high ADHD adults failing to significantly deactivate medial prefrontal regions while at the same time showing significant attenuation of
Vollebregt, Madelon A.; van Dongen-Boomsma, Martine; Buitelaar, Jan K.; Slaats-Willemse, Dorine
Background: The number of placebo-controlled randomized studies relating to EEG-neurofeedback and its effect on neurocognition in attention-deficient/hyperactivity disorder (ADHD) is limited. For this reason, a double blind, randomized, placebo-controlled study was designed to assess the effects of EEG-neurofeedback on neurocognitive functioning…
Dijk, Derk Jan; Beersma, Domien G.M.; Daan, Serge; Bloem, Gerda M.; Hoofdakker, Rutger H. van den
The relation between EEG power density during slow wave sleep (SWS) deprivation and power density during subsequent sleep was investigated. Nine young male adults slept in the laboratory for 3 consecutive nights. Spectral analysis of the EEG on the 2nd (baseline) night revealed an exponential
Maes, J; Verbraecken, J; Willemen, M; De Volder, I; van Gastel, A; Michiels, N; Verbeek, I; Vandekerckhove, M; Wuyts, J; Haex, B; Willemen, T; Exadaktylos, V; Bulckaert, A; Cluydts, R
Misperception of Sleep Onset Latency, often found in Primary Insomnia, has been cited to be influenced by hyperarousal, reflected in EEG- and ECG-related indices. The aim of this retrospective study was to examine the association between Central Nervous System (i.e. EEG) and Autonomic Nervous System activity in the Sleep Onset Period and the first NREM sleep cycle in Primary Insomnia (n=17) and healthy controls (n=11). Furthermore, the study examined the influence of elevated EEG and Autonomic Nervous System activity on Stage2 sleep-protective mechanisms (K-complexes and sleep spindles). Confirming previous findings, the Primary Insomnia-group overestimated Sleep Onset Latency and this overestimation was correlated with elevated EEG activity. A higher amount of beta EEG activity during the Sleep Onset Period was correlated with the appearance of K-complexes immediately followed by a sleep spindle in the Primary Insomnia-group. This can be interpreted as an extra attempt to protect sleep continuity or as a failure of the sleep-protective role of the K-complex by fast EEG frequencies following within one second. The strong association found between K-alpha (K-complex within one second followed by 8-12 Hz EEG activity) in Stage2 sleep and a lower parasympathetic Autonomic Nervous System dominance (less high frequency HR) in Slow-wave sleep, further assumes a state of hyperarousal continuing through sleep in Primary Insomnia. © 2013.
Ortigue, Stephanie; Patel, Nisa; Bianchi-Demicheli, Francesco
Electroencephalogram (EEG) combined with brain source localization algorithms is becoming a powerful tool in the neuroimaging study of human cerebral functions. The present article provides a tutorial on the various EEG methods currently used to study the human brain activity, notably during sexual response. Review of published literature on standard EEG waveform analyses and most recent electrical neuroimaging techniques (microstate approach and two methods of brain source localization). Retrospective overview of pertinent literature. Although the standard EEG waveform analyses enable millisecond time-resolution information about the human sexual responses in the brain, less is clear about their related spatial information. Nowadays, the improvement of EEG techniques and statistical approaches allows the visualization of the dynamics of the human sexual response with a higher spatiotemporal resolution. Here, we describe these enhanced techniques and summarize along with an overview of what we have learned from them in terms of chronoarchitecture of sexual response in the human brain. Finally, the speculation on how we may be able to use other enhanced approaches, such as independent component analysis, are also presented. EEG neuroimaging has already been proven as a strong worthwhile research tool. Combining this approach with standard EEG waveform analyses in sexual medicine may provide a better understanding of the neural activity underlying the human sexual response in both healthy and clinical populations.
Schindler, Kaspar; Gast, Heidemarie; Goodfellow, Marc; Rummel, Christian
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
Shah, Pankaj B; James, Saji; Elayaraja, S
This is an updated version of original Cochrane review published in Issue 1, 2014.Febrile seizures can be classified as simple or complex. Complex febrile seizures are associated with fever that lasts longer than 15 minutes, occur more than once within 24 hours and are confined to one side of the child's body. It is common in some countries for doctors to recommend an electroencephalograph (EEG) for children with complex febrile seizures. A limited evidence base is available to support the use of EEG and its timing after complex febrile seizures among children. To assess the use of EEG and its timing after complex febrile seizures in children younger than five years of age. For the latest update of this review, we searched the Cochrane Epilepsy Group Specialized Register (6 July 2015), the Cochrane Central Register of Controlled Trials (CENTRAL, 2005, Issue 6), MEDLINE (6 July 2015) and ClinicalTrials.gov (6 July 2015). We applied no language restrictions. All randomised controlled trials (RCTs) that examined the utility of an EEG and its timing after complex febrile seizures in children. Review authors selected and retrieved the articles and independently assessed which articles should be included. We resolved disagreements by discussion and by consultation with the Cochrane Epilepsy Group. We applied standard methodological procedures expected by Cochrane. Of 37 potentially eligible studies, no RCTs met the inclusion criteria. We found no RCTs as evidence to support or refute the use of EEG and its timing after complex febrile seizures among children. An RCT can be planned in such a way that participants are randomly assigned to the EEG group and to the non-EEG group with sufficient sample size. Since the last version of this review, we found no new studies.
Holm, Anu; Lukander, Kristian; Korpela, Jussi; Sallinen, Mikael; Müller, Kiti M I
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".
Logemann, H N Alexander; Lansbergen, Marieke M; Van Os, Titus W D P; Böcker, Koen B E; Kenemans, J Leon
EEG-feedback, also called neurofeedback, is a training procedure aimed at altering brain activity, and is used as a treatment for disorders like Attention Deficit/Hyperactivity Disorder (ADHD). Studies have reported positive effects of neurofeedback on attention and other dependent variables. However, double-blind studies including a sham neurofeedback control group are lacking. The inclusion of such group is crucial to control for unspecific effects. The current work presents a sham-controlled, double-blind evaluation. The hypothesis was that neurofeedback enhances attention and decreases impulsive behavior. Participants (n=27) were students selected on relatively high scores on impulsivity/inattention questionnaires (Barrat Impulsivity Scale and Broadbent CFQ). They were assigned to a neurofeedback treatment or a sham group. (sham)Neurofeedback training was planned for 15 weeks consisting of a total of 30 sessions, each lasting 22 min. Before and after 16 sessions (i.e., interim analyses), qEEG was recorded and impulsivity and inattention was assessed using a stop signal task and reversed continuous performance task and two questionnaires. Results of the interim analyses showed that participants were blind with respect to group inclusion, but no trend towards an effect of neurofeedback on behavioral measures was observed. Therefore in line with ethical guidelines the experiment was ceased. These results implicate a possible lack of effect of neurofeedback when one accounts for non-specific effects. However, the specific form of feedback and application of the sham-controlled double-blind design may have diminished the effect of neurofeedback. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.
