WorldWideScience

Sample records for eeg signal acquisition

  1. Design of EEG Signal Acquisition System Using Arduino MEGA1280 and EEGAnalyzer

    Directory of Open Access Journals (Sweden)

    Saptono Debyo

    2016-01-01

    Full Text Available This study integrates the hardware circuit design and software development to achieve a 16 channels Electroencephalogram (EEG system for Brain Computer Interface (BCI applications. Signals obtained should be strong enough amplitude that is usually expressed in units of millivolts and reasonably clean of noise that appears when the data acquisition process. The process of data acquisition consists of two stages are the acquisition of the original EEG signal can be done by the active electrode with an instrumentation amplifier or a preamplifier and processing the signal to get better signals with improved signal quality by removing noise using filters with IC OPAMP. The design of a preamplifier with high common-mode rejection ratio and high signal-to-noise ratio is very important. Moreover, the friction between the electrode pads and the skin as well as the design of dual power supply. Designs used single-power AC-coupled circuit, which effectively reduces the DC bias and improves the error caused by the effects of part errors. At the same time, the digital way is applied to design the adjustable amplification and filter function, which can design for different EEG frequency bands. The next step, those EEG signals received by the microcontroller through a port Analog to Digital Converter (ADC that integrated and converted into digital signals and stored in the RAM of microcontroller which simultaneously at 16 ports in accordance with the minimal number of points of data collection on the human scalp. Implementation results have shown the series of acquisitions to work properly so that it can be displayed EEG signals via software EEGAnalyzer.

  2. Multi-Channel Electroencephalogram (EEG) Signal Acquisition and its Effective Channel selection with De-noising Using AWICA for Biometric System

    OpenAIRE

    B.Sabarigiri; D.Suganyadevi

    2014-01-01

    the embedding of low cost electroencephalogram (EEG) sensors in wireless headsets gives improved authentication based on their brain wave signals has become a practical opportunity. In this paper signal acquisition along with effective multi-channel selection from a specific area of the brain and denoising using AWICA methods are proposed for EEG based personal identification. At this point, to develop identification system the steps are as follows. (i) the high-quality device with the least ...

  3. Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG

    Science.gov (United States)

    Bleichner, Martin G.; Debener, Stefan

    2017-01-01

    Electroencephalography (EEG) is an important clinical tool and frequently used to study the brain-behavior relationship in humans noninvasively. Traditionally, EEG signals are recorded by positioning electrodes on the scalp and keeping them in place with glue, rubber bands, or elastic caps. This setup provides good coverage of the head, but is impractical for EEG acquisition in natural daily-life situations. Here, we propose the transparent EEG concept. Transparent EEG aims for motion tolerant, highly portable, unobtrusive, and near invisible data acquisition with minimum disturbance of a user's daily activities. In recent years several ear-centered EEG solutions that are compatible with the transparent EEG concept have been presented. We discuss work showing that miniature electrodes placed in and around the human ear are a feasible solution, as they are sensitive enough to pick up electrical signals stemming from various brain and non-brain sources. We also describe the cEEGrid flex-printed sensor array, which enables unobtrusive multi-channel EEG acquisition from around the ear. In a number of validation studies we found that the cEEGrid enables the recording of meaningful continuous EEG, event-related potentials and neural oscillations. Here, we explain the rationale underlying the cEEGrid ear-EEG solution, present possible use cases and identify open issues that need to be solved on the way toward transparent EEG. PMID:28439233

  4. Signal Quality Evaluation of Emerging EEG Devices

    Directory of Open Access Journals (Sweden)

    Thea Radüntz

    2018-02-01

    Full Text Available Electroencephalogram (EEG registration as a direct measure of brain activity has unique potentials. It is one of the most reliable and predicative indicators when studying human cognition, evaluating a subject's health condition, or monitoring their mental state. Unfortunately, standard signal acquisition procedures limit the usability of EEG devices and narrow their application outside the lab. Emerging sensor technology allows gel-free EEG registration and wireless signal transmission. Thus, it enables quick and easy application of EEG devices by users themselves. Although a main requirement for the interpretation of an EEG is good signal quality, there is a lack of research on this topic in relation to new devices. In our work, we compared the signal quality of six very different EEG devices. On six consecutive days, 24 subjects wore each device for 60 min and completed tasks and games on the computer. The registered signals were evaluated in the time and frequency domains. In the time domain, we examined the percentage of artifact-contaminated EEG segments and the signal-to-noise ratios. In the frequency domain, we focused on the band power variation in relation to task demands. The results indicated that the signal quality of a mobile, gel-based EEG system could not be surpassed by that of a gel-free system. However, some of the mobile dry-electrode devices offered signals that were almost comparable and were very promising. This study provided a differentiated view of the signal quality of emerging mobile and gel-free EEG recording technology and allowed an assessment of the functionality of the new devices. Hence, it provided a crucial prerequisite for their general application, while simultaneously supporting their further development.

  5. Engagement Assessment Using EEG Signals

    Science.gov (United States)

    Li, Feng; Li, Jiang; McKenzie, Frederic; Zhang, Guangfan; Wang, Wei; Pepe, Aaron; Xu, Roger; Schnell, Thomas; Anderson, Nick; Heitkamp, Dean

    2012-01-01

    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.

  6. Correlation of BOLD Signal with Linear and Nonlinear Patterns of EEG in Resting State EEG-Informed fMRI

    Directory of Open Access Journals (Sweden)

    Galina V. Portnova

    2018-01-01

    Full Text Available Concurrent EEG and fMRI acquisitions in resting state showed a correlation between EEG power in various bands and spontaneous BOLD fluctuations. However, there is a lack of data on how changes in the complexity of brain dynamics derived from EEG reflect variations in the BOLD signal. The purpose of our study was to correlate both spectral patterns, as linear features of EEG rhythms, and nonlinear EEG dynamic complexity with neuronal activity obtained by fMRI. We examined the relationships between EEG patterns and brain activation obtained by simultaneous EEG-fMRI during the resting state condition in 25 healthy right-handed adult volunteers. Using EEG-derived regressors, we demonstrated a substantial correlation of BOLD signal changes with linear and nonlinear features of EEG. We found the most significant positive correlation of fMRI signal with delta spectral power. Beta and alpha spectral features had no reliable effect on BOLD fluctuation. However, dynamic changes of alpha peak frequency exhibited a significant association with BOLD signal increase in right-hemisphere areas. Additionally, EEG dynamic complexity as measured by the HFD of the 2–20 Hz EEG frequency range significantly correlated with the activation of cortical and subcortical limbic system areas. Our results indicate that both spectral features of EEG frequency bands and nonlinear dynamic properties of spontaneous EEG are strongly associated with fluctuations of the BOLD signal during the resting state condition.

  7. Bluetooth Communication Interface for EEG Signal Recording in Hyperbaric Chambers.

    Science.gov (United States)

    Pastena, Lucio; Formaggio, Emanuela; Faralli, Fabio; Melucci, Massimo; Rossi, Marco; Gagliardi, Riccardo; Ricciardi, Lucio; Storti, Silvia F

    2015-07-01

    Recording biological signals inside a hyperbaric chamber poses technical challenges (the steel walls enclosing it greatly attenuate or completely block the signals as in a Faraday cage), practical (lengthy cables creating eddy currents), and safety (sparks hazard from power supply to the electronic apparatus inside the chamber) which can be overcome with new wireless technologies. In this technical report we present the design and implementation of a Bluetooth system for electroencephalographic (EEG) recording inside a hyperbaric chamber and describe the feasibility of EEG signal transmission outside the chamber. Differently from older systems, this technology allows the online recording of amplified signals, without interference from eddy currents. In an application of this technology, we measured EEG activity in professional divers under three experimental conditions in a hyperbaric chamber to determine how oxygen, assumed at a constant hyperbaric pressure of 2.8 ATA , affects the bioelectrical activity. The EEG spectral power estimated by fast Fourier transform and the cortical sources of the EEG rhythms estimated by low-resolution brain electromagnetic analysis were analyzed in three different EEG acquisitions: breathing air at sea level; breathing oxygen at a simulated depth of 18 msw, and breathing air at sea level after decompression.

  8. Analyzing Electroencephalogram Signal Using EEG Lab

    Directory of Open Access Journals (Sweden)

    Mukesh BHARDWAJ

    2009-01-01

    Full Text Available The EEG is composed of electrical potentials arising from several sources. Each source (including separate neural clusters, blink artifact or pulse artifact forms a unique topography onto the scalp – ‘scalp map‘. Scalp map may be 2-D or 3-D.These maps are mixed according to the principle of linear superposition. Independent component analysis (ICA attempts to reverse the superposition by separating the EEG into mutually independent scalp maps, or components. MATLAB toolbox and graphic user interface, EEGLAB is used for processing EEG data of any number of channels. Wavelet toolbox has been used for 2-D signal analysis.

  9. Identifying the effects of microsaccades in tripolar EEG signals.

    Science.gov (United States)

    Bellisle, Rachel; Steele, Preston; Bartels, Rachel; Lei Ding; Sunderam, Sridhar; Besio, Walter

    2017-07-01

    Microsaccades are tiny, involuntary eye movements that occur during fixation, and they are necessary to human sight to maintain a sharp image and correct the effects of other fixational movements. Researchers have theorized and studied the effects of microsaccades on electroencephalography (EEG) signals to understand and eliminate the unwanted artifacts from EEG. The tripolar concentric ring electrode (TCRE) sensors are used to acquire TCRE EEG (tEEG). The tEEG detects extremely focal signals from directly below the TCRE sensor. We have noticed a slow wave frequency found in some tEEG recordings. Therefore, we conducted the current work to determine if there was a correlation between the slow wave in the tEEG and the microsaccades. This was done by analyzing the coherence of the frequency spectrums of both tEEG and eye movement in recordings where microsaccades are present. Our preliminary findings show that there is a correlation between the two.

  10. EEG Signal Classification With Super-Dirichlet Mixture Model

    DEFF Research Database (Denmark)

    Ma, Zhanyu; Tan, Zheng-Hua; Prasad, Swati

    2012-01-01

    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...

  11. Dataset of quantitative spectral EEG of different stages of kindling acquisition in rats.

    Science.gov (United States)

    Jalilifar, Mostafa; Yadollahpour, Ali

    2018-02-01

    The data represented here are in relation with the manuscript "Quantitative assessments of extracellular EEG to classify specific features of main phases of seizure acquisition based on kindling model in Rat" (Jalilifar et al., 2017) [1] which quantitatively classified different main stages of the kindling process based on their electrophysiological characteristics using EEG signal processing. The data in the graphical form reported the contribution of different sub bands of EEG in different stages of kindling- induced epileptogenesis. Only EEG signals related to stages 1-2 (initial seizure stages (ISSs)), 3 (localized seizure stage (LSS)), and 4-5 (generalized seizure stages (GSSs) were transferred into frequency function by Fast Fourier Transform (FFT) and their power spectrum and power of each sub bands including delta (1-4 Hz), Theta (4-8 Hz), alpha (8-12 Hz), beta (12-28 Hz), gamma (28-40 Hz) were calculated with MATLAB 2013b. Accordingly, all results were obtained quantitatively which can contribute to reduce the errors in the behavioral assessments.

  12. Transfer function between EEG and BOLD signals of epileptic activity

    Directory of Open Access Journals (Sweden)

    Marco eLeite

    2013-01-01

    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.

  13. Mutual information measures applied to EEG signals for sleepiness characterization

    OpenAIRE

    Melia, Umberto Sergio Pio; Guaita, Marc; Vallverdú Ferrer, Montserrat; Embid, Cristina; Vilaseca, I; Salamero, Manuel; Santamaria, Joan

    2015-01-01

    Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders with a great impact on the patient lives. While many studies have been carried out in order to assess daytime sleepiness, the automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on non-linear dynamical analysis of EEG signal was proposed. Multichannel EEG signals were recorded during five maintenance of wakefulness (MWT) and multiple sleep lat...

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

    Science.gov (United States)

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

    2015-01-01

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

  15. ECG contamination of EEG signals: effect on entropy.

    Science.gov (United States)

    Chakrabarti, Dhritiman; Bansal, Sonia

    2016-02-01

    Entropy™ is a proprietary algorithm which uses spectral entropy analysis of electroencephalographic (EEG) signals to produce indices which are used as a measure of depth of hypnosis. We describe a report of electrocardiographic (ECG) contamination of EEG signals leading to fluctuating erroneous Entropy values. An explanation is provided for mechanism behind this observation by describing the spread of ECG signals in head and neck and its influence on EEG/Entropy by correlating the observation with the published Entropy algorithm. While the Entropy algorithm has been well conceived, there are still instances in which it can produce erroneous values. Such erroneous values and their cause may be identified by close scrutiny of the EEG waveform if Entropy values seem out of sync with that expected at given anaesthetic levels.

  16. Executed Movement Using EEG Signals through a Naive Bayes Classifier

    Directory of Open Access Journals (Sweden)

    Juliano Machado

    2014-11-01

    Full Text Available Recent years have witnessed a rapid development of brain-computer interface (BCI technology. An independent BCI is a communication system for controlling a device by human intension, e.g., a computer, a wheelchair or a neuroprosthes is, not depending on the brain’s normal output pathways of peripheral nerves and muscles, but on detectable signals that represent responsive or intentional brain activities. This paper presents a comparative study of the usage of the linear discriminant analysis (LDA and the naive Bayes (NB classifiers on describing both right- and left-hand movement through electroencephalographic signal (EEG acquisition. For the analysis, we considered the following input features: the energy of the segments of a band pass-filtered signal with the frequency band in sensorimotor rhythms and the components of the spectral energy obtained through the Welch method. We also used the common spatial pattern (CSP filter, so as to increase the discriminatory activity among movement classes. By using the database generated by this experiment, we obtained hit rates up to 70%. The results are compatible with previous studies.