Yingying Jiao; Bao-Liang Lu
Slow eye movement (SEM) is reported as a reliable indicator of sleep onset period (SOP) in sleep researches, but its characteristics and functions for detecting driving fatigue have not been fully studied. Through visual observations on ten subjects' experimental data, we found that SEMs tend to occur during eye closure events (ECEs). SEMs accompanied with alpha wave's attenuation during simulated driving was observed in our study. We used box plots to analyze the distribution of durations of different ECEs to measure sleepiness level. Experimental results indicate that the ECEs with SEM have higher duration distribution, representing higher sleepiness level, especially for those accompanied by alpha wave's attenuation. This verifies that SEM can be used as a reliable indicator for recognizing driver's SOP. In light of this and considering the possible accompanying of Electroencephalograph (EEG) wave changes, we propose a new algorithm for detecting SEM, which extracted EEG power related features from occipital O2 signal to add them into features set of horizontal Electro-Oculogram (HEOG) signal. Then, maximum relevance and minimum redundancy (mRMR) method was used for feature selection and support vector machine (SVM) was used to classify the SEM class and non-SEM class. Experimental results demonstrate that using EEG power related features can improve the algorithm's accuracy by an average 1.4%. The feature P(α+θ)/β was ranked highest by mRMR among all EEG features, indicating the interactive relationship between EEG waves and SEM.
Goshvarpour, Ateke; Abbasi, Ataollah; Goshvarpour, Atefeh
The objective of the present study is to investigate the anatomical distribution of the cortical sources of emotional response to music videos by means of electroencephalogram (EEG) analysis. A novel methodology is introduced to determine the nonlinear couplings between different brain regions based on the coherence analysis, nonlinear features of EEG recordings and a source localization method, standard low resolution electromagnetic tomography (sLORETA). 32 channels of EEG time series of 32 subjects available in DEAP database were studied. The Lyapunov exponents and approximate entropy were applied to the EEG. The coherence for Lyapunov exponents and approximate entropy were calculated between each electrode paired to all other electrodes. Considering valence and arousal related effects, the sLORETA was applied to each above mentioned feature to determine emotional processing cortices. Using the proposed methodology, significant differences in sLORETA activity are observed between different emotional states. These changes were dominantly localized in the Brodmann 11 area (frontal lobe). In addition, some evidences provided that the left hemisphere is more activated to valence and arousal-related effects. Results suggest that considering two dimensions of emotions concurrently, a wider brain region was dominated in synchronization: superior frontal gyrus, middle frontal gyrus, and superior parietal lobule. Cooperating nonlinear coupling along with EEG source localization methods could provide an interesting tool for understanding the cortical specialization in emotional processes.
Dong, Li; Li, Fali; Liu, Qiang; Wen, Xin; Lai, Yongxiu; Xu, Peng; Yao, Dezhong
Reference electrode standardization technique (REST) has been increasingly acknowledged and applied as a re-reference technique to transform an actual multi-channels recordings to approximately zero reference ones in electroencephalography/event-related potentials (EEG/ERPs) community around the world in recent years. However, a more easy-to-use toolbox for re-referencing scalp EEG data to zero reference is still lacking. Here, we have therefore developed two open-source MATLAB toolboxes for REST of scalp EEG. One version of REST is closely integrated into EEGLAB, which is a popular MATLAB toolbox for processing the EEG data; and another is a batch version to make it more convenient and efficient for experienced users. Both of them are designed to provide an easy-to-use for novice researchers and flexibility for experienced researchers. All versions of the REST toolboxes can be freely downloaded at http://www.neuro.uestc.edu.cn/rest/Down.html, and the detailed information including publications, comments and documents on REST can also be found from this website. An example of usage is given with comparative results of REST and average reference. We hope these user-friendly REST toolboxes could make the relatively novel technique of REST easier to study, especially for applications in various EEG studies.
Skrandies, Wolfgang; Klein, Alexander
We investigated the change of evoked EEG frequencies induced by learning to solve mathematical tasks by applying divisibility rules. The performance on easy (divisibility by 2, 3, or 5) and hard tasks (divisibility by 9 or by 11) was compared. In a behavioral experiment on 52 adults we found a significant increase in performance from 67% to 90% correct responses induced by rule learning. Subsequently, the EEG data recorded from 30 additional volunteers were analyzed. EEG recordings were performed in two parts: First, subjects had to solve 200 tasks without knowing the divisibility rules. Then the rules were explained, followed by another set of 200 tasks. EEG was measured simultaneously in 30 channels, artifacts were removed offline, and the data before and after rule learning were compared. A wavelet transformation with the Morlet-5 wavelet was computed, and the scalp topography of the maximal frequency and its occurrence time was compared. Largest effects were observed with frequencies between about 6 and 18 Hz. In the frequency band between 12 and 30 Hz maximal frequencies were significantly different after successful learning over frontal and centro-parietal scalp areas of the right hemisphere. These changes were paralleled by decreased response times. In summary, our data illustrate a significant relation between successful learning divisibility rules and changes in the frequency content of the task-related EEG. Significant effects were observed after a very short training period of less than 10 min. Copyright © 2014 Elsevier B.V. All rights reserved.
Li, Yuanqing; Cichocki, Andrzej; Amari, Shun-Ichi
In this paper, we use a two-stage sparse factorization approach for blindly estimating the channel parameters and then estimating source components for electroencephalogram (EEG) signals. EEG signals are assumed to be linear mixtures of source components, artifacts, etc. Therefore, a raw EEG data matrix can be factored into the product of two matrices, one of which represents the mixing matrix and the other the source component matrix. Furthermore, the components are sparse in the time-frequency domain, i.e., the factorization is a sparse factorization in the time frequency domain. It is a challenging task to estimate the mixing matrix. Our extensive analysis and computational results, which were based on many sets of EEG data, not only provide firm evidences supporting the above assumption, but also prompt us to propose a new algorithm for estimating the mixing matrix. After the mixing matrix is estimated, the source components are estimated in the time frequency domain using a linear programming method. In an example of the potential applications of our approach, we analyzed the EEG data that was obtained from a modified Sternberg memory experiment. Two almost uncorrelated components obtained by applying the sparse factorization method were selected for phase synchronization analysis. Several interesting findings were obtained, especially that memory-related synchronization and desynchronization appear in the alpha band, and that the strength of alpha band synchronization is related to memory performance.