  17. Study on non-linear bistable dynamics model based EEG signal discrimination analysis method.

    Science.gov (United States)

    Ying, Xiaoguo; Lin, Han; Hui, Guohua

    2015-01-01

    Electroencephalogram (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations generating from ionic current flows within the neurons of the brain. EEG signal is looked as one of the most important factors that will be focused in the next 20 years. In this paper, EEG signal discrimination based on non-linear bistable dynamical model was proposed. EEG signals were processed by non-linear bistable dynamical model, and features of EEG signals were characterized by coherence index. Experimental results showed that the proposed method could properly extract the features of different EEG signals.

  18. Corrected Four-Sphere Head Model for EEG Signals.

    Science.gov (United States)

    Næss, Solveig; Chintaluri, Chaitanya; Ness, Torbjørn V; Dale, Anders M; Einevoll, Gaute T; Wójcik, Daniel K

    2017-01-01

    The EEG signal is generated by electrical brain cell activity, often described in terms of current dipoles. By applying EEG forward models we can compute the contribution from such dipoles to the electrical potential recorded by EEG electrodes. Forward models are key both for generating understanding and intuition about the neural origin of EEG signals as well as inverse modeling, i.e., the estimation of the underlying dipole sources from recorded EEG signals. Different models of varying complexity and biological detail are used in the field. One such analytical model is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF), skull, and scalp, respectively. While conceptually clear, the mathematical expression for the electric potentials in the four-sphere model is cumbersome, and we observed that the formulas presented in the literature contain errors. Here, we derive and present the correct analytical formulas with a detailed derivation. A useful application of the analytical four-sphere model is that it can serve as ground truth to test the accuracy of numerical schemes such as the Finite Element Method (FEM). We performed FEM simulations of the four-sphere head model and showed that they were consistent with the corrected analytical formulas. For future reference we provide scripts for computing EEG potentials with the four-sphere model, both by means of the correct analytical formulas and numerical FEM simulations.

  19. Corrected Four-Sphere Head Model for EEG Signals

    Directory of Open Access Journals (Sweden)

    Solveig Næss

    2017-10-01

    Full Text Available The EEG signal is generated by electrical brain cell activity, often described in terms of current dipoles. By applying EEG forward models we can compute the contribution from such dipoles to the electrical potential recorded by EEG electrodes. Forward models are key both for generating understanding and intuition about the neural origin of EEG signals as well as inverse modeling, i.e., the estimation of the underlying dipole sources from recorded EEG signals. Different models of varying complexity and biological detail are used in the field. One such analytical model is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF, skull, and scalp, respectively. While conceptually clear, the mathematical expression for the electric potentials in the four-sphere model is cumbersome, and we observed that the formulas presented in the literature contain errors. Here, we derive and present the correct analytical formulas with a detailed derivation. A useful application of the analytical four-sphere model is that it can serve as ground truth to test the accuracy of numerical schemes such as the Finite Element Method (FEM. We performed FEM simulations of the four-sphere head model and showed that they were consistent with the corrected analytical formulas. For future reference we provide scripts for computing EEG potentials with the four-sphere model, both by means of the correct analytical formulas and numerical FEM simulations.

  20. Quantitative change of EEG and respiration signals during mindfulness meditation

    Science.gov (United States)

    2014-01-01

    Background This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing. Methods EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation. Results Different frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%). Conclusion Support vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies. PMID:24939519

  1. Modulation of EEG Theta Band Signal Complexity by Music Therapy

    Science.gov (United States)

    Bhattacharya, Joydeep; Lee, Eun-Jeong

    The primary goal of this study was to investigate the impact of monochord (MC) sounds, a type of archaic sounds used in music therapy, on the neural complexity of EEG signals obtained from patients undergoing chemotherapy. The secondary goal was to compare the EEG signal complexity values for monochords with those for progressive muscle relaxation (PMR), an alternative therapy for relaxation. Forty cancer patients were randomly allocated to one of the two relaxation groups, MC and PMR, over a period of six months; continuous EEG signals were recorded during the first and last sessions. EEG signals were analyzed by applying signal mode complexity, a measure of complexity of neuronal oscillations. Across sessions, both groups showed a modulation of complexity of beta-2 band (20-29Hz) at midfrontal regions, but only MC group showed a modulation of complexity of theta band (3.5-7.5Hz) at posterior regions. Therefore, the neuronal complexity patterns showed different changes in EEG frequency band specific complexity resulting in two different types of interventions. Moreover, the different neural responses to listening to monochords and PMR were observed after regular relaxation interventions over a short time span.

  2. Attenuation of artifacts in EEG signals measured inside an MRI scanner using constrained independent component analysis

    International Nuclear Information System (INIS)

    Rasheed, Tahir; Lee, Young-Koo; Lee, Soo Yeol; Kim, Tae-Seong

    2009-01-01

    Integration of electroencephalography (EEG) and functional magnetic imaging (fMRI) resonance will allow analysis of the brain activities at superior temporal and spatial resolution. However simultaneous acquisition of EEG and fMRI is hindered by the enhancement of artifacts in EEG, the most prominent of which are ballistocardiogram (BCG) and electro-oculogram (EOG) artifacts. The situation gets even worse if the evoked potentials are measured inside MRI for their minute responses in comparison to the spontaneous brain responses. In this study, we propose a new method of attenuating these artifacts from the spontaneous and evoked EEG data acquired inside an MRI scanner using constrained independent component analysis with a priori information about the artifacts as constraints. With the proposed techniques of reference function generation for the BCG and EOG artifacts as constraints, our new approach performs significantly better than the averaged artifact subtraction (AAS) method. The proposed method could be an alternative to the conventional ICA method for artifact attenuation, with some advantages. As a performance measure we have achieved much improved normalized power spectrum ratios (INPS) for continuous EEG and correlation coefficient (cc) values with outside MRI visual evoked potentials for visual evoked EEG, as compared to those obtained with the AAS method. The results show that our new approach is more effective than the conventional methods, almost fully automatic, and no extra ECG signal measurements are involved

  3. Classifying Drivers' Cognitive Load Using EEG Signals.

    Science.gov (United States)

    Barua, Shaibal; Ahmed, Mobyen Uddin; Begum, Shahina

    2017-01-01

    A growing traffic safety issue is the effect of cognitive loading activities on traffic safety and driving performance. To monitor drivers' mental state, understanding cognitive load is important since while driving, performing cognitively loading secondary tasks, for example talking on the phone, can affect the performance in the primary task, i.e. driving. Electroencephalography (EEG) is one of the reliable measures of cognitive load that can detect the changes in instantaneous load and effect of cognitively loading secondary task. In this driving simulator study, 1-back task is carried out while the driver performs three different simulated driving scenarios. This paper presents an EEG based approach to classify a drivers' level of cognitive load using Case-Based Reasoning (CBR). The results show that for each individual scenario as well as using data combined from the different scenarios, CBR based system achieved approximately over 70% of classification accuracy.

  4. Mutual information measures applied to EEG signals for sleepiness characterization.

    Science.gov (United States)

    Melia, Umberto; Guaita, Marc; Vallverdú, Montserrat; Embid, Cristina; Vilaseca, Isabel; Salamero, Manel; Santamaria, Joan

    2015-03-01

    Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders with a great impact on the patient lives. While many studies have been carried out in order to assess daytime sleepiness, the automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on non-linear dynamical analysis of EEG signal was proposed. Multichannel EEG signals were recorded during five maintenance of wakefulness (MWT) and multiple sleep latency (MSLT) tests alternated throughout the day from patients suffering from sleep disordered breathing. A group of 20 patients with excessive daytime sleepiness (EDS) was compared with a group of 20 patients without daytime sleepiness (WDS), by analyzing 60-s EEG windows in waking state. Measures obtained from cross-mutual information function (CMIF) and auto-mutual-information function (AMIF) were calculated in the EEG. These functions permitted a quantification of the complexity properties of the EEG signal and the non-linear couplings between different zones of the scalp. Statistical differences between EDS and WDS groups were found in β band during MSLT events (p-value CMIF measures yielded sensitivity and specificity above 80% and AUC of ROC above 0.85 in classifying EDS and WDS patients. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

  5. Artifact removal from EEG signals using adaptive filters in cascade

    Science.gov (United States)

    Garcés Correa, A.; Laciar, E.; Patiño, H. D.; Valentinuzzi, M. E.

    2007-11-01

    Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records.

  6. Artifact removal from EEG signals using adaptive filters in cascade

    International Nuclear Information System (INIS)

    Garces Correa, A; Laciar, E; Patino, H D; Valentinuzzi, M E

    2007-01-01

    Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records

  7. Artifact removal from EEG signals using adaptive filters in cascade

    Energy Technology Data Exchange (ETDEWEB)

    Garces Correa, A [Gabinete de TecnologIa Medica, Facultad de Ingenieria, Universidad Nacional de San Juan (Argentina); Laciar, E [Gabinete de TecnologIa Medica, Facultad de Ingenieria, Universidad Nacional de San Juan (Argentina); Patino, H D [Instituto de Automatica, Facultad de Ingenieria, Universidad Nacional de San Juan (Argentina); Valentinuzzi, M E [Instituto Superior de Investigaciones Biologicas (INSIBIO), UNT-CONICET, Tucuman (Argentina)

    2007-11-15

    Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records.

  8. Viability of Controlling Prosthetic Hand Utilizing Electroencephalograph (EEG) Dataset Signal

    Science.gov (United States)

    Miskon, Azizi; A/L Thanakodi, Suresh; Raihan Mazlan, Mohd; Mohd Haziq Azhar, Satria; Nooraya Mohd Tawil, Siti

    2016-11-01

    This project presents the development of an artificial hand controlled by Electroencephalograph (EEG) signal datasets for the prosthetic application. The EEG signal datasets were used as to improvise the way to control the prosthetic hand compared to the Electromyograph (EMG). The EMG has disadvantages to a person, who has not used the muscle for a long time and also to person with degenerative issues due to age factor. Thus, the EEG datasets found to be an alternative for EMG. The datasets used in this work were taken from Brain Computer Interface (BCI) Project. The datasets were already classified for open, close and combined movement operations. It served the purpose as an input to control the prosthetic hand by using an Interface system between Microsoft Visual Studio and Arduino. The obtained results reveal the prosthetic hand to be more efficient and faster in response to the EEG datasets with an additional LiPo (Lithium Polymer) battery attached to the prosthetic. Some limitations were also identified in terms of the hand movements, weight of the prosthetic, and the suggestions to improve were concluded in this paper. Overall, the objective of this paper were achieved when the prosthetic hand found to be feasible in operation utilizing the EEG datasets.

  9. Continuous emotion detection using EEG signals and facial expressions

    NARCIS (Netherlands)

    Soleymani, Mohammad; Asghari-Esfeden, Sadjad; Pantic, Maja; Fu, Yun

    Emotions play an important role in how we select and consume multimedia. Recent advances on affect detection are focused on detecting emotions continuously. In this paper, for the first time, we continuously detect valence from electroencephalogram (EEG) signals and facial expressions in response to

  10. Seizure classification in EEG signals utilizing Hilbert-Huang transform

    OpenAIRE

    Oweis, Rami J; Abdulhay, Enas W

    2011-01-01

    Abstract Background Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Method Discrimination in this ...

  11. A review of channel selection algorithms for EEG signal processing

    Science.gov (United States)

    Alotaiby, Turky; El-Samie, Fathi E. Abd; Alshebeili, Saleh A.; Ahmad, Ishtiaq

    2015-12-01

    Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.

  12. Mushu, a free- and open source BCI signal acquisition, written in Python.

    Science.gov (United States)

    Venthur, Bastian; Blankertz, Benjamin

    2012-01-01

    The following paper describes Mushu, a signal acquisition software for retrieval and online streaming of Electroencephalography (EEG) data. It is written, but not limited, to the needs of Brain Computer Interfacing (BCI). It's main goal is to provide a unified interface to EEG data regardless of the amplifiers used. It runs under all major operating systems, like Windows, Mac OS and Linux, is written in Python and is free- and open source software licensed under the terms of the GNU General Public License.

  13. Seizure classification in EEG signals utilizing Hilbert-Huang transform

    Directory of Open Access Journals (Sweden)

    Abdulhay Enas W

    2011-05-01

    Full Text Available Abstract Background Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG signals. Method Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. Results The t-test results in a P-value Conclusion An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

  14. Seizure classification in EEG signals utilizing Hilbert-Huang transform.

    Science.gov (United States)

    Oweis, Rami J; Abdulhay, Enas W

    2011-05-24

    Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals. Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering. The t-test results in a P-value with respect to its fast response and ease to use. An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.

  15. EEG-Annotate: Automated identification and labeling of events in continuous signals with applications to EEG.

    Science.gov (United States)

    Su, Kyung-Min; Hairston, W David; Robbins, Kay

    2018-01-01

    In controlled laboratory EEG experiments, researchers carefully mark events and analyze subject responses time-locked to these events. Unfortunately, such markers may not be available or may come with poor timing resolution for experiments conducted in less-controlled naturalistic environments. We present an integrated event-identification method for identifying particular responses that occur in unlabeled continuously recorded EEG signals based on information from recordings of other subjects potentially performing related tasks. We introduce the idea of timing slack and timing-tolerant performance measures to deal with jitter inherent in such non-time-locked systems. We have developed an implementation available as an open-source MATLAB toolbox (http://github.com/VisLab/EEG-Annotate) and have made test data available in a separate data note. We applied the method to identify visual presentation events (both target and non-target) in data from an unlabeled subject using labeled data from other subjects with good sensitivity and specificity. The method also identified actual visual presentation events in the data that were not previously marked in the experiment. Although the method uses traditional classifiers for initial stages, the problem of identifying events based on the presence of stereotypical EEG responses is the converse of the traditional stimulus-response paradigm and has not been addressed in its current form. In addition to identifying potential events in unlabeled or incompletely labeled EEG, these methods also allow researchers to investigate whether particular stereotypical neural responses are present in other circumstances. Timing-tolerance has the added benefit of accommodating inter- and intra- subject timing variations. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

  16. Toward a fully integrated wireless wearable EEG-NIRS bimodal acquisition system.

    Science.gov (United States)

    Safaie, J; Grebe, R; Abrishami Moghaddam, H; Wallois, F

    2013-10-01

    Interactions between neuronal electrical activity and regional changes in microcirculation are assumed to play a major role in physiological brain activity and the development of pathological disorders, but have been poorly elucidated to date. There is a need for advanced diagnostic tools to investigate the relationships between these two physiological processes. To meet these needs, a wireless wearable system has been developed, which combines a near infrared spectroscopy (NIRS) system using light emitting diodes (LEDs) as a light source and silicon photodiodes as a detector with an integrated electroencephalography (EEG) system. The main advantages over currently available devices are miniaturization and integration of a real-time electrical and hemodynamic activity monitor into one wearable device. For patient distributed monitoring and creating a body-area network, up to seven same devices can be connected to a single base station (PC) synchronously. Each node presents enhanced portability due to the wireless communication and highly integrated components resulting in a small, lightweight signal acquisition device. Further progress includes the individual control of LEDs output to automatically or interactively adjust emitted light to the actual local situation online, the use of silicon photodiodes with a safe low-voltage power supply, and an integrated three dimensional accelerometer for movement detection for the identification of motion artifacts. The device was tested and validated using our enhanced EEG-NIRS tissue mimicking fluid phantom for sensitivity mapping. Typical somatotopic electrical evoked potential experiments were performed to verify clinical applicability.

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

    Directory of Open Access Journals (Sweden)

    Andreas Pinegger

    2016-09-01

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

  18. Toward a fully integrated wireless wearable EEG-NIRS bimodal acquisition system

    Science.gov (United States)

    Safaie, J.; Grebe, R.; Abrishami Moghaddam, H.; Wallois, F.

    2013-10-01

    Objective. Interactions between neuronal electrical activity and regional changes in microcirculation are assumed to play a major role in physiological brain activity and the development of pathological disorders, but have been poorly elucidated to date. There is a need for advanced diagnostic tools to investigate the relationships between these two physiological processes.Approach. To meet these needs, a wireless wearable system has been developed, which combines a near infrared spectroscopy (NIRS) system using light emitting diodes (LEDs) as a light source and silicon photodiodes as a detector with an integrated electroencephalography (EEG) system. Main results. The main advantages over currently available devices are miniaturization and integration of a real-time electrical and hemodynamic activity monitor into one wearable device. For patient distributed monitoring and creating a body-area network, up to seven same devices can be connected to a single base station (PC) synchronously. Each node presents enhanced portability due to the wireless communication and highly integrated components resulting in a small, lightweight signal acquisition device. Further progress includes the individual control of LEDs output to automatically or interactively adjust emitted light to the actual local situation online, the use of silicon photodiodes with a safe low-voltage power supply, and an integrated three dimensional accelerometer for movement detection for the identification of motion artifacts. Significance. The device was tested and validated using our enhanced EEG-NIRS tissue mimicking fluid phantom for sensitivity mapping. Typical somatotopic electrical evoked potential experiments were performed to verify clinical applicability.

  19. Hyperspherical Manifold for EEG Signals of Epileptic Seizures

    Directory of Open Access Journals (Sweden)

    Tahir Ahmad

    2012-01-01

    Full Text Available The mathematical modelling of EEG signals of epileptic seizures presents a challenge as seizure data is erratic, often with no visible trend. Limitations in existing models indicate a need for a generalized model that can be used to analyze seizures without the need for apriori information, whilst minimizing the loss of signal data due to smoothing. This paper utilizes measure theory to design a discrete probability measure that reformats EEG data without altering its geometric structure. An analysis of EEG data from three patients experiencing epileptic seizures is made using the developed measure, resulting in successful identification of increased potential difference in portions of the brain that correspond to physical symptoms demonstrated by the patients. A mapping then is devised to transport the measure data onto the surface of a high-dimensional manifold, enabling the analysis of seizures using directional statistics and manifold theory. The subset of seizure signals on the manifold is shown to be a topological space, verifying Ahmad's approach to use topological modelling.

  20. A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies.

    Science.gov (United States)

    Puce, Aina; Hämäläinen, Matti S

    2017-05-31

    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.

  1. A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies

    Directory of Open Access Journals (Sweden)

    Aina Puce

    2017-05-01

    Full Text Available 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.

  2. An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods

    Directory of Open Access Journals (Sweden)

    Arjon Turnip

    2014-12-01

    Full Text Available In the modern world of automation, biological signals, especially Electroencephalogram (EEG is gaining wide attention as a source of biometric information. Eye-blinks and movement of the eyeballs produce electrical signals (contaminate the EEG signals that are collectively known as ocular artifacts. These noise signals are required to be separated from the EEG signals to obtain the accurate results. This paper reports an experiment of ocular artifacts elimination from EEG signal using blind source separation algorithm based on independent component analysis and principal component analysis. EEG signals are recorded on three conditions, which are normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of EEG signals in the range of 12-14 Hz either on normal, closed, and blinked eyes conditions is obtained. 

  3. Correntropy measures to detect daytime sleepiness from EEG signals

    International Nuclear Information System (INIS)

    Melia, Umberto; Vallverdú, Montserrat; Caminal, Pere; Guaita, Marc; Montserrat, Josep M; Vilaseca, Isabel; Salamero, Manel; Gaig, Carles; Santamaria, Joan

    2014-01-01

    Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders and has a great impact on patients’ lives. While many studies have been carried out in order to assess daytime sleepiness, automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on correntropy function analysis of EEG signals was proposed in order to detect patients suffering from EDS. Multichannel EEG signals were recorded during five Maintenance of Wakefulness Tests (MWT) and Multiple Sleep Latency Tests (MSLT) alternated throughout the day for patients suffering from sleep disordered breathing (SDB). A group of 20 patients with EDS was compared with a group of 20 patients without daytime sleepiness (WDS), by analyzing 60 s EEG windows in a waking state. Measures obtained from the cross-correntropy function (CCORR) and auto-correntropy function (ACORR) were calculated in the EEG frequency bands: δ, 0.1–4 Hz; θ, 4–8 Hz; α, 8–12 Hz; β, 12–30 Hz; total band TB, 0.1–45 Hz. These functions permitted the quantification of complex signal properties and the non-linear couplings between different areas of the scalp. Statistical differences between EDS and WDS groups were mainly found in the β band during MSLT events (p-value < 0.0001). The WDS group presented more complexity in the occipital zone than the EDS group, while a stronger nonlinear coupling between the occipital and frontal regions was detected in EDS patients than in the WDS group. At best, ACORR and CCORR measures yielded sensitivity and specificity above 80% and the area under ROC curve (AUC) was above 0.85 in classifying EDS and WDS patients. These performances represent an improvement with respect to classical EEG indices applied in the same database (sensitivity and specificity were never above 80% and AUC was under 0.75). (paper)

  4. Continuous EEG signal analysis for asynchronous BCI application.

    Science.gov (United States)

    Hsu, Wei-Yen

    2011-08-01

    In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.

  5. Compressive sensing scalp EEG signals: implementations and practical performance.

    Science.gov (United States)

    Abdulghani, Amir M; Casson, Alexander J; Rodriguez-Villegas, Esther

    2012-11-01

    Highly miniaturised, wearable computing and communication systems allow unobtrusive, convenient and long term monitoring of a range of physiological parameters. For long term operation from the physically smallest batteries, the average power consumption of a wearable device must be very low. It is well known that the overall power consumption of these devices can be reduced by the inclusion of low power consumption, real-time compression of the raw physiological data in the wearable device itself. Compressive sensing is a new paradigm for providing data compression: it has shown significant promise in fields such as MRI; and is potentially suitable for use in wearable computing systems as the compression process required in the wearable device has a low computational complexity. However, the practical performance very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Long term electroencephalography (EEG) is a fundamental tool for the investigation of neurological disorders and is increasingly used in many non-medical applications, such as brain-computer interfaces. This article investigates in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals.

  6. Experiments on Classification of Electroencephalography (EEG Signals in Imagination of Direction using Stacked Autoencoder

    Directory of Open Access Journals (Sweden)

    Kenta Tomonaga

    2017-08-01

    Full Text Available This paper presents classification methods for electroencephalography (EEG signals in imagination of direction measured by a portable EEG headset. In the authorsr previous studies, principal component analysis extracted significant features from EEG signals to construct neural network classifiers. To improve the performance, the authors have implemented a Stacked Autoencoder (SAE for the classification. The SAE carries out feature extraction and classification in a form of multi-layered neural network. Experimental results showed that the SAE outperformed the previous classifiers.

  7. Association between increased EEG signal complexity and cannabis dependence.

    Science.gov (United States)

    Laprevote, Vincent; Bon, Laura; Krieg, Julien; Schwitzer, Thomas; Bourion-Bedes, Stéphanie; Maillard, Louis; Schwan, Raymund

    2017-12-01

    Both acute and regular cannabis use affects the functioning of the brain. While several studies have demonstrated that regular cannabis use can impair the capacity to synchronize neural assemblies during specific tasks, less is known about spontaneous brain activity. This can be explored by measuring EEG complexity, which reflects the spontaneous variability of human brain activity. A recent study has shown that acute cannabis use can affect that complexity. Since the characteristics of cannabis use can affect the impact on brain functioning, this study sets out to measure EEG complexity in regular cannabis users with or without dependence, in comparison with healthy controls. We recruited 26 healthy controls, 25 cannabis users without cannabis dependence and 14 cannabis users with cannabis dependence, based on DSM IV TR criteria. The EEG signal was extracted from at least 250 epochs of the 500ms pre-stimulation phase during a visual evoked potential paradigm. Brain complexity was estimated using Lempel-Ziv Complexity (LZC), which was compared across groups by non-parametric Kruskall-Wallis ANOVA. The analysis revealed a significant difference between the groups, with higher LZC in participants with cannabis dependence than in non-dependent cannabis users. There was no specific localization of this effect across electrodes. We showed that cannabis dependence is associated to an increased spontaneous brain complexity in regular users. This result is in line with previous results in acute cannabis users. It may reflect increased randomness of neural activity in cannabis dependence. Future studies should explore whether this effect is permanent or diminishes with cannabis cessation. Copyright © 2017 Elsevier B.V. and ECNP. All rights reserved.

  8. Spatio-temporal coupling of EEG signals in epilepsy

    Science.gov (United States)

    Senger, Vanessa; Müller, Jens; Tetzlaff, Ronald

    2011-05-01

    Approximately 1% of the world's population suffer from epileptic seizures throughout their lives that mostly come without sign or warning. Thus, epilepsy is the most common chronical disorder of the neurological system. In the past decades, the problem of detecting a pre-seizure state in epilepsy using EEG signals has been addressed in many contributions by various authors over the past two decades. Up to now, the goal of identifying an impending epileptic seizure with sufficient specificity and reliability has not yet been achieved. Cellular Nonlinear Networks (CNN) are characterized by local couplings of dynamical systems of comparably low complexity. Thus, they are well suited for an implementation as highly parallel analogue processors. Programmable sensor-processor realizations of CNN combine high computational power comparable to tera ops of digital processors with low power consumption. An algorithm allowing an automated and reliable detection of epileptic seizure precursors would be a"huge step" towards the vision of an implantable seizure warning device that could provide information to patients and for a time/event specific treatment directly in the brain. Recent contributions have shown that modeling of brain electrical activity by solutions of Reaction-Diffusion-CNN as well as the application of a CNN predictor taking into account values of neighboring electrodes may contribute to the realization of a seizure warning device. In this paper, a CNN based predictor corresponding to a spatio-temporal filter is applied to multi channel EEG data in order to identify mutual couplings for different channels which lead to a enhanced prediction quality. Long term EEG recordings of different patients are considered. Results calculated for these recordings with inter-ictal phases as well as phases with seizures will be discussed in detail.

  9. Compressive Sampling of EEG Signals with Finite Rate of Innovation

    Directory of Open Access Journals (Sweden)

    Poh Kok-Kiong

    2010-01-01

    Full Text Available Analyses of electroencephalographic signals and subsequent diagnoses can only be done effectively on long term recordings that preserve the signals' morphologies. Currently, electroencephalographic signals are obtained at Nyquist rate or higher, thus introducing redundancies. Existing compression methods remove these redundancies, thereby achieving compression. We propose an alternative compression scheme based on a sampling theory developed for signals with a finite rate of innovation (FRI which compresses electroencephalographic signals during acquisition. We model the signals as FRI signals and then sample them at their rate of innovation. The signals are thus effectively represented by a small set of Fourier coefficients corresponding to the signals' rate of innovation. Using the FRI theory, original signals can be reconstructed using this set of coefficients. Seventy-two hours of electroencephalographic recording are tested and results based on metrices used in compression literature and morphological similarities of electroencephalographic signals are presented. The proposed method achieves results comparable to that of wavelet compression methods, achieving low reconstruction errors while preserving the morphologiies of the signals. More importantly, it introduces a new framework to acquire electroencephalographic signals at their rate of innovation, thus entailing a less costly low-rate sampling device that does not waste precious computational resources.

  10. Unique portable signal acquisition/processing station

    International Nuclear Information System (INIS)

    Garron, R.D.; Azevedo, S.G.