Stewart, Jennifer L.; Coan, James A.; Towers, David N.; Allen, John J. B.
Background Although it has been argued that frontal electroencephalographic (EEG) asymmetry at rest may be a risk marker for major depressive disorder (MDD), it is unclear whether a pattern of relatively less left than right activity characterizes depressed individuals during emotional challenges. Examination of frontal asymmetry during emotion task manipulations could provide an assessment of the function of systems relevant for MDD, and test the limits of frontal EEG asymmetry as a marker of risk for depression. Methods EEG data were assessed during a facial emotion task, wherein 306 individuals age 18–34 (31% male) with (n =143) and without (n = 163) DSM-IV defined lifetime MDD made directed facial actions of approach (angry and happy) and withdrawal (afraid and sad) expressions. Results Lifetime depressed individuals displayed less relative left frontal activity than never-depressed individuals during all facial expressions across four EEG reference montages, findings that were not due to emotional experience, facial expression quality, electromyographic (EMG) activity, or current depression status. Limitations Although this was a sizable sample, only one emotion task was utilized. Conclusions Results provide further support for frontal EEG asymmetry as a risk marker for MDD. PMID:20870293
Tarullo, Amanda R.; Garvin, Melissa C.; Gunnar, Megan R.
While effects of institutional care on behavioral development have been studied extensively, effects on neural systems underlying these socioemotional and attention deficits are only beginning to be examined. The current study assessed electroencephalogram (EEG) power in 18-month-old internationally adopted, post-institutionalized children (n = 37) and comparison groups of non-adopted children (n = 47) and children internationally adopted from foster care (n = 39). For their age, post-institutionalized children had an atypical EEG power distribution, with relative power concentrated in lower frequency bands compared to non-adopted children. Both internationally adopted groups had lower absolute alpha power than non-adopted children. EEG power was not related to growth at adoption or to global cognitive ability. Atypical EEG power distribution at 18 months predicted indiscriminate friendliness and poorer inhibitory control at 36 months. Both post-institutionalized and foster care children were more likely than non-adopted children to exhibit indiscriminate friendliness. Results are consistent with a cortical hypoactivation model of the effects of early deprivation on neural development and provide initial evidence associating this atypical EEG pattern with indiscriminate friendliness. Outcomes observed in the foster care children raise questions about the specificity of institutional rearing as a risk factor and emphasize the need for broader consideration of the effects of early deprivation and disruptions in care. PMID:21171750
Full Text Available The dynamics of human electroencephalography (EEG have been proved to be related to cognitive activities. This study separately assessed the two EEG components, amplitude and rhythm, aiming to capture their individual contributions to cognitive functions. We extracted the local peaks of EEGs under rest or photic stimulation and calculated the symbolic dynamics of their voltages (amplitude and interpeak intervals (instantaneous frequency, individually. The sample consisted of 89 geriatric outpatients in three patient groups: 38 fresh cases of vascular dementia (VD, 22 fresh cases of Alzheimer’s disease (AD and 29 controls. Both sample entropy and number of forbidden words revealed significantly less regular symbolic dynamics in the whole EEG tracings of the VD than the AD and control groups. We found consistent results between groups with the symbolic dynamics in the local-peak voltage sequence rather than the interpeak interval sequence. Photic stimulation amplified the differences between groups. These results suggest that the EEG dynamics which relates to either cognitive functions or the underlying pathologies of dementia are embedded within the dynamics of the amount of but not the interval between each synchronized firing of adjacent cerebral neurons.
Full Text Available Recent evidence suggests that learned self-regulation of localized brain activity in deep limbic areas such as the amygdala, may alleviate symptoms of affective disturbances. Thus far self-regulation of amygdala activity could be obtained only via fMRI guided neurofeedback, an expensive and immobile procedure. EEG on the other hand is relatively inexpensive and can be easily implemented in any location. However the clinical utility of EEG neurofeedback for affective disturbances remains limited due to low spatial resolution, which hampers the targeting of deep limbic areas such as the amygdala. We introduce an EEG prediction model of amygdala activity from a single electrode. The gold standard used for training is the fMRI-BOLD signal in the amygdala during simultaneous EEG/fMRI recording. The suggested model is based on a time/frequency representation of the EEG data with varying time-delay. Previous work has shown a strong inhomogeneity among subjects as is reflected by the models created to predict the amygdala BOLD response from EEG data. In that work, different models were constructed for different subjects. In this work, we carefully analyzed the inhomogeneity among subjects and were able to construct a single model for the majority of the subjects. We introduce a method for inhomogeneity assessment. This enables us to demonstrate a choice of subjects for which a single model could be derived. We further demonstrate the ability to modulate brain-activity in a neurofeedback setting using feedback generated by the model. We tested the effect of the neurofeedback training by showing that new subjects can learn to down-regulate the signal amplitude compared to a sham group, which received a feedback obtained by a different participant. This EEG based model can overcome substantial limitations of fMRI-NF. It can enable investigation of NF training using multiple sessions and large samples in various locations.
Melissant, Co; Ypma, Alexander; Frietman, Edward E E; Stam, Cornelis J
Many researchers have studied automatic EEG classification and recently a lot of work has been done on artefact-removal from EEG data using independent component analyses (ICA). However, demonstrating that a ICA-processed multichannel EEG measurement becomes more interpretable compared to the raw data (as is usually done in work on ICA-processing of EEG data) does not yet prove that detection of (incipient) anomalies is also better possible after ICA-processing. The objective of this study is to show that ICA-preprocessing is useful when constructing a detection system for Alzheimer's disease. The paper describes a method for detection of EEG patterns indicative of Alzheimer's disease using automatic pattern recognition techniques. Our method incorporates an artefact removal stage based on ICA prior to automatic classification. The method is evaluated on measurements of a length of 8s from two groups of patients, where one group is in an initial stage of the disease (28 patients), whereas the other group is in a more progressed stage (15 patients). Both setups include a control group that should be classified as normal (10 and 21, respectively). Our final classification results for the group with severe Alzheimer's disease are comparable to the best results from literature. We show that ICA-based reduction of artefacts improves classification results for patients in an initial stage. We conclude that a more robust detection of Alzheimer's disease related EEG patterns may be obtained by employing ICA as ICA based pre-processing of EEG data can improve classification results for patients in an initial stage of Alzheimer's disease.