    1983-01-01

    At Lawrence Livermore National Laboratory, there are experimental applications requiring digital signal acquisition as well as data reduction and analysis. A prototype Signal Acquisition/Processing Station (SAPS) has been constructed and is currently undergoing tests. The system employs an LSI-11/23 computer with Data Translation analog-to-digital hardware. SAPS is housed in a roll-around cart which has been designed to withstand most subtle EMI/RFI environments. A user-friendly menu allows a user to access powerful data acquisition packages with a minimum of training. The software architecture of SAPS involves two operating systems, each being transparent to the user. Since this is a general purpose workstation with several units being utilized, an emphasis on low cost, reliability, and maintenance was stressed during conception and design. The system is targeted for mid-range frequency data acquisition; between a data logger and a transient digitizer

  11. Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients

    Science.gov (United States)

    Lahmiri, Salim

    2018-01-01

    The aim of our current study is to check whether multifractal patterns of the electroencephalographic (EEG) signals of normal and epileptic patients are statistically similar or different. In this regard, the generalized Hurst exponent (GHE) method is used for robust estimation of the multifractals in each type of EEG signals, and three powerful statistical tests are performed to check existence of differences between estimated GHEs from healthy control subjects and epileptic patients. The obtained results show that multifractals exist in both types of EEG signals. Particularly, it was found that the degree of fractal is more pronounced in short variations of normal EEG signals than in short variations of EEG signals with seizure free intervals. In contrary, it is more pronounced in long variations of EEG signals with seizure free intervals than in normal EEG signals. Importantly, both parametric and nonparametric statistical tests show strong evidence that estimated GHEs of normal EEG signals are statistically and significantly different from those with seizure free intervals. Therefore, GHEs can be efficiently used to distinguish between healthy and patients suffering from epilepsy.

  12. Resonance detection of EEG signals using two-layer wavelet analysis

    International Nuclear Information System (INIS)

    Abdallah, H. M; Odeh, F.S.

    2000-01-01

    This paper presents the hybrid quadrature mirror filter (HQMF) algorithm applied to the electroencephalogram (EEG) signal during mental activity. The information contents of this signal, i.e., its medical diagnosis, lie in its power spectral density (PSD). The HQMF algorithm is a modified technique that is based on the shape and the details of the signal. If applied efficiently, the HQMF algorithm will produce much better results than conventional wavelet methods in detecting (diagnosing) the information of the EEG signal from its PSD. This technique is applicable not only to EEG signals, but is highly recommended to compression analysis and de noising techniques. (authors). 16 refs., 9 figs

  13. EEG

    Science.gov (United States)

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

  14. Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal

    OpenAIRE

    Hamidreza Namazi; Amin Akrami; Sina Nazeri; Vladimir V. Kulish

    2016-01-01

    An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally ...

  15. [A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

    Science.gov (United States)

    Zhang, Y; Liu, A; Yu, K

    1999-06-01

    A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

  16. Brain Network Analysis from High-Resolution EEG Signals

    Science.gov (United States)

    de Vico Fallani, Fabrizio; Babiloni, Fabio

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

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

    Science.gov (United States)

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

    2016-09-01

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

  18. EEG Signal Quality of a Subcutaneous Recording System Compared to Standard Surface Electrodes

    Directory of Open Access Journals (Sweden)

    Jonas Duun-Henriksen

    2015-01-01

    Full Text Available Purpose. We provide a comprehensive verification of a new subcutaneous EEG recording device which promises robust and unobtrusive measurements over ultra-long time periods. The approach is evaluated against a state-of-the-art surface EEG electrode technology. Materials and Methods. An electrode powered by an inductive link was subcutaneously implanted on five subjects. Surface electrodes were placed at sites corresponding to the subcutaneous electrodes, and the EEG signals were evaluated with both quantitative (power spectral density and coherence analysis and qualitative (blinded subjective scoring by neurophysiologists analysis. Results. The power spectral density and coherence analysis were very similar during measurements of resting EEG. The scoring by neurophysiologists showed a higher EEG quality for the implanted system for different subject states (eyes open and eyes closed. This was most likely due to higher amplitude of the subcutaneous signals. During periods with artifacts, such as chewing, blinking, and eye movement, the two systems performed equally well. Conclusions. Subcutaneous measurements of EEG with the test device showed high quality as measured by both quantitative and more subjective qualitative methods. The signal might be superior to surface EEG in some aspects and provides a method of ultra-long term EEG recording in situations where this is required and where a small number of EEG electrodes are sufficient.

  19. M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition Architecture for Miniaturized EEG-NIRS-Based Hybrid BCI and Monitoring.

    Science.gov (United States)

    von Luhmann, Alexander; Wabnitz, Heidrun; Sander, Tilmann; Muller, Klaus-Robert

    2017-06-01

    For the further development of the fields of telemedicine, neurotechnology, and brain-computer interfaces, advances in hybrid multimodal signal acquisition and processing technology are invaluable. Currently, there are no commonly available hybrid devices combining bioelectrical and biooptical neurophysiological measurements [here electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS)]. Our objective was to design such an instrument in a miniaturized, customizable, and wireless form. We present here the design and evaluation of a mobile, modular, multimodal biosignal acquisition architecture (M3BA) based on a high-performance analog front-end optimized for biopotential acquisition, a microcontroller, and our openNIRS technology. The designed M3BA modules are very small configurable high-precision and low-noise modules (EEG input referred noise @ 500 SPS 1.39 μV pp , NIRS noise equivalent power NEP 750 nm = 5.92 pW pp , and NEP 850 nm = 4.77 pW pp ) with full input linearity, Bluetooth, 3-D accelerometer, and low power consumption. They support flexible user-specified biopotential reference setups and wireless body area/sensor network scenarios. Performance characterization and in-vivo experiments confirmed functionality and quality of the designed architecture. Telemedicine and assistive neurotechnology scenarios will increasingly include wearable multimodal sensors in the future. The M3BA architecture can significantly facilitate future designs for research in these and other fields that rely on customized mobile hybrid biosignal modal biosignal acquisition architecture (M3BA), multimodal, near-infrared spectroscopy (NIRS), wireless body area network (WBAN), wireless body sensor network (WBSN).

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Sarah Blum

    2017-01-01

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

  3. EEG Signal Decomposition and Improved Spectral Analysis Using Wavelet Transform

    National Research Council Canada - National Science Library

    Bhatti, Muhammad

    2001-01-01

    EEG (Electroencephalograph), as a noninvasive testing method, plays a key role in the diagnosing diseases, and is useful for both physiological research and medical applications. Wavelet transform (WT...

  4. Subdural to subgaleal EEG signal transmission: The role of distance, leakage and insulating affectors

    DEFF Research Database (Denmark)

    Duun-Henriksen, Jonas; Kjaer, Troels Wesenberg; Madsen, Rasmus Elsborg

    2013-01-01

    Objective To estimate the area of cortex affecting the extracranial EEG signal. MethodsThe coherence between intra- and extracranial EEG channels were evaluated on at least 10min of spontaneous, awake data from seven patients admitted for epilepsy surgery work up. Results Cortical electrodes show...

  5. EEG biofeedback

    OpenAIRE

    Dvořáček, Michael

    2010-01-01

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

  6. Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm

    Directory of Open Access Journals (Sweden)

    E. Parvinnia

    2014-01-01

    Full Text Available Electroencephalogram (EEG signals are often used to diagnose diseases such as seizure, alzheimer, and schizophrenia. One main problem with the recorded EEG samples is that they are not equally reliable due to the artifacts at the time of recording. EEG signal classification algorithms should have a mechanism to handle this issue. It seems that using adaptive classifiers can be useful for the biological signals such as EEG. In this paper, a general adaptive method named weighted distance nearest neighbor (WDNN is applied for EEG signal classification to tackle this problem. This classification algorithm assigns a weight to each training sample to control its influence in classifying test samples. The weights of training samples are used to find the nearest neighbor of an input query pattern. To assess the performance of this scheme, EEG signals of thirteen schizophrenic patients and eighteen normal subjects are analyzed for the classification of these two groups. Several features including, fractal dimension, band power and autoregressive (AR model are extracted from EEG signals. The classification results are evaluated using Leave one (subject out cross validation for reliable estimation. The results indicate that combination of WDNN and selected features can significantly outperform the basic nearest-neighbor and the other methods proposed in the past for the classification of these two groups. Therefore, this method can be a complementary tool for specialists to distinguish schizophrenia disorder.

  7. EEG

    African Journals Online (AJOL)

    2017-09-03

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

  8. A portable, differential amplifier for recording high frequency EEG signals and evoked potentials

    International Nuclear Information System (INIS)

    Donos, Cristian; Giurgiu, Liviu; Popescu, Aurel; Mocanu, Marian

    2010-01-01

    In a clinical context, EEG refers to recording the brain's spontaneous electric activity, using small electrodes placed on the scalp. The signals collected are electric 'potentials' measured between two electrodes. Usually, for a healthy adult, these signals have small voltage (10 μV to 100 μV) and frequencies in the 0-40 Hz range. In the scientific literature, there are mentioned EEG signals and evoked potentials that have higher frequencies (up to 600 Hz) and amplitudes lower than 500 ηV. For this reason, building an amplifier capable of recording EEG signals in the ηV range and with frequencies up to couple of kHz is necessary to continue research beyond 600 Hz. We designed a very low noise amplifier that is able to measure/record EEG signals in the ηV range over a very large frequency bandwidth (0.09 Hz -385 kHz).(Author)

  9. Analysis of Small Muscle Movement Effects on EEG Signals

    Science.gov (United States)

    2016-12-22

    different conditions are recorded in this experiment. These conditions are the resting state, left finger keyboard press, right finger keyboard...51 4.3.2. Right and Left Finger Keyboard Press Conditions ..................................... 57 4.4. Detection of Hand...solving Gamma 30 Hz and higher Blending of multiple brain functions ; Muscle related artifacts 2.2. EEG Artifacts EEG recordings are intended to

  10. Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement

    Science.gov (United States)

    Kim, Kyungsoo; Lim, Sung-Ho; Lee, Jaeseok; Kang, Won-Seok; Moon, Cheil; Choi, Ji-Woong

    2016-01-01

    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°. PMID:27322267

  11. Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement

    Directory of Open Access Journals (Sweden)

    Kyungsoo Kim

    2016-06-01

    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°.

  12. A distributed microcomputer-controlled system for data acquisition and power spectral analysis of EEG.

    Science.gov (United States)

    Vo, T D; Dwyer, G; Szeto, H H

    1986-04-01

    A relatively powerful and inexpensive microcomputer-based system for the spectral analysis of the EEG is presented. High resolution and speed is achieved with the use of recently available large-scale integrated circuit technology with enhanced functionality (INTEL Math co-processors 8087) which can perform transcendental functions rapidly. The versatility of the system is achieved with a hardware organization that has distributed data acquisition capability performed by the use of a microprocessor-based analog to digital converter with large resident memory (Cyborg ISAAC-2000). Compiled BASIC programs and assembly language subroutines perform on-line or off-line the fast Fourier transform and spectral analysis of the EEG which is stored as soft as well as hard copy. Some results obtained from test application of the entire system in animal studies are presented.

  13. PENGEMBANGAN ALAT BANTU PEMODELAN TERAPI LENGAN PASCA STROKE DENGAN MEMANFAATKAN SINYAL ELECTROENCEPHALOGRAPHY (EEG) MENGGUNAKAN EMOTIV

    OpenAIRE

    Fatmawati, Ester; Prawito, Prawito; Wijaya, Sastra Kusuma

    2016-01-01

    Design modeling has been done post-stroke therapy arm by utilizing command brain signals generated by Electroencephalography (EEG). EEG signals provides a lot of information, one of which is motor information. Every body moving describe the unique form of brain signals. In conditions paralysis, motor information on the EEG signals will still be found when someone tries to move his limbs. The basic concepts of this study are the EEG signal acquisition using the Emotiv EPOC +, controling signal...

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

    Science.gov (United States)

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

    2007-01-01

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

  15. EEG-informed fMRI analysis during a hand grip task: estimating the relationship between EEG rhythms and the BOLD signal

    Directory of Open Access Journals (Sweden)

    Roberta eSclocco

    2014-04-01

    Full Text Available In the last decade, an increasing interest has arisen in investigating the relationship between the electrophysiological and hemodynamic measurements of brain activity, such as EEG and (BOLD fMRI. In particular, changes in BOLD have been shown to be associated with changes in the spectral profile of neural activity, rather than with absolute power. Concurrently, recent findings showed that different EEG rhythms are independently related to changes in the BOLD signal: therefore, it would be important to distinguish between the contributions of the different EEG rhythms to BOLD fluctuations when modeling the relationship between the two signals. Here we propose a method to perform EEG-informed fMRI analysis, in which the EEG regressors take into account both the changes in the spectral profile and the rhythms distinction. We applied it to EEG-fMRI data during a hand grip task in healthy subjects, and compared the results with those obtained by two existing models found in literature. Our results showed that the proposed method better captures the correlations between BOLD signal and EEG rhythms modulations, identifying task-related, well localized activated volumes. Furthermore, we showed that including among the regressors also EEG rhythms not primarily involved in the task enhances the performance of the analysis, even when only correlations with BOLD signal and specific EEG rhythms are explored.

  16. Wavelet-Based Artifact Identification and Separation Technique for EEG Signals during Galvanic Vestibular Stimulation

    Science.gov (United States)

    Adib, Mani; Cretu, Edmond

    2013-01-01

    We present a new method for removing artifacts in electroencephalography (EEG) records during Galvanic Vestibular Stimulation (GVS). The main challenge in exploiting GVS is to understand how the stimulus acts as an input to brain. We used EEG to monitor the brain and elicit the GVS reflexes. However, GVS current distribution throughout the scalp generates an artifact on EEG signals. We need to eliminate this artifact to be able to analyze the EEG signals during GVS. We propose a novel method to estimate the contribution of the GVS current in the EEG signals at each electrode by combining time-series regression methods with wavelet decomposition methods. We use wavelet transform to project the recorded EEG signal into various frequency bands and then estimate the GVS current distribution in each frequency band. The proposed method was optimized using simulated signals, and its performance was compared to well-accepted artifact removal methods such as ICA-based methods and adaptive filters. The results show that the proposed method has better performance in removing GVS artifacts, compared to the others. Using the proposed method, a higher signal to artifact ratio of −1.625 dB was achieved, which outperformed other methods such as ICA-based methods, regression methods, and adaptive filters. PMID:23956786

  17. Wavelet-Based Artifact Identification and Separation Technique for EEG Signals during Galvanic Vestibular Stimulation

    Directory of Open Access Journals (Sweden)

    Mani Adib

    2013-01-01

    Full Text Available We present a new method for removing artifacts in electroencephalography (EEG records during Galvanic Vestibular Stimulation (GVS. The main challenge in exploiting GVS is to understand how the stimulus acts as an input to brain. We used EEG to monitor the brain and elicit the GVS reflexes. However, GVS current distribution throughout the scalp generates an artifact on EEG signals. We need to eliminate this artifact to be able to analyze the EEG signals during GVS. We propose a novel method to estimate the contribution of the GVS current in the EEG signals at each electrode by combining time-series regression methods with wavelet decomposition methods. We use wavelet transform to project the recorded EEG signal into various frequency bands and then estimate the GVS current distribution in each frequency band. The proposed method was optimized using simulated signals, and its performance was compared to well-accepted artifact removal methods such as ICA-based methods and adaptive filters. The results show that the proposed method has better performance in removing GVS artifacts, compared to the others. Using the proposed method, a higher signal to artifact ratio of −1.625 dB was achieved, which outperformed other methods such as ICA-based methods, regression methods, and adaptive filters.

  18. Epileptic seizure detection in EEG signal with GModPCA and support vector machine.

    Science.gov (United States)

    Jaiswal, Abeg Kumar; Banka, Haider

    2017-01-01

    Epilepsy is one of the most common neurological disorders caused by recurrent seizures. Electroencephalograms (EEGs) record neural activity and can detect epilepsy. Visual inspection of an EEG signal for epileptic seizure detection is a time-consuming process and may lead to human error; therefore, recently, a number of automated seizure detection frameworks were proposed to replace these traditional methods. Feature extraction and classification are two important steps in these procedures. Feature extraction focuses on finding the informative features that could be used for classification and correct decision-making. Therefore, proposing effective feature extraction techniques for seizure detection is of great significance. Principal Component Analysis (PCA) is a dimensionality reduction technique used in different fields of pattern recognition including EEG signal classification. Global modular PCA (GModPCA) is a variation of PCA. In this paper, an effective framework with GModPCA and Support Vector Machine (SVM) is presented for epileptic seizure detection in EEG signals. The feature extraction is performed with GModPCA, whereas SVM trained with radial basis function kernel performed the classification between seizure and nonseizure EEG signals. Seven different experimental cases were conducted on the benchmark epilepsy EEG dataset. The system performance was evaluated using 10-fold cross-validation. In addition, we prove analytically that GModPCA has less time and space complexities as compared to PCA. The experimental results show that EEG signals have strong inter-sub-pattern correlations. GModPCA and SVM have been able to achieve 100% accuracy for the classification between normal and epileptic signals. Along with this, seven different experimental cases were tested. The classification results of the proposed approach were better than were compared the results of some of the existing methods proposed in literature. It is also found that the time and space

  19. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

    Science.gov (United States)

    Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

    2017-09-27

    An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal

    Directory of Open Access Journals (Sweden)

    Shanzhi Xu

    2018-02-01

    Full Text Available The recorded electroencephalography (EEG signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.

  1. Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal.

    Science.gov (United States)

    Xu, Shanzhi; Hu, Hai; Ji, Linhong; Wang, Peng

    2018-02-26

    The recorded electroencephalography (EEG) signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA) is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC) and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA) EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.

  2. Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns.

    Science.gov (United States)

    Liao, Shih-Cheng; Wu, Chien-Te; Huang, Hao-Chuan; Cheng, Wei-Teng; Liu, Yi-Hung

    2017-06-14

    Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP

  3. Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns

    Directory of Open Access Journals (Sweden)

    Shih-Cheng Liao

    2017-06-01

    Full Text Available Major depressive disorder (MDD has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP. The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total. Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be

  4. An EEG-Based Person Authentication System with Open-Set Capability Combining Eye Blinking Signals.

    Science.gov (United States)

    Wu, Qunjian; Zeng, Ying; Zhang, Chi; Tong, Li; Yan, Bin

    2018-01-24

    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.

  5. The effect of hypobaric hypoxia on multichannel EEG signal complexity.

    Science.gov (United States)

    Papadelis, Christos; Kourtidou-Papadeli, Chrysoula; Bamidis, Panagiotis D; Maglaveras, Nikos; Pappas, Konstantinos

    2007-01-01

    The objective of this study was the development and evaluation of nonlinear electroencephalography parameters which assess hypoxia-induced EEG alterations, and describe the temporal characteristics of different hypoxic levels' residual effect upon the brain electrical activity. Multichannel EEG, pO2, pCO2, ECG, and respiration measurements were recorded from 10 subjects exposed to three experimental conditions (100% oxygen, hypoxia, recovery) at three-levels of reduced barometric pressure. The mean spectral power of EEG under each session and altitude were estimated for the standard bands. Approximate Entropy (ApEn) of EEG segments was calculated, and the ApEn's time-courses were smoothed by a moving average filter. On the smoothed diagrams, parameters were defined. A significant increase in total power and power of theta and alpha bands was observed during hypoxia. Visual interpretation of ApEn time-courses revealed a characteristic pattern (decreasing during hypoxia and recovering after oxygen re-administration). The introduced qEEG parameters S1 and K1 distinguished successfully the three hypoxic conditions. The introduced parameters based on ApEn time-courses are assessing reliably and effectively the different hypoxic levels. ApEn decrease may be explained by neurons' functional isolation due to hypoxia since decreased complexity corresponds to greater autonomy of components, although this interpretation should be further supported by electrocorticographic animal studies. The introduced qEEG parameters seem to be appropriate for assessing the hypoxia-related neurophysiological state of patients in the hyperbaric chambers in the treatment of decompression sickness, carbon dioxide poisoning, and mountaineering.

  6. Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal.

    Science.gov (United States)

    Namazi, Hamidreza; Akrami, Amin; Nazeri, Sina; Kulish, Vladimir V

    2016-01-01

    An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal EEG signal. Also, odorant having higher entropy causes the EEG signal to have lower approximate entropy. The method discussed here can be applied and investigated in case of patients with brain diseases as the rehabilitation purpose.

  7. Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

    Directory of Open Access Journals (Sweden)

    Chin-Teng Lin

    2018-01-01

    Full Text Available Electroencephalogram (EEG signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA, feature extraction, and the Gaussian mixture model (GMM to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.

  8. Prompt recognition of brain states by their EEG signals

    DEFF Research Database (Denmark)

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

    1997-01-01

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

  9. Investigation of mental fatigue through EEG signal processing based on nonlinear analysis: Symbolic dynamics

    International Nuclear Information System (INIS)

    Azarnoosh, Mahdi; Motie Nasrabadi, Ali; Mohammadi, Mohammad Reza; Firoozabadi, Mohammad

    2011-01-01

    Highlights: Mental fatigue indices’ variation discussed during simple long-term attentive task. Symbolic dynamics of reaction time and EEG signal determine mental state variation. Nonlinear quantifiers such as entropy can display chaotic behaviors of the brain. Frontal and central lobes of the brain are effective in attention investigations. Mental fatigue causes a reduction in the complexity of the brain’s activity. Abstract: To investigate nonlinear analysis of attention physiological indices this study used a simple repetitive attentive task in four consecutive trials that resulted in mental fatigue. Traditional performance indices, such as reaction time, error responses, and EEG signals, were simultaneously recorded to evaluate differences between the trials. Performance indices analysis demonstrated that a selected task leads to mental fatigue. In addition, the study aimed to find a method to determine mental fatigue based on nonlinear analysis of EEG signals. Symbolic dynamics was selected as a qualitative method used to extract some quantitative qualifiers such as entropy. This method was executed on the reaction time of responses, and EEG signals to distinguish mental states. The results revealed that nonlinear analysis of reaction time, and EEG signals of the frontal and central lobes of the brain could differentiate between attention, and occurrence of mental fatigue in trials. In addition, the trend of entropy variation displayed a reduction in the complexity of mental activity as fatigue occurred.

  10. Decoding Individual Finger Movements from One Hand Using Human EEG Signals

    Science.gov (United States)

    Gonzalez, Jania; Ding, Lei

    2014-01-01

    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

  11. Developmental changes of BOLD signal correlations with global human EEG power and synchronization during working memory.

    Directory of Open Access Journals (Sweden)

    Lars Michels

    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

  12. Interictal functional connectivity of human epileptic networks assessed by intracerebral EEG and BOLD signal fluctuations.

    Directory of Open Access Journals (Sweden)

    Gaelle Bettus

    Full Text Available In this study, we aimed to demonstrate whether spontaneous fluctuations in the blood oxygen level dependent (BOLD signal derived from resting state functional magnetic resonance imaging (fMRI reflect spontaneous neuronal activity in pathological brain regions as well as in regions spared by epileptiform discharges. This is a crucial issue as coherent fluctuations of fMRI signals between remote brain areas are now widely used to define functional connectivity in physiology and in pathophysiology. We quantified functional connectivity using non-linear measures of cross-correlation between signals obtained from intracerebral EEG (iEEG and resting-state functional MRI (fMRI in 5 patients suffering from intractable temporal lobe epilepsy (TLE. Functional connectivity was quantified with both modalities in areas exhibiting different electrophysiological states (epileptic and non affected regions during the interictal period. Functional connectivity as measured from the iEEG signal was higher in regions affected by electrical epileptiform abnormalities relative to non-affected areas, whereas an opposite pattern was found for functional connectivity measured from the BOLD signal. Significant negative correlations were found between the functional connectivities of iEEG and BOLD signal when considering all pairs of signals (theta, alpha, beta and broadband and when considering pairs of signals in regions spared by epileptiform discharges (in broadband signal. This suggests differential effects of epileptic phenomena on electrophysiological and hemodynamic signals and/or an alteration of the neurovascular coupling secondary to pathological plasticity in TLE even in regions spared by epileptiform discharges. In addition, indices of directionality calculated from both modalities were consistent showing that the epileptogenic regions exert a significant influence onto the non epileptic areas during the interictal period. This study shows that functional

  13. Development of signal acquisition device of rotating coil measurement system

    International Nuclear Information System (INIS)

    Zhou Jianxin; Li Li; Kang Wen; Deng Chengdong; Yin Baogui; Fu Shinian

    2013-01-01

    A new rotating coil magnetic measurement system using the technical solution of the combination of a dynamic signal acquisition card and software with specific functions was developed. The acquisition device of the system successfully implemented the function of the PDI-5025 integrator. The sampling rate, the range, the accuracy and the flexibility of the system were improved. The development program of signal acquisition equipment, the realization of the acquisition function and the reliability and stability of the system were introduced. (authors)

  14. Automatic Seizure Detection in Rats Using Laplacian EEG and Verification with Human Seizure Signals

    Science.gov (United States)

    Feltane, Amal; Boudreaux-Bartels, G. Faye; Besio, Walter

    2012-01-01

    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. PMID:23073989

  15. Automatic removal of eye-movement and blink artifacts from EEG signals.

    Science.gov (United States)

    Gao, Jun Feng; Yang, Yong; Lin, Pan; Wang, Pei; Zheng, Chong Xun

    2010-03-01

    Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.

  16. Classification of EEG signals using a genetic-based machine learning classifier.

    Science.gov (United States)

    Skinner, B T; Nguyen, H T; Liu, D K

    2007-01-01

    This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices.

  17. EEG signal classification based on artificial neural networks and amplitude spectra features

    Science.gov (United States)

    Chojnowski, K.; FrÄ czek, J.

    BCI (called Brain-Computer Interface) is an interface that allows direct communication between human brain and an external device. It bases on EEG signal collection, processing and classification. In this paper a complete BCI system is presented which classifies EEG signal using artificial neural networks. For this purpose we used a multi-layered perceptron architecture trained with the RProp algorithm. Furthermore a simple multi-threaded method for automatic network structure optimizing was shown. We presented the results of our system in the opening and closing eyes recognition task. We also showed how our system could be used for controlling devices basing on imaginary hand movements.

  18. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

    Science.gov (United States)

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  19. Improving the quality of a collective signal in a consumer EEG headset.

    Directory of Open Access Journals (Sweden)

    Alejandro Morán

    Full Text Available This work focuses on the experimental data analysis of electroencephalography (EEG data, in which multiple sensors are recording oscillatory voltage time series. The EEG data analyzed in this manuscript has been acquired using a low-cost commercial headset, the Emotiv EPOC+. Our goal is to compare different techniques for the optimal estimation of collective rhythms from EEG data. To this end, a traditional method such as the principal component analysis (PCA is compared to more recent approaches to extract a collective rhythm from phase-synchronized data. Here, we extend the work by Schwabedal and Kantz (PRL 116, 104101 (2016 evaluating the performance of the Kosambi-Hilbert torsion (KHT method to extract a collective rhythm from multivariate oscillatory time series and compare it to results obtained from PCA. The KHT method takes advantage of the singular value decomposition algorithm and accounts for possible phase lags among different time series and allows to focus the analysis on a specific spectral band, optimally amplifying the signal-to-noise ratio of a common rhythm. We evaluate the performance of these methods for two particular sets of data: EEG data recorded with closed eyes and EEG data recorded while observing a screen flickering at 15 Hz. We found an improvement in the signal-to-noise ratio of the collective signal for the KHT over the PCA, particularly when random temporal shifts are added to the channels.