Full Text Available Electroencephalography (EEG is considered the output of a brain and it is a bioelectrical signal with multiscale and nonlinear properties. Motor Imagery EEG (MI-EEG not only has a close correlation with the human imagination and movement intention but also contains a large amount of physiological or disease information. As a result, it has been fully studied in the field of rehabilitation. To correctly interpret and accurately extract the features of MI-EEG signals, many nonlinear dynamic methods based on entropy, such as Approximate Entropy (ApEn, Sample Entropy (SampEn, Fuzzy Entropy (FE, and Permutation Entropy (PE, have been proposed and exploited continuously in recent years. However, these entropy-based methods can only measure the complexity of MI-EEG based on a single scale and therefore fail to account for the multiscale property inherent in MI-EEG. To solve this problem, Multiscale Sample Entropy (MSE, Multiscale Permutation Entropy (MPE, and Multiscale Fuzzy Entropy (MFE are developed by introducing scale factor. However, MFE has not been widely used in analysis of MI-EEG, and the same parameter values are employed when the MFE method is used to calculate the fuzzy entropy values on multiple scales. Actually, each coarse-grained MI-EEG carries the characteristic information of the original signal on different scale factors. It is necessary to optimize MFE parameters to discover more feature information. In this paper, the parameters of MFE are optimized independently for each scale factor, and the improved MFE (IMFE is applied to the feature extraction of MI-EEG. Based on the event-related desynchronization (ERD/event-related synchronization (ERS phenomenon, IMFE features from multi channels are fused organically to construct the feature vector. Experiments are conducted on a public dataset by using Support Vector Machine (SVM as a classifier. The experiment results of 10-fold cross-validation show that the proposed method yields
Full Text Available This paper made a research on the feature extraction and pattern recognition of left and right hands motor imagery EEG signals. In combination with the data from BCI Competition III, denoising preprocessing is carried out for EEG signals firstly; and then, the relative wavelet energy is extracted as a feature vector from the Channels C3 and C4 by use of the algorithm for relative wavelet energy, and pattern recognition is carried out by use of the radial basis function neural network (RBFNN. Simulation results show that the proposed method achieves good classification results.
Fallon, N; Chiu, Y; Nurmikko, T; Stancak, A
Fibromyalgia syndrome (FM) is a chronic pain disorder characterized by widespread pain, sleep disturbance, fatigue and cognitive/affective symptoms. Functional imaging studies have revealed that FM and other chronic pain syndromes can affect resting brain activity. This study utilized electroencephalographic (EEG) recordings to investigate the relative power of ongoing oscillatory activity in the resting brain. A 64-channel EEG was recorded at rest in 19 female FM patients and 18 healthy, age-matched, control subjects. The Manual Tender Point Scale (MTPS) examination was performed to quantify tonic pain and tenderness on the day of testing along with measures of mood, arousal and fatigue. Oscillations in delta, theta, alpha, beta and gamma frequency bands were analysed using Standardised Low-Resolution Brain Electromagnetic Tomography to evaluate sources of spectral activity throughout the whole brain. FM patients exhibited greater pain, tiredness and tension on the day of testing relative to healthy control participants and augmented theta activity in prefrontal and anterior cingulate cortices. No significant differences were seen in other frequency bands. Augmented frontal theta activity in FM patients significantly correlated with measures of tenderness and mean tiredness scores. The findings indicate that alterations to resting-state oscillatory activity may relate to ongoing tonic pain and fatigue in FM, and manifest in brain regions relevant for cognitive-attentional aspects of pain processing and endogenous pain inhibition. Enhanced low-frequency oscillations were previously seen in FM and other chronic pain syndromes, and may relate to pathophysiological mechanisms for ongoing pain such as thalamocortical dysrhythmia. Increased prefrontal theta activity may contribute to persistent pain in fibromyalgia or represent the outcome of prolonged symptoms. The findings point to the potential for therapeutic interventions aimed at normalizing neural oscillations
Hu, Xin; Yu, Jianwen; Song, Mengdi; Yu, Chun; Wang, Fei; Sun, Pei; Wang, Daifa; Zhang, Dan
Compared with the well documented neurophysiological findings on negative emotions, much less is known about positive emotions. In the present study, we explored the EEG correlates of ten different positive emotions (joy, gratitude, serenity, interest, hope, pride, amusement, inspiration, awe, and love). A group of 20 participants were invited to watch 30 short film clips with their EEGs simultaneously recorded. Distinct topographical patterns for different positive emotions were found for the correlation coefficients between the subjective ratings on the ten positive emotions per film clip and the corresponding EEG spectral powers in different frequency bands. Based on the similarities of the participants' ratings on the ten positive emotions, these emotions were further clustered into three representative clusters, as 'encouragement' for awe, gratitude, hope, inspiration, pride, 'playfulness' for amusement, joy, interest, and 'harmony' for love, serenity. Using the EEG spectral powers as features, both the binary classification on the higher and lower ratings on these positive emotions and the binary classification between the three positive emotion clusters, achieved accuracies of approximately 80% and above. To our knowledge, our study provides the first piece of evidence on the EEG correlates of different positive emotions.
Wang, Shuaifang; Li, Yan; Wen, Peng; Lai, David
The alcoholism can be detected by analyzing electroencephalogram (EEG) signals. However, analyzing multi-channel EEG signals is a challenging task, which often requires complicated calculations and long execution time. This paper proposes three data selection methods to extract representative data from the EEG signals of alcoholics. The methods are the principal component analysis based on graph entropy (PCA-GE), the channel selection based on graph entropy (GE) difference, and the mathematic combinations channel selection, respectively. For comparison purposes, the selected data from the three methods are then classified by three classifiers: the J48 decision tree, the K-nearest neighbor and the Kstar, separately. The experimental results show that the proposed methods are successful in selecting data without compromising the classification accuracy in discriminating the EEG signals from alcoholics and non-alcoholics. Among them, the proposed PCA-GE method uses only 29.69% of the whole data and 29.5% of the computation time but achieves a 94.5% classification accuracy. The channel selection method based on the GE difference also gains a 91.67% classification accuracy by using only 29.69% of the full size of the original data. Using as little data as possible without sacrificing the final classification accuracy is useful for online EEG analysis and classification application design.