  20. Feature Optimize and Classification of EEG Signals: Application to Lie Detection Using KPCA and ELM

    Directory of Open Access Journals (Sweden)

    GAO Junfeng

    2014-04-01

    Full Text Available EEG signals had been widely used to detect liars recent years. To overcome the shortcomings of current signals processing, kernel principal component analysis (KPCA and extreme learning machine (ELM was combined to detect liars. We recorded the EEG signals at Pz from 30 randomly divided guilty and innocent subjects. Each five Probe responses were averaged within subject and then extracted wavelet features. KPCA was employed to select feature subset with deduced dimensions based on initial wavelet features, which was fed into ELM. To date, there is no perfect solution for the number of its hidden nodes (NHN. We used grid searching algorithm to select simultaneously the optimal values of the dimension of feature subset and NHN based on cross- validation method. The best classification mode was decided with the optimal searching values. Experimental results show that for EEG signals from the experiment of lie detection, KPCA_ELM has higher classification accuracy with faster training speed than other widely-used classification modes, which is especially suitable for online EEG signals processing system.

  1. EEG signal classification using PSO trained RBF neural network for epilepsy identification

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    Full Text Available The electroencephalogram (EEG is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT. To classify the EEG signal, we used a radial basis function neural network (RBFNN. As shown herein, the network can be trained to optimize the mean square error (MSE by using a modified particle swarm optimization (PSO algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learning

  2. A New Approach to Eliminate High Amplitude Artifacts in EEG Signals

    Directory of Open Access Journals (Sweden)

    Ana Rita Teixeira

    2016-09-01

    Full Text Available High amplitude artifacts represent a problem during EEG recordings in neuroscience research. Taking this into account, this paper proposes a method to identify high amplitude artifacts with no requirement for visual inspection, electrooscillogram (EOG reference channel or user assigned parameters. A potential solution to the high amplitude artifacts (HAA elimination is presented based on blind source separation methods. The assumption underlying the selection of components is that HAA are independent of the EEG signal and different HAA can be generated during the EEG recordings. Therefore, the number of components related to HAA is variable and depends on the processed signal, which means that the method is adaptable to the input signal. The results show, when removing the HAA artifacts, the delta band is distorted but all the other frequency bands are preserved. A case study with EEG signals recorded while participants performed on the Halstead Category Test (HCT is presented. After HAA removal, data analysis revealed, as expected, an error-related frontal ERP wave: the feedback-related negativity (FRN in response to feedback stimuli.

  3. Complexity of EEG-signal in Time Domain - Possible Biomedical Application

    Science.gov (United States)

    Klonowski, Wlodzimierz; Olejarczyk, Elzbieta; Stepien, Robert

    2002-07-01

    Human brain is a highly complex nonlinear system. So it is not surprising that in analysis of EEG-signal, which represents overall activity of the brain, the methods of Nonlinear Dynamics (or Chaos Theory as it is commonly called) can be used. Even if the signal is not chaotic these methods are a motivating tool to explore changes in brain activity due to different functional activation states, e.g. different sleep stages, or to applied therapy, e.g. exposure to chemical agents (drugs) and physical factors (light, magnetic field). The methods supplied by Nonlinear Dynamics reveal signal characteristics that are not revealed by linear methods like FFT. Better understanding of principles that govern dynamics and complexity of EEG-signal can help to find `the signatures' of different physiological and pathological states of human brain, quantitative characteristics that may find applications in medical diagnostics.

  4. LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

    Science.gov (United States)

    Zhang, Tao; Chen, Wanzhong

    2017-08-01

    Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.

  5. Prognostic and diagnostic value of EEG signal coupling measures in coma.

    Science.gov (United States)

    Zubler, Frederic; Koenig, Christa; Steimer, Andreas; Jakob, Stephan M; Schindler, Kaspar A; Gast, Heidemarie

    2016-08-01

    Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients. In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome. Using cross-validation, the predictive value of measure combinations was assessed with a Bayes classifier with mixture of Gaussians. Five of eight measures showed a statistically significant difference between patients grouped according to outcome; one measure revealed differences in patients grouped according to the etiology. Interestingly, a high level of synchrony between the left and right hemisphere was associated with mortality on intensive care unit, whereas higher synchrony between anterior and posterior brain regions was associated with survival. The combination with the best predictive value reached an area-under the curve of 0.875 (for patients with post anoxic encephalopathy: 0.946). EEG synchronization measures can contribute to clinical assessment, and provide new approaches for understanding the pathophysiology of coma. Prognostication in coma remains a challenging task. qEEG could improve current multi-modal approaches. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  6. Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors

    Directory of Open Access Journals (Sweden)

    N. Sriraam

    2011-01-01

    Full Text Available A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

  7. Influence of Wilbraham-Gibbs Phenomenon on Digital Stochastic Measurement of EEG Signal Over an Interval

    Directory of Open Access Journals (Sweden)

    Sovilj P.

    2014-10-01

    Full Text Available Measurement methods, based on the approach named Digital Stochastic Measurement, have been introduced, and several prototype and small-series commercial instruments have been developed based on these methods. These methods have been mostly investigated for various types of stationary signals, but also for non-stationary signals. This paper presents, analyzes and discusses digital stochastic measurement of electroencephalography (EEG signal in the time domain, emphasizing the problem of influence of the Wilbraham-Gibbs phenomenon. The increase of measurement error, related to the Wilbraham-Gibbs phenomenon, is found. If the EEG signal is measured and measurement interval is 20 ms wide, the average maximal error relative to the range of input signal is 16.84 %. If the measurement interval is extended to 2s, the average maximal error relative to the range of input signal is significantly lowered - down to 1.37 %. Absolute errors are compared with the error limit recommended by Organisation Internationale de Métrologie Légale (OIML and with the quantization steps of the advanced EEG instruments with 24-bit A/D conversion

  8. Single Trial Classification of Evoked EEG Signals Due to RGB Colors

    Directory of Open Access Journals (Sweden)

    Eman Alharbi

    2016-03-01

    Full Text Available Recently, the impact of colors on the brain signals has become one of the leading researches in BCI systems. These researches are based on studying the brain behavior after color stimulus, and finding a way to classify its signals offline without considering the real time. Moving to the next step, we present a real time classification model (online for EEG signals evoked by RGB colors stimuli, which is not presented in previous studies. In this research, EEG signals were recorded from 7 subjects through BCI2000 toolbox. The Empirical Mode Decomposition (EMD technique was used at the signal analysis stage. Various feature extraction methods were investigated to find the best and reliable set, including Event-related spectral perturbations (ERSP, Target mean with Feast Fourier Transform (FFT, Wavelet Packet Decomposition (WPD, Auto Regressive model (AR and EMD residual. A new feature selection method was created based on the peak's time of EEG signal when red and blue colors stimuli are presented. The ERP image was used to find out the peak's time, which was around 300 ms for the red color and around 450 ms for the blue color. The classification was performed using the Support Vector Machine (SVM classifier, LIBSVM toolbox being used for that purpose. The EMD residual was found to be the most reliable method that gives the highest classification accuracy with an average of 88.5% and with an execution time of only 14 seconds.

  9. Automated Detection of Driver Fatigue Based on AdaBoost Classifier with EEG Signals

    Directory of Open Access Journals (Sweden)

    Jianfeng Hu

    2017-08-01

    Full Text Available Purpose: Driving fatigue has become one of the important causes of road accidents, there are many researches to analyze driver fatigue. EEG is becoming increasingly useful in the measuring fatigue state. Manual interpretation of EEG signals is impossible, so an effective method for automatic detection of EEG signals is crucial needed.Method: In order to evaluate the complex, unstable, and non-linear characteristics of EEG signals, four feature sets were computed from EEG signals, in which fuzzy entropy (FE, sample entropy (SE, approximate Entropy (AE, spectral entropy (PE, and combined entropies (FE + SE + AE + PE were included. All these feature sets were used as the input vectors of AdaBoost classifier, a boosting method which is fast and highly accurate. To assess our method, several experiments including parameter setting and classifier comparison were conducted on 28 subjects. For comparison, Decision Trees (DT, Support Vector Machine (SVM and Naive Bayes (NB classifiers are used.Results: The proposed method (combination of FE and AdaBoost yields superior performance than other schemes. Using FE feature extractor, AdaBoost achieves improved area (AUC under the receiver operating curve of 0.994, error rate (ERR of 0.024, Precision of 0.969, Recall of 0.984, F1 score of 0.976, and Matthews correlation coefficient (MCC of 0.952, compared to SVM (ERR at 0.035, Precision of 0.957, Recall of 0.974, F1 score of 0.966, and MCC of 0.930 with AUC of 0.990, DT (ERR at 0.142, Precision of 0.857, Recall of 0.859, F1 score of 0.966, and MCC of 0.716 with AUC of 0.916 and NB (ERR at 0.405, Precision of 0.646, Recall of 0.434, F1 score of 0.519, and MCC of 0.203 with AUC of 0.606. It shows that the FE feature set and combined feature set outperform other feature sets. AdaBoost seems to have better robustness against changes of ratio of test samples for all samples and number of subjects, which might therefore aid in the real-time detection of driver

  10. Combined analysis of cortical (EEG) and nerve stump signals improves robotic hand control.

    Science.gov (United States)

    Tombini, Mario; Rigosa, Jacopo; Zappasodi, Filippo; Porcaro, Camillo; Citi, Luca; Carpaneto, Jacopo; Rossini, Paolo Maria; Micera, Silvestro

    2012-01-01

    Interfacing an amputee's upper-extremity stump nerves to control a robotic hand requires training of the individual and algorithms to process interactions between cortical and peripheral signals. To evaluate for the first time whether EEG-driven analysis of peripheral neural signals as an amputee practices could improve the classification of motor commands. Four thin-film longitudinal intrafascicular electrodes (tf-LIFEs-4) were implanted in the median and ulnar nerves of the stump in the distal upper arm for 4 weeks. Artificial intelligence classifiers were implemented to analyze LIFE signals recorded while the participant tried to perform 3 different hand and finger movements as pictures representing these tasks were randomly presented on a screen. In the final week, the participant was trained to perform the same movements with a robotic hand prosthesis through modulation of tf-LIFE-4 signals. To improve the classification performance, an event-related desynchronization/synchronization (ERD/ERS) procedure was applied to EEG data to identify the exact timing of each motor command. Real-time control of neural (motor) output was achieved by the participant. By focusing electroneurographic (ENG) signal analysis in an EEG-driven time window, movement classification performance improved. After training, the participant regained normal modulation of background rhythms for movement preparation (α/β band desynchronization) in the sensorimotor area contralateral to the missing limb. Moreover, coherence analysis found a restored α band synchronization of Rolandic area with frontal and parietal ipsilateral regions, similar to that observed in the opposite hemisphere for movement of the intact hand. Of note, phantom limb pain (PLP) resolved for several months. Combining information from both cortical (EEG) and stump nerve (ENG) signals improved the classification performance compared with tf-LIFE signals processing alone; training led to cortical reorganization and

  11. Recognizing the degree of human attention using EEG signals from mobile sensors.

    Science.gov (United States)

    Liu, Ning-Han; Chiang, Cheng-Yu; Chu, Hsuan-Chin

    2013-08-09

    During the learning process, whether students remain attentive throughout instruction generally influences their learning efficacy. If teachers can instantly identify whether students are attentive they can be suitably reminded to remain focused, thereby improving their learning effects. Traditional teaching methods generally require that teachers observe students' expressions to determine whether they are attentively learning. However, this method is often inaccurate and increases the burden on teachers. With the development of electroencephalography (EEG) detection tools, mobile brainwave sensors have become mature and affordable equipment. Therefore, in this study, whether students are attentive or inattentive during instruction is determined by observing their EEG signals. Because distinguishing between attentiveness and inattentiveness is challenging, two scenarios were developed for this study to measure the subjects' EEG signals when attentive and inattentive. After collecting EEG data using mobile sensors, various common features were extracted from the raw data. A support vector machine (SVM) classifier was used to calculate and analyze these features to identify the combination of features that best indicates whether students are attentive. Based on the experiment results, the method proposed in this study provides a classification accuracy of up to 76.82%. The study results can be used as a reference for learning system designs in the future.

  12. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

    Science.gov (United States)

    Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza

    2013-03-01

    Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  13. Analysis of the influence of memory content of auditory stimuli on the memory content of EEG signal.

    Science.gov (United States)

    Namazi, Hamidreza; Khosrowabadi, Reza; Hussaini, Jamal; Habibi, Shaghayegh; Farid, Ali Akhavan; Kulish, Vladimir V

    2016-08-30

    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 may exist between the memory content of stimulus and the memory content of EEG signal. For this purpose we consider the Hurst exponent as the measure of memory. This study reveals the plasticity of human EEG signals in relation to the auditory stimuli. For the first time we demonstrated that the memory content of an EEG signal shifts towards the memory content of the auditory stimulus used. The results of this analysis showed that an auditory stimulus with higher memory content causes a larger increment in the memory content of an EEG signal. For the verification of this result, we benefit from approximate entropy as indicator of time series randomness. The capability, observed in this research, can be further investigated in relation to human memory.

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

    Science.gov (United States)

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

    2015-09-01

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

  15. Automatic identification of epileptic seizures from EEG signals using linear programming boosting.

    Science.gov (United States)

    Hassan, Ahnaf Rashik; Subasi, Abdulhamit

    2016-11-01

    Computerized epileptic seizure detection is essential for expediting epilepsy diagnosis and research and for assisting medical professionals. Moreover, the implementation of an epilepsy monitoring device that has low power and is portable requires a reliable and successful seizure detection scheme. In this work, the problem of automated epilepsy seizure detection using singe-channel EEG signals has been addressed. At first, segments of EEG signals are decomposed using a newly proposed signal processing scheme, namely complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Six spectral moments are extracted from the CEEMDAN mode functions and train and test matrices are formed afterward. These matrices are fed into the classifier to identify epileptic seizures from EEG signal segments. In this work, we implement an ensemble learning based machine learning algorithm, namely linear programming boosting (LPBoost) to perform classification. The efficacy of spectral features in the CEEMDAN domain is validated by graphical and statistical analyses. The performance of CEEMDAN is compared to those of its predecessors to further inspect its suitability. The effectiveness and the appropriateness of LPBoost are demonstrated as opposed to the commonly used classification models. Resubstitution and 10 fold cross-validation error analyses confirm the superior algorithm performance of the proposed scheme. The algorithmic performance of our epilepsy seizure identification scheme is also evaluated against state-of-the-art works in the literature. Experimental outcomes manifest that the proposed seizure detection scheme performs better than the existing works in terms of accuracy, sensitivity, specificity, and Cohen's Kappa coefficient. It can be anticipated that owing to its use of only one channel of EEG signal, the proposed method will be suitable for device implementation, eliminate the onus of clinicians for analyzing a large bulk of data manually, and

  16. Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals.

    Science.gov (United States)

    Zhuang, Ning; Zeng, Ying; Yang, Kai; Zhang, Chi; Tong, Li; Yan, Bin

    2018-03-12

    Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods.

  17. Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals

    Science.gov (United States)

    Zeng, Ying; Yang, Kai; Tong, Li; Yan, Bin

    2018-01-01

    Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods. PMID:29534515

  18. A novel deep learning approach for classification of EEG motor imagery signals.

    Science.gov (United States)

    Tabar, Yousef Rezaei; Halici, Ugur

    2017-02-01

    Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.

  19. The effects of lossy compression on diagnostically relevant seizure information in EEG signals.

    Science.gov (United States)

    Higgins, G; McGinley, B; Faul, S; McEvoy, R P; Glavin, M; Marnane, W P; Jones, E

    2013-01-01

    This paper examines the effects of compression on EEG signals, in the context of automated detection of epileptic seizures. Specifically, it examines the use of lossy compression on EEG signals in order to reduce the amount of data which has to be transmitted or stored, while having as little impact as possible on the information in the signal relevant to diagnosing epileptic seizures. Two popular compression methods, JPEG2000 and SPIHT, were used. A range of compression levels was selected for both algorithms in order to compress the signals with varying degrees of loss. This compression was applied to the database of epileptiform data provided by the University of Freiburg, Germany. The real-time EEG analysis for event detection automated seizure detection system was used in place of a trained clinician for scoring the reconstructed data. Results demonstrate that compression by a factor of up to 120:1 can be achieved, with minimal loss in seizure detection performance as measured by the area under the receiver operating characteristic curve of the seizure detection system.

  20. Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations.

    Directory of Open Access Journals (Sweden)

    Gilney Figueira Zebende

    Full Text Available 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.

  1. On seeing the trees and the forest: single-signal and multisignal analysis of periictal intracranial EEG.

    Science.gov (United States)

    Schindler, Kaspar; Gast, Heidemarie; Goodfellow, Marc; Rummel, Christian

    2012-09-01

    Epileptic seizures are associated with a dysregulation of electrical brain activity on many different spatial scales. To better understand the dynamics of epileptic seizures, that is, how the seizures initiate, propagate, and terminate, it is important to consider changes of electrical brain activity on different spatial scales. Herein we set out to analyze periictal electrical brain activity on comparatively small and large spatial scales by assessing changes in single intracranial electroencephalography (EEG) signals and of averaged interdependences of pairs of EEG signals. Single and multiple EEG signals are analyzed by combining methods from symbolic dynamics and information theory. This computationally efficient approach is chosen because at its core it consists of analyzing the occurrence of patterns and bears analogy to classical visual EEG reading. Symbolization is achieved by first mapping the EEG signals into bit strings, that is, long sequences of zeros and ones, depending solely on whether their amplitudes increase or decrease. Bit strings reflect relational aspects between consecutive values of the original EEG signals, but not the values themselves. For each bit string the relative frequencies of the different constituent short bit patterns are then determined and used to compute two information theoretical measures: (1) redundancy (R) of single bit strings characterizes electrical brain activity on a comparatively small spatial scale represented by a single EEG signal and (2) averaged pair-wise mutual information with all other bit strings (M), which allows tracking of larger-scale EEG dynamics. We analyzed 20 periictal intracranial EEG recordings from five patients with pharmacoresistant temporal lobe epilepsy. At seizure onset, R first strongly increased and then decreased toward seizure termination, whereas M gradually increased throughout the seizure. Bit strings with maximal R were always derived from EEG signals recorded from the visually

  2. SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal

    Science.gov (United States)

    Cherkassky, Vladimir; Lee, Jieun; Veber, Brandon; Patterson, Edward E.; Brinkmann, Benjamin H.; Worrell, Gregory A.

    2017-01-01

    Objective This paper describes a data-analytic modeling approach for prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. Methods Our work emphasizes understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during pre-processing and post-processing are considered and investigated for their effect on seizure prediction accuracy. Results Our empirical results show that the proposed SVM-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90–100%, and the false-positive rate is about 0–0.3 times per day. The results also suggest good prediction is subject-specific (dog or human), in agreement with earlier studies. Conclusion Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5–7 seizures. Significance The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages. PMID:27362758

  3. Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Prima Dewi Purnamasari

    2017-05-01

    Full Text Available The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN, and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction

  4. The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.

    Science.gov (United States)

    Wang, Gang; Teng, Chaolin; Li, Kuo; Zhang, Zhonglin; Yan, Xiangguo

    2016-09-01

    The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.

  5. Fast-Acquisition/Weak-Signal-Tracking GPS Receiver for HEO

    Science.gov (United States)

    Wintemitz, Luke; Boegner, Greg; Sirotzky, Steve

    2004-01-01

    A report discusses the technical background and design of the Navigator Global Positioning System (GPS) receiver -- . a radiation-hardened receiver intended for use aboard spacecraft. Navigator is capable of weak signal acquisition and tracking as well as much faster acquisition of strong or weak signals with no a priori knowledge or external aiding. Weak-signal acquisition and tracking enables GPS use in high Earth orbits (HEO), and fast acquisition allows for the receiver to remain without power until needed in any orbit. Signal acquisition and signal tracking are, respectively, the processes of finding and demodulating a signal. Acquisition is the more computationally difficult process. Previous GPS receivers employ the method of sequentially searching the two-dimensional signal parameter space (code phase and Doppler). Navigator exploits properties of the Fourier transform in a massively parallel search for the GPS signal. This method results in far faster acquisition times [in the lab, 12 GPS satellites have been acquired with no a priori knowledge in a Low-Earth-Orbit (LEO) scenario in less than one second]. Modeling has shown that Navigator will be capable of acquiring signals down to 25 dB-Hz, appropriate for HEO missions. Navigator is built using the radiation-hardened ColdFire microprocessor and housing the most computationally intense functions in dedicated field-programmable gate arrays. The high performance of the algorithm and of the receiver as a whole are made possible by optimizing computational efficiency and carefully weighing tradeoffs among the sampling rate, data format, and data-path bit width.

  6. Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients.

    Science.gov (United States)

    Ebrahimi, Farideh; Mikaeili, Mohammad; Estrada, Edson; Nazeran, Homer

    2008-01-01

    Currently in the world there is an alarming number of people who suffer from sleep disorders. A number of biomedical signals, such as EEG, EMG, ECG and EOG are used in sleep labs among others for diagnosis and treatment of sleep related disorders. The usual method for sleep stage classification is visual inspection by a sleep specialist. This is a very time consuming and laborious exercise. Automatic sleep stage classification can facilitate this process. The definition of sleep stages and the sleep literature show that EEG signals are similar in Stage 1 of non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. Therefore, in this work an attempt was made to classify four sleep stages consisting of Awake, Stage 1 + REM, Stage 2 and Slow Wave Stage based on the EEG signal alone. Wavelet packet coefficients and artificial neural networks were deployed for this purpose. Seven all night recordings from Physionet database were used in the study. The results demonstrated that these four sleep stages could be automatically discriminated from each other with a specificity of 94.4 +/- 4.5%, a of sensitivity 84.2+3.9% and an accuracy of 93.0 +/- 4.0%.

  7. Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms

    Science.gov (United States)

    Zhang, Zhiwen; Duan, Feng; Zhou, Xin; Meng, Zixuan

    2017-01-01

    Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance. PMID:28874909

  8. Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.

    Science.gov (United States)

    Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Mubin, Marizan; Saad, Ismail

    2016-01-01

    In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

  9. Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms.

    Science.gov (United States)

    Liu, Rensong; Zhang, Zhiwen; Duan, Feng; Zhou, Xin; Meng, Zixuan

    2017-01-01

    Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K -nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance.

  10. ABC optimized RBF network for classification of EEG signal for epileptic seizure identification

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    2017-03-01

    Full Text Available The brain signals usually generate certain electrical signals that can be recorded and analyzed for detection in several brain disorder diseases. These small signals are expressly called as Electroencephalogram (EEG signals. This research work analyzes the epileptic disorder in human brain through EEG signal analysis by integrating the best attributes of Artificial Bee Colony (ABC and radial basis function networks (RBFNNs. We have used Discrete Wavelet Transform (DWT technique for extraction of potential features from the signal. In our study, for classification of these signals, in this paper, the RBFNNs have been trained by a modified version of ABC algorithm. In the modified ABC, the onlooker bees are selected based on binary tournament unlike roulette wheel selection of ABC. Additionally, kernels such as Gaussian, Multi-quadric, and Inverse-multi-quadric are used for measuring the effectiveness of the method in numerous mixtures of healthy segments, seizure-free segments, and seizure segments. Our experimental outcomes confirm that RBFNN with inverse-multi-quadric kernel trained with modified ABC is significantly better than RBFNNs with other kernels trained by ABC and modified ABC.

  11. A low-rank matrix recovery approach for energy efficient EEG acquisition for a wireless body area network.

    Science.gov (United States)

    Majumdar, Angshul; Gogna, Anupriya; Ward, Rabab

    2014-08-25

    We address the problem of acquiring and transmitting EEG signals in Wireless Body Area Networks (WBAN) in an energy efficient fashion. In WBANs, the energy is consumed by three operations: sensing (sampling), processing and transmission. Previous studies only addressed the problem of reducing the transmission energy. For the first time, in this work, we propose a technique to reduce sensing and processing energy as well: this is achieved by randomly under-sampling the EEG signal. We depart from previous Compressed Sensing based approaches and formulate signal recovery (from under-sampled measurements) as a matrix completion problem. A new algorithm to solve the matrix completion problem is derived here. We test our proposed method and find that the reconstruction accuracy of our method is significantly better than state-of-the-art techniques; and we achieve this while saving sensing, processing and transmission energy. Simple power analysis shows that our proposed methodology consumes considerably less power compared to previous CS based techniques.

  12. Analysis of signal acquisition in GPS receiver software

    Directory of Open Access Journals (Sweden)

    Vlada S. Sokolović

    2011-01-01

    . the whole receiver is software implemented in a MATLAB software package. One of the processes during the signal processing is the initial synchronization (acquisition, where a signal is detected and the carrier frequency is determined as well as the phase sequence code and the carrier Doppler frequency. The acquisition aim is to determine, in the shortest time possible, the parameters of the detected signals and forward them to the next block in synchronization. Depending on the speed and accuracy of the signal parameter determination, different methods of acquisition are applied in practice. The paper presents the methods of serial, parallel and cyclic convolution. For comparison purposes, the architectures of signal processing of particular methods for implementation in receiver software are shown. All measurements were performed on the same signal under the same conditions. On the basis of the tests performed, a detailed analysis of the collected data was carried out and the most acceptable acquisition method for implementation in software GPS receiver was proposed. Because of a relatively high level of noise at the receiver entrance and the received signal interference, the comparison of the results has been done on the basis of the analytical results and the mean time of signal synchronization. The measurement results are shown in tables for easy comparison. The results of measurements using the proposed method are presented as well. The technology of receiver software allows the user to access easily to the architecture of the receiver and therefore allows a simple change of parameters. The influence of the parameters on the process of signal acquisition is also shown in the paper. The graphic presentation shows how and to what extent some of the parameters affect the process of the receiver signal processing. All listed acquisition methods are used in practice. The proposed method is the most suitable for application in software receivers. Based on the analysis

  13. An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information.

    Science.gov (United States)

    Kumar, Shiu; Sharma, Alok; Tsunoda, Tatsuhiko

    2017-12-28

    Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent. In this study, we propose a mutual information based frequency band selection approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks. CSP features are extracted from multiple overlapping sub-bands. An additional sub-band has been introduced that cover the wide frequency band (7-30 Hz) and two different types of features are extracted using CSP and common spatio-spectral pattern techniques, respectively. Mutual information is then computed from the extracted features of each of these bands and the top filter banks are selected for further processing. Linear discriminant analysis is applied to the features extracted from each of the filter banks. The scores are fused together, and classification is done using support vector machine. The proposed method is evaluated using BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, and it outperformed all other competing methods achieving the lowest misclassification rate and the highest kappa coefficient on all three datasets. Introducing a wide sub-band and using mutual information for selecting the most discriminative sub-bands, the proposed method shows improvement in motor imagery EEG signal classification.