Hu, Xin; Yu, Jianwen; Song, Mengdi; Yu, Chun; Wang, Fei; Sun, Pei; Wang, Daifa; Zhang, Dan
Compared with the well documented neurophysiological findings on negative emotions, much less is known about positive emotions. In the present study, we explored the EEG correlates of ten different positive emotions (joy, gratitude, serenity, interest, hope, pride, amusement, inspiration, awe, and love). A group of 20 participants were invited to watch 30 short film clips with their EEGs simultaneously recorded. Distinct topographical patterns for different positive emotions were found for the correlation coefficients between the subjective ratings on the ten positive emotions per film clip and the corresponding EEG spectral powers in different frequency bands. Based on the similarities of the participants’ ratings on the ten positive emotions, these emotions were further clustered into three representative clusters, as ‘encouragement’ for awe, gratitude, hope, inspiration, pride, ‘playfulness’ for amusement, joy, interest, and ‘harmony’ for love, serenity. Using the EEG spectral powers as features, both the binary classification on the higher and lower ratings on these positive emotions and the binary classification between the three positive emotion clusters, achieved accuracies of approximately 80% and above. To our knowledge, our study provides the first piece of evidence on the EEG correlates of different positive emotions. PMID:28184194
Galina V. Portnova
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.
Dhanuka, A.K; Jain, B.K; Daljit, Singh; Maheshwari, D
...) and is associated with absence seizures in more than one third of cases. Fifteen patients with juvenile myoclonic epilepsy were studied with regard to their clinical profile, EEG data and sleep EEG findings...
Waterman, D.; Woestenburg, J.C.; Elton, M.; Hofman, W.; Kok, A.
The present report concerns the first study in which electrooculographic (EOG) contamination of electroencephalographic (EEG) recordings in rapid eye movement (REM) sleep is systematically investigated. Contamination of REM sleep EEG recordings in six subjects was evaluated in the frequency domain.
Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J
In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.
Hansen, Sofie Therese
investigate the extraction of EEG components having bandpower dynamics correlated with fMRI components. We show that adding anatomical information to the inference scheme improves the recovery of correlated components compared to only using functional information. The anatomical information is incorporated......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...
Zhao, Jianlin; Zhou, Weidong; Liu, Kai; Cai, Dongmei
We employed two methods of support vector machines (SVM) combined with two kinds of wavelet analysis to classify these EEG signals, on the basis of the different profiles, energy, and frequency characteristics of the EEG during the seizures. One method was to classify these signals using waveform characteristics of the EEG signal. The other was to classify these signals based on fluctuation index and variation coefficient of the EEG signal. We compared the classification accuracies of these two methods with the intermittent EEG and epileptic EEG. The results of the experiments showed that both the two methods for distinguishing epileptic EEG and interictal EEG can achieve an effective performance. It was also confirmed that the latter, the method based on the fluctuation index and variation coefficient, possesses a better effect of classification.
van der Lubbe, Robert Henricus Johannes; Wauschkuhn, Bernd; Wascher, Edmund; Niehoff, Torsten; Kömpf, Detlef; Verleger, Rolf
During preparation of horizontal saccades in humans, several lateralized (relative to saccade direction), event-related EEG components occur that have been interpreted as reflecting activity of frontal and parietal eye fields. We investigated to what degree these components are specific to saccade
Full Text Available Abstract Background The integration of EEG and fMRI is attractive because of their complementary precision regarding time and space. But the relationship between the indirect hemodynamic fMRI signal and the more direct EEG signal is uncertain. Event-related EEG responses can be analyzed in two different ways, reflecting two different kinds of brain activity: evoked, i.e. phase-locked to the stimulus, such as evoked potentials, or induced, i.e. non phase-locked to the stimulus such as event-related oscillations. In order to determine which kind of EEG activity was more closely related with fMRI, EEG and fMRI signals were acquired together, while subjects were presented with two kinds of rare events intermingled with frequent distractors. Target events had to be signaled by pressing a button and Novel events had to be ignored. Results Both Targets and Novels triggered a P300, of larger amplitude in the Novel condition. On the opposite, the fMRI BOLD response was stronger in the Target condition. EEG event-related oscillations in the gamma band (32–38 Hz reacted in a way similar to the BOLD response. Conclusions The reasons for such opposite differential reactivity between oscillations / fMRI on the one hand, and evoked potentials on the other, are discussed in the paper. Those results provide further arguments for a closer relationship between fast oscillations and the BOLD signal, than between evoked potentials and the BOLD signal.
Westhall, Erik; Rosén, Ingmar; Rossetti, Andrea O
of cardiac arrest patients included in the Target Temperature Management trial. The main objective was to evaluate if malignant EEG-patterns could reliably be identified. METHODS: Full-length EEGs from 103 comatose cardiac arrest patients were interpreted by four EEG-specialists with different nationalities...... in an international context with high reliability. SIGNIFICANCE: The establishment of strict criteria with high transferability between interpreters will increase the usefulness of routine EEG to assess neurological prognosis after cardiac arrest....
Ignaccolo, M.; Latka, M.; Jernajczyk, W.; Grigolini, P.; West, B. J.
EEG time series are analyzed using the diffusion entropy method. The resulting EEG entropy manifests short-time scaling, asymptotic saturation and an attenuated alpha-rhythm modulation. These properties are faithfully modeled by a phenomenological Langevin equation interpreted within a neural network context. Detrended fluctuation analysis of the EEG data is compared with diffusion entropy analysis and is found to suppress certain important properties of the EEG time series.
Barua, Shaibal; Begum, Shahina
Brain waves obtained by Electroencephalograms (EEG) recording are an important research area in medical and health and brain computer interface (BCI). Due to the nature of EEG signal, noises and artifacts can contaminate it, which leads to a serious misinterpretation in EEG signal analysis. These contaminations are referred to as artifacts, which are signals of other than brain activity. Moreover, artifacts can cause significant miscalculation of the EEG measurements that reduces the clinical...
Full Text Available Due to its millisecond-scale temporal resolution, EEG allows to assess neural correlates with precisely defined temporal relationship relative to a given event. This knowledge is generally lacking in data from functional magnetic resonance imaging (fMRI which has a temporal resolution on the scale of seconds so that possibilities to combine the two modalities are sought. Previous applications combining event-related potentials (ERPs with simultaneous fMRI BOLD generally aimed at measuring known ERP components in single trials and correlate the resulting time series with the fMRI BOLD signal. While it is a valuable first step, this procedure cannot guarantee that variability of the chosen ERP component is specific for the targeted neurophysiological process on the group and single subject level. Here we introduce a newly developed data-driven analysis procedure that automatically selects task-specific electrophysiological independent components (ICs. We used single-trial simultaneous EEG/fMRI analysis of a visual Go/Nogo task to assess inhibition-related EEG components, their trial-to-trial amplitude variability, and the relationship between this variability and the fMRI. Single-trial EEG/fMRI analysis within a subgroup of 22 participants revealed positive correlations of fMRI BOLD signal with EEG-derived regressors in fronto-striatal regions which were more pronounced in an early compared to a late phase of task execution. In sum, selecting Nogo-related ICs in an automated, single subject procedure reveals fMRI-BOLD responses correlated to different phases of task execution. Furthermore, to illustrate utility and generalizability of the method beyond detecting the presence or absence of reliable inhibitory components in the EEG, we show that the independent component selection can be extended to other events in the same dataset, e.g. the visual responses.
Baldin, Elisa; Hauser, W A; Buchhalter, Jeffrey R; Hesdorffer, Dale C; Ottman, Ruth
No previous population-based study has addressed the contribution of activation procedures to the yield of epileptiform abnormalities on serial EEGs. We assessed yield of activation-related epileptiform abnormalities and predictors of finding an activation-related abnormality with multiple EEGs in a population-based study of newly diagnosed epilepsy. We used the resources of the Rochester Epidemiology Project to identify 449 residents of Rochester, Minnesota with a diagnosis of newly diagnosed epilepsy at age 1 year or older, between 1960 and 1994, who had at least one EEG. Information on all activation procedures (i.e., sleep, hyperventilation, and photic activation) and seizure/epilepsy characteristics was obtained by comprehensive review of medical records. At the first EEG, the yield of epileptiform abnormalities was greatest for individuals 1 to 19 years of age at diagnosis, for each activation procedure. The yield in patients aged 1 to 19 versus ≥20 years was 21.6% versus 10.3% for sleep, 6.5% versus 3.3% for photic stimulation, and 10.3% versus 5% for hyperventilation. Among young people (aged 1-19 years), sleep was associated with an increased likelihood of finding an activation-related abnormality on any EEG. The likelihood of finding an activation-related abnormality on any EEG was decreased for postnatal symptomatic and for unknown etiology. Among activation procedures, sleep showed the highest yield of epileptiform abnormalities. There was a low yield for photic stimulation and hyperventilation. Within each activation procedure, younger age at diagnosis had the greatest yield. Sleep is the most effective activation procedure, especially in younger patients, and should be performed when possible.
Jensen, Camilla Birgitte Falk; Petersen, Michael Kai; Larsen, Jakob Eg
Combine wireless neuroheadsets with smartphones that enable mobile brain imaging can potentially allow us to design cognitive interfaces which adapt to our affective responses. Neuroimaging experiments using electroencephalography (EEG) initially identified two components elicited by pleasant...... susceptible to noise if captured in a mobile context. Hypothesizing that retrieval of emotional responses in mobile usage scenarios could be enhanced through spatial filtering, we compare a standard EEG electrode based analysis against an approach based on independent component analysis (ICA). By clustering...... scalp maps and time series responses we identify neural signatures that are differentially modulated when passively viewing neutral, pleasant and unpleasant images. While early responses can be detected from the raw EEG signal we identify multiple early and late ICA components that are modulated...
Fingelkurts, Andrew A; Fingelkurts, Alexander A; Kallio-Tamminen, Tarja
The therapeutic potential of meditation for physical and mental well-being is well documented, however the possibility of adverse effects warrants further discussion of the suitability of any particular meditation practice for every given participant. This concern highlights the need for a personalized approach in the meditation practice adjusted for a concrete individual. This can be done by using an objective screening procedure that detects the weak and strong cognitive skills in brain function, thus helping design a tailored meditation training protocol. Quantitative electroencephalogram (qEEG) is a suitable tool that allows identification of individual neurophysiological types. Using qEEG screening can aid developing a meditation training program that maximizes results and minimizes risk of potential negative effects. This brief theoretical-conceptual review provides a discussion of the problem and presents some illustrative results on the usage of qEEG screening for the guidance of mediation personalization. Copyright © 2015 Elsevier Ltd. All rights reserved.
Lovelace, Joseph A; Witt, Tyler S; Beyette, Fred R
A design for a modular, compact, and accurate wireless electroencephalograph (EEG) system is proposed. EEG is the only non-invasive measure for neuronal function of the brain. Using a number of digital signal processing (DSP) techniques, this neuronal function can be acquired and processed into meaningful representations of brain activity. The system described here utilizes Bluetooth to wirelessly transmit the digitized brain signal for an end application use. In this way, the system is portable, and modular in terms of the device to which it can interface. Brain Computer Interface (BCI) has become a popular extension of EEG systems in modern research. This design serves as a platform for applications using BCI capability.
Zibrandtsen, I. C.; Kidmose, Preben; Christensen, Christian Bech
Objective Ear-EEG is recording of electroencephalography from a small device in the ear. This is the first study to compare ictal and interictal abnormalities recorded with ear-EEG and simultaneous scalp-EEG in an epilepsy monitoring unit. Methods We recorded and compared simultaneous ear......-EEG and scalp-EEG from 15 patients with suspected temporal lobe epilepsy. EEGs were compared visually by independent neurophysiologists. Correlation and time-frequency analysis was used to quantify the similarity between ear and scalp electrodes. Spike-averages were used to assess similarity of interictal...... and frequency dynamics can be observed from visual inspection and time-frequency analysis. Spike averages derived from ear-EEG electrodes yield a recognizable spike appearance. Conclusions Our results suggest that ear-EEG can reliably detect electroencephalographic patterns associated with focal temporal lobe...
Background and Aim: Recently an electroencephalographic (EEG) spectral entropy module (M-ENTROPY) for an anaesthetic monitor has become commercially available. We compared its performance as an indicator of the state of anaesthesia with that of an older conventional quantitative EEG (QEEG) module (M-EEG) by ...
Patki, S.; Grundlehner, B.; Nakada, T.; Penders, J.
Miniaturized, low power and low noise circuits and systems are instrumental in bringing EEG monitoring to the home environment. In this paper, we present a miniaturized, low noise and low-power EEG wireless platform integrated into a wearable headset. The wireless EEG headset achieves remote and
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.
van Putten, Michel Johannes Antonius Maria
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
Reuber, M.; Fernandez, G.S.E.; Bauer, J.; Singh, D.D.; Elger, C.E.
PURPOSE: To examine interictal EEG abnormalities in patients with psychogenic nonepileptic seizures (PNESs). METHODS: (a) Retrospective study of EEG reports of 187 consecutive patients with PNES seen at the Department of Epileptology, Bonn, Germany; (b) Blinded, multirater comparison of EEGs of all
McHugh, J C
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.
Recording EEG In Young Children Without Sedation. ... African Journal of Neurological Sciences ... The aim of this work was to determine if it is possible to carry out EEG in children up to 4 years old without sedation and analyze the factors that could influence upon the possibility of performing EEG, in vigil or with sedation.
Suhhova, Anna; Bachmann, Maie; Karai, Deniss; Lass, Jaanus; Hinrikus, Hiie
This study is aimed at evaluating the effect of microwave radiation on human brain bioelectric activity at different levels of exposure. For this purpose, 450 MHz microwave exposure modulated at 40 Hz frequency was applied to a group of 15 healthy volunteers at two different specific absorption rate (SAR) levels: a higher level of 0.303 W/kg (field strength 24.5 V/m) and a lower level of 0.003 W/kg (field strength 2.45 V/m). Ten exposure cycles (1 min off and 1 min on) at fixed SAR values were applied. A resting eyes-closed electroencephalogram (EEG) was continuously recorded. Results showed a statistically significant increase in the EEG power in the EEG beta2 (157%), beta1 (61%) and alpha (68%) frequency bands at the higher SAR level, and in the beta2 (39%) frequency band at the lower SAR level. Statistically significant changes were detected for six individual subjects in the EEG alpha band and four subjects in the beta1 and beta2 bands at the higher SAR level; three subjects were affected in the alpha, beta1 and beta2 bands at the lower SAR level. The study showed that decreasing the SAR 100 times reduced the related changes in the EEG three to six times and the number of affected subjects, but did not exclude the effect. Copyright © 2012 Wiley Periodicals, Inc.
Perescis, Martin F J; de Bruin, Natasja; Heijink, Liesbeth; Kruse, Chris; Vinogradova, Lyudmila; Lüttjohann, Annika; van Luijtelaar, Gilles; van Rijn, Clementina M
Cannabinoid CB1 antagonists have been investigated for possible treatment of e.g. obesity-related disorders. However, clinical application was halted due to their symptoms of anxiety and depression. In addition to these adverse effects, we have shown earlier that chronic treatment with the CB1 antagonist rimonabant may induce EEG-confirmed convulsive seizures. In a regulatory repeat-dose toxicity study violent episodes of "muscle spasms" were observed in Wistar rats, daily dosed with the CB1 receptor antagonist SLV326 during 5 months. The aim of the present follow-up study was to investigate whether these violent movements were of an epileptic origin. In selected SLV326-treated and control animals, EEG and behavior were monitored for 24 hours. 25% of SLV326 treated animals showed 1 to 21 EEG-confirmed generalized convulsive seizures, whereas controls were seizure-free. The behavioral seizures were typical for a limbic origin. Moreover, interictal spikes were found in 38% of treated animals. The frequency spectrum of the interictal EEG of the treated rats showed a lower theta peak frequency, as well as lower gamma power compared to the controls. These frequency changes were state-dependent: they were only found during high locomotor activity. It is concluded that long term blockade of the endogenous cannabinoid system can provoke limbic seizures in otherwise healthy rats. Additionally, SLV326 alters the frequency spectrum of the EEG when rats are highly active, suggesting effects on complex behavior and cognition.
Castelnovo, Anna; Riedner, Brady A; Smith, Richard F; Tononi, Giulio; Boly, Melanie; Benca, Ruth M
To examine scalp and source power topography in sleep arousals disorders (SADs) using high-density EEG (hdEEG). Fifteen adult subjects with sleep arousal disorders (SADs) and 15 age- and gender-matched good sleeping healthy controls were recorded in a sleep laboratory setting using a 256 channel EEG system. Scalp EEG analysis of all night NREM sleep revealed a localized decrease in slow wave activity (SWA) power (1-4 Hz) over centro-parietal regions relative to the rest of the brain in SADs compared to good sleeping healthy controls. Source modelling analysis of 5-minute segments taken from N3 during the first half of the night revealed that the local decrease in SWA power was prominent at the level of the cingulate, motor, and sensori-motor associative cortices. Similar patterns were also evident during REM sleep and wake. These differences in local sleep were present in the absence of any detectable clinical or electrophysiological sign of arousal. Overall, results suggest the presence of local sleep differences in the brain of SADs patients during nights without clinical episodes. The persistence of similar topographical changes in local EEG power during REM sleep and wakefulness points to trait-like functional changes that cross the boundaries of NREM sleep. The regions identified by source imaging are consistent with the current neurophysiological understanding of SADs as a disorder caused by local arousals in motor and cingulate cortices. Persistent localized changes in neuronal excitability may predispose affected subjects to clinical episodes.
Ferri, Raffaele; Rundo, Francesco; Silvani, Alessandro; Zucconi, Marco; Bruni, Oliviero; Ferini-Strambi, Luigi; Plazzi, Giuseppe; Manconi, Mauro
We aimed to analyze quantitatively rapid eye movement (REM) sleep electroencephalogram (EEG) in controls, drug-naïve idiopathic REM sleep behavior disorder patients (iRBD), and iRBD patients treated with clonazepam. Twenty-nine drug-naïve iRBD patients (mean age 68.2 years), 14 iRBD patients under chronic clonazepam therapy (mean age 66.3 years), and 21 controls (mean age 66.8 years) were recruited. Power spectra were obtained from sleep EEG (central derivation), using a 2-second sliding window, with 1-second steps. The power values of each REM sleep EEG spectral band (one every second) were normalized with respect to the average power value obtained during sleep stage 2 in the same individual. In drug-naïve patients, the normalized power values showed a less pronounced REM-related decrease of power in all bands with frequency EEG bands and were almost completely normalized in patients treated with clonazepam. The REM sleep EEG structure changes found in this study disclose subtle but significant alterations in the cortical electrophysiology of RBD that might represent the early expression of the supposed neurodegenerative processes already taking place at this stage of the disease and might be the target of better and effective future therapeutic strategies for this condition.
Li, Gang; Chung, Wan-Young
Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI) systems have been proposed to detect driver drowsiness. However, detecting driver drowsiness at its early stage poses a major practical hurdle when using existing BMI systems. This study proposes a context-aware BMI system aimed to detect driver drowsiness at its early stage by enriching the EEG data with the intensity of head-movements. The proposed system is carefully designed for low-power consumption with on-chip feature extraction and low energy Bluetooth connection. Also, the proposed system is implemented using JAVA programming language as a mobile application for on-line analysis. In total, 266 datasets obtained from six subjects who participated in a one-hour monotonous driving simulation experiment were used to evaluate this system. According to a video-based reference, the proposed system obtained an overall detection accuracy of 82.71% for classifying alert and slightly drowsy events by using EEG data alone and 96.24% by using the hybrid data of head-movement and EEG. These results indicate that the combination of EEG data and head-movement contextual information constitutes a robust solution for the early detection of driver drowsiness. PMID:26308002
Lusby, Cara M; Goodman, Sherryl H; Yeung, Ellen W; Bell, Martha Ann; Stowe, Zachary N
Associations between infants' frontal EEG asymmetry and temperamental negative affectivity (NA) across infants' first year of life and the potential moderating role of maternal prenatal depressive symptoms were examined prospectively in infants (n = 242) of mothers at elevated risk for perinatal depression. In predicting EEG, in the context of high prenatal depressive symptoms, infant NA and frontal EEG asymmetry were negatively associated at 3 months of age and positively associated by 12 months of age. By contrast, for low depression mothers, infant NA and EEG were not significantly associated at any age. Postnatal depressive symptoms did not add significantly to the models. Dose of infants' exposure to maternal depression mattered: infants exposed either pre- or postnatally shifted from a positive association at 3 months to a negative association at 12 months; those exposed both pre- and postnatally shifted from a negative association at 3 months to a positive association at 12 months. Prenatal relative to postnatal exposure did not matter for patterns of association between NA and EEG. The findings highlight the importance of exploring how vulnerabilities at two levels of analysis, behavioral and psychophysiological, co-occur over the course of infancy and in the context of mothers' depressive symptomatology.
Wang, Grace Y; Kydd, Robert; Russell, Bruce R
Methadone has been used to treat opiate dependence since the mid-1960s. Despite its clinical effectiveness there is evidence from neuropsychological studies demonstrating that its long-term use might have negative effects on cognition. Nevertheless, it remains uncertain whether the observed cognitive impairments in patients undertaking methadone maintenance treatment (MMT) are solely attributable to the pharmacological effects of methadone, as suggested by some researchers. Determining the effects of MMT on neuropsychological function using electroencephalography (EEG) combined with event-related potentials (ERP) has been used infrequently. However EEG and ERP provide a means of closely examining information processing to determine whether MMT induces any deficits. The purpose of this review was to investigate whether psychophysiological evidence supports cognitive impairment in association with MMT by focusing on research using EEG and ERPs. The findings of EEG studies to date appear not support the notion that cognitive impairments are attributable to the specific pharmacological effects of methadone suggested by some neuropsychological studies. However, due to the methodological deficits and limited number of the studies, any conclusion based on the findings of the existing EEG studies should be avoided.
Krishnaswamy, Pavitra; Bonmassar, Giorgio; Poulsen, Catherine; Pierce, Eric T; Purdon, Patrick L.; Brown, Emery N.
Combining electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) offers the potential for imaging brain activity with high spatial and temporal resolution. This potential remains limited by the significant ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation-related head movement within the magnetic field. We model the BCG artifact using a harmonic basis, pose the artifact removal problem as a local harmonic regression analysis, and develop an efficient maximum likelihood algorithm to estimate and remove BCG artifacts. Our analysis paradigm accounts for time-frequency overlap between the BCG artifacts and neurophysiologic EEG signals, and tracks the spatiotemporal variations in both the artifact and the signal. We evaluate performance on: simulated oscillatory and evoked responses constructed with realistic artifacts; actual anesthesia-induced oscillatory recordings; and actual visual evoked potential recordings. In each case, the local harmonic regression analysis effectively removes the BCG artifacts, and recovers the neurophysiologic EEG signals. We further show that our algorithm outperforms commonly used reference-based and component analysis techniques, particularly in low SNR conditions, the presence of significant time-frequency overlap between the artifact and the signal, and/or large spatiotemporal variations in the BCG. Because our algorithm does not require reference signals and has low computational complexity, it offers a practical tool for removing BCG artifacts from EEG data recorded in combination with fMRI. PMID:26151100
Full Text Available Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI systems have been proposed to detect driver drowsiness. However, detecting driver drowsiness at its early stage poses a major practical hurdle when using existing BMI systems. This study proposes a context-aware BMI system aimed to detect driver drowsiness at its early stage by enriching the EEG data with the intensity of head-movements. The proposed system is carefully designed for low-power consumption with on-chip feature extraction and low energy Bluetooth connection. Also, the proposed system is implemented using JAVA programming language as a mobile application for on-line analysis. In total, 266 datasets obtained from six subjects who participated in a one-hour monotonous driving simulation experiment were used to evaluate this system. According to a video-based reference, the proposed system obtained an overall detection accuracy of 82.71% for classifying alert and slightly drowsy events by using EEG data alone and 96.24% by using the hybrid data of head-movement and EEG. These results indicate that the combination of EEG data and head-movement contextual information constitutes a robust solution for the early detection of driver drowsiness.
Barry, Robert J; De Blasio, Frances; Rushby, Jacqueline A; Clarke, Adam R
We examined relationships between the phase of narrow-band electroencephalographic (EEG) activity at stimulus onset and the resultant event-related potentials (ERPs) in an equiprobable auditory Go/NoGo task with a fixed SOA, in the context of a novel conceptualisation of orthogonal phase effects (cortical negativity vs. positivity, negative driving vs. positive driving, waxing vs. waning). ERP responses to each stimulus type were analysed. Prestimulus narrow-band EEG activity (in 1Hz bands from 1 to 13Hz) at Cz was assessed for each trial using FFT decomposition of the EEG data. For each frequency, the cycle at stimulus onset was used to sort trials into four phases, for which ERPs were derived from the raw