  14. Biomedical signal acquisition, processing and transmission using smartphone

    International Nuclear Information System (INIS)

    Roncagliolo, Pablo; Arredondo, Luis; Gonzalez, AgustIn

    2007-01-01

    This article describes technical aspects involved in the programming of a system of acquisition, processing and transmission of biomedical signals by using mobile devices. This task is aligned with the permanent development of new technologies for the diagnosis and sickness treatment, based on the feasibility of measuring continuously different variables as electrocardiographic signals, blood pressure, oxygen concentration, pulse or simply temperature. The contribution of this technology is settled on its portability and low cost, which allows its massive use. Specifically this work analyzes the feasibility of acquisition and the processing of signals from a standard smartphone. Work results allow to state that nowadays these equipments have enough processing capacity to execute signals acquisition systems. These systems along with external servers make it possible to imagine a near future where the possibility of making continuous measures of biomedical variables will not be restricted only to hospitals but will also begin to be more frequently used in the daily life and at home

  15. Biomedical signal acquisition, processing and transmission using smartphone

    Energy Technology Data Exchange (ETDEWEB)

    Roncagliolo, Pablo [Department of Electronics, Universidad Tecnica Federico Santa Maria, Casilla 110-V, ValparaIso (Chile); Arredondo, Luis [Department of Biomedical Engineering, Universidad de ValparaIso, Casilla 123-V, ValparaIso (Chile); Gonzalez, AgustIn [Department of Electronics, Universidad Tecnica Federico Santa MarIa, Casilla 110-V, ValparaIso (Chile)

    2007-11-15

    This article describes technical aspects involved in the programming of a system of acquisition, processing and transmission of biomedical signals by using mobile devices. This task is aligned with the permanent development of new technologies for the diagnosis and sickness treatment, based on the feasibility of measuring continuously different variables as electrocardiographic signals, blood pressure, oxygen concentration, pulse or simply temperature. The contribution of this technology is settled on its portability and low cost, which allows its massive use. Specifically this work analyzes the feasibility of acquisition and the processing of signals from a standard smartphone. Work results allow to state that nowadays these equipments have enough processing capacity to execute signals acquisition systems. These systems along with external servers make it possible to imagine a near future where the possibility of making continuous measures of biomedical variables will not be restricted only to hospitals but will also begin to be more frequently used in the daily life and at home.

  16. Biomedical signal acquisition, processing and transmission using smartphone

    Science.gov (United States)

    Roncagliolo, Pablo; Arredondo, Luis; González, Agustín

    2007-11-01

    This article describes technical aspects involved in the programming of a system of acquisition, processing and transmission of biomedical signals by using mobile devices. This task is aligned with the permanent development of new technologies for the diagnosis and sickness treatment, based on the feasibility of measuring continuously different variables as electrocardiographic signals, blood pressure, oxygen concentration, pulse or simply temperature. The contribution of this technology is settled on its portability and low cost, which allows its massive use. Specifically this work analyzes the feasibility of acquisition and the processing of signals from a standard smartphone. Work results allow to state that nowadays these equipments have enough processing capacity to execute signals acquisition systems. These systems along with external servers make it possible to imagine a near future where the possibility of making continuous measures of biomedical variables will not be restricted only to hospitals but will also begin to be more frequently used in the daily life and at home.

  17. Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

    Directory of Open Access Journals (Sweden)

    Zhixian Yang

    2014-01-01

    Full Text Available Background electroencephalography (EEG, recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE and sample entropy (SampEn in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.

  18. a Signal-Tuned Gabor Transform with Application to Eeg Analysis

    Science.gov (United States)

    Torreão, José R. A.; Victer, Silvia M. C.; Fernandes, João L.

    2013-04-01

    We introduce a time-frequency transform based on Gabor functions whose parameters are given by the Fourier transform of the analyzed signal. At any given frequency, the width and the phase of the Gabor function are obtained, respectively, from the magnitude and the phase of the signal's corresponding Fourier component, yielding an analyzing kernel which is a representation of the signal's content at that particular frequency. The resulting Gabor transform tunes itself to the input signal, allowing the accurate detection of time and frequency events, even in situations where the traditional Gabor and S-transform approaches tend to fail. This is the case, for instance, when considering the time-frequency representation of electroencephalogram traces (EEG) of epileptic subjects, as illustrated by the experimental study presented here.

  19. A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals

    Science.gov (United States)

    Quintero-Rincón, Antonio; Pereyra, Marcelo; D'Giano, Carlos; Batatia, Hadj; Risk, Marcelo

    2016-04-01

    Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biomedical signal processing. In this work we propose a new algorithm for seizure onset detection and spread estimation in epilepsy patients. The algorithm is based on a multilevel 1-D wavelet decomposition that captures the physiological brain frequency signals coupled with a generalized gaussian model. Preliminary experiments with signals from 30 epilepsy crisis and 11 subjects, suggest that the proposed methodology is a powerful tool for detecting the onset of epilepsy seizures with his spread across the brain.

  20. A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.

    Science.gov (United States)

    Zarei, Roozbeh; He, Jing; Siuly, Siuly; Zhang, Yanchun

    2017-07-01

    Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time

  1. Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics.

    Science.gov (United States)

    Cuesta-Frau, David; Miró-Martínez, Pau; Jordán Núñez, Jorge; Oltra-Crespo, Sandra; Molina Picó, Antonio

    2017-08-01

    This paper evaluates the performance of first generation entropy metrics, featured by the well known and widely used Approximate Entropy (ApEn) and Sample Entropy (SampEn) metrics, and what can be considered an evolution from these, Fuzzy Entropy (FuzzyEn), in the Electroencephalogram (EEG) signal classification context. The study uses the commonest artifacts found in real EEGs, such as white noise, and muscular, cardiac, and ocular artifacts. Using two different sets of publicly available EEG records, and a realistic range of amplitudes for interfering artifacts, this work optimises and assesses the robustness of these metrics against artifacts in class segmentation terms probability. The results show that the qualitative behaviour of the two datasets is similar, with SampEn and FuzzyEn performing the best, and the noise and muscular artifacts are the most confounding factors. On the contrary, there is a wide variability as regards initialization parameters. The poor performance achieved by ApEn suggests that this metric should not be used in these contexts. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Classifying epileptic EEG signals with delay permutation entropy and Multi-Scale K-means.

    Science.gov (United States)

    Zhu, Guohun; Li, Yan; Wen, Peng Paul; Wang, Shuaifang

    2015-01-01

    Most epileptic EEG classification algorithms are supervised and require large training datasets, that hinder their use in real time applications. This chapter proposes an unsupervised Multi-Scale K-means (MSK-means) MSK-means algorithm to distinguish epileptic EEG signals and identify epileptic zones. The random initialization of the K-means algorithm can lead to wrong clusters. Based on the characteristics of EEGs, the MSK-means MSK-means algorithm initializes the coarse-scale centroid of a cluster with a suitable scale factor. In this chapter, the MSK-means algorithm is proved theoretically superior to the K-means algorithm on efficiency. In addition, three classifiers: the K-means, MSK-means MSK-means and support vector machine (SVM), are used to identify seizure and localize epileptogenic zone using delay permutation entropy features. The experimental results demonstrate that identifying seizure with the MSK-means algorithm and delay permutation entropy achieves 4. 7 % higher accuracy than that of K-means, and 0. 7 % higher accuracy than that of the SVM.

  3. Experimental industrial signal acquisition board in a large scientific device

    Science.gov (United States)

    Zeng, Xiangzhen; Ren, Bin

    2018-02-01

    In order to measure the industrial signal of neutrino experiment, a set of general-purpose industrial data acquisition board has been designed. It includes the function of switch signal input and output, and the function of analog signal input. The main components are signal isolation amplifier and filter circuit, ADC circuit, microcomputer systems and isolated communication interface circuit. Through the practical experiments, it shows that the system is flexible, reliable, convenient and economical, and the system has characters of high definition and strong anti-interference ability. Thus, the system fully meets the design requirements.

  4. Characterization of EEG Signals Using Wavelet Packet and Fuzzy Entropy in Motor Imagination Tasks

    Directory of Open Access Journals (Sweden)

    Boris Alexander Medina

    2017-05-01

    Full Text Available Context:  Clinical rhythm analysis on advanced signal processing methods is very important in medical areas such as brain disorder diagnostic, epilepsy, sleep analysis, anesthesia analysis, and more recently in brain-computer interfaces (BCI. Method: Wavelet transform package is used on this work to extract brain rhythms of electroencephalographic signals (EEG related to motor imagination tasks. We used the Competition BCI 2008 database for this characterization. Using statistical functions we obtained features that characterizes brain rhythms, which are discriminated using different classifiers; they were evaluated using a 10-fold cross validation criteria. Results: The classification accuracy achieved 81.11% on average, with a degree of agreement of 61%, indicating a "suitable" concordance, as it has been reported in the literature. An analysis of relevance showed the concentration of characteristics provided in the nodes as a result of Wavelet decomposition, as well as the characteristics that more information content contribute to improve the separability decision region for the classification task. Conclusions: The proposed method can be used as a reference to support future studies focusing on characterizing EEG signals oriented to the imagination of left and right hand movement, considering that our results proved to compared favourably to those reported in the literature. Language: Spanish.

  5. A Novel Method for Detection of Epilepsy in Short and Noisy EEG Signals Using Ordinal Pattern Analysis

    Directory of Open Access Journals (Sweden)

    Iman Veisi

    2010-03-01

    Full Text Available Introduction: In this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. Epilepsy is considered as a dynamical change in nonlinear and complex brain system. The ability of the proposed measure for characterizing the normal and epileptic EEG signals when the signal is short or is contaminated with noise is investigated and compared with some traditional chaos-based measures. Materials and Methods: In the proposed method, the phase space of the time series is reconstructed and then partitioned using ordinal patterns. The partitions can be labeled using a set of symbols. Therefore, the state trajectory is converted to a symbol sequence. A finite state machine is then constructed to model the sequence. A new complexity measure is proposed to detect dynamical changes using the state transition matrix of the state machine. The proposed complexity measure was applied to detect epilepsy in short and noisy EEG signals and the results were compared with some chaotic measures. Results: The results indicate that this complexity measure can distinguish normal and epileptic EEG signals with an accuracy of more than 97% for clean EEG and more than 75% for highly noised EEG signals. Discussion and Conclusion: The complexity measure can be computed in a very fast and easy way and, unlike traditional chaotic measures, is robust with respect to noise corrupting the data. This measure is also capable of dynamical change detection in short time series data.

  6. Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

    Directory of Open Access Journals (Sweden)

    Boon-Giin Lee

    2014-09-01

    Full Text Available Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals.

  7. Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal

    NARCIS (Netherlands)

    Radha, M.; Garcia Molina, G.; Poel, M.; Tononi, G.

    2014-01-01

    Automatic sleep staging on an online basis has recently emerged as a research topic motivated by fundamental sleep research. The aim of this paper is to find optimal signal processing methods and machine learning algorithms to achieve online sleep staging on the basis of a single EEG signal. The

  8. Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features

    Science.gov (United States)

    Nguyen, Chuong H.; Karavas, George K.; Artemiadis, Panagiotis

    2018-02-01

    Objective. In this paper, we investigate the suitability of imagined speech for brain-computer interface (BCI) applications. Approach. A novel method based on covariance matrix descriptors, which lie in Riemannian manifold, and the relevance vector machines classifier is proposed. The method is applied on electroencephalographic (EEG) signals and tested in multiple subjects. Main results. The method is shown to outperform other approaches in the field with respect to accuracy and robustness. The algorithm is validated on various categories of speech, such as imagined pronunciation of vowels, short words and long words. The classification accuracy of our methodology is in all cases significantly above chance level, reaching a maximum of 70% for cases where we classify three words and 95% for cases of two words. Significance. The results reveal certain aspects that may affect the success of speech imagery classification from EEG signals, such as sound, meaning and word complexity. This can potentially extend the capability of utilizing speech imagery in future BCI applications. The dataset of speech imagery collected from total 15 subjects is also published.

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

    Science.gov (United States)

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

    2016-03-01

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

  10. Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization.

    Science.gov (United States)

    Adam, Asrul; Shapiai, Mohd Ibrahim; Tumari, Mohd Zaidi Mohd; Mohamad, Mohd Saberi; Mubin, Marizan

    2014-01-01

    Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

  11. Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System.

    Science.gov (United States)

    Jiang, Yizhang; Wu, Dongrui; Deng, Zhaohong; Qian, Pengjiang; Wang, Jun; Wang, Guanjin; Chung, Fu-Lai; Choi, Kup-Sze; Wang, Shitong

    2017-12-01

    Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.

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

    Science.gov (United States)

    Oyama, Katsunori; Sakatani, Kaoru

    2016-01-01

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

  13. Wavelet based analysis of multi-electrode EEG-signals in epilepsy

    Science.gov (United States)

    Hein, Daniel A.; Tetzlaff, Ronald

    2005-06-01

    For many epilepsy patients seizures cannot sufficiently be controlled by an antiepileptic pharmacatherapy. Furthermore, only in small number of cases a surgical treatment may be possible. The aim of this work is to contribute to the realization of an implantable seizure warning device. By using recordings of electroenzephalographical(EEG) signals obtained from the department of epileptology of the University of Bonn we studied a recently proposed algorithm for the detection of parameter changes in nonlinear systems. Firstly, after calculating the crosscorrelation function between the signals of two electrodes near the epileptic focus, a wavelet-analysis follows using a sliding window with the so called Mexican-Hat wavelet. Then the Shannon-Entropy of the wavelet-transformed data has been determined providing the information content on a time scale in subject to the dilation of the wavelet-transformation. It shows distinct changes at the seizure onset for all dilations and for all patients.

  14. Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application

    Directory of Open Access Journals (Sweden)

    Angel Mur

    2016-04-01

    Full Text Available In this paper, we propose a new unsupervised method to automatically characterize and detect events in multichannel signals. This method is used to identify artifacts in electroencephalogram (EEG recordings of brain activity. The proposed algorithm has been evaluated and compared with a supervised method. To this end an example of the performance of the algorithm to detect artifacts is shown. The results show that although both methods obtain similar classification, the proposed method allows detecting events without training data and can also be applied in signals whose events are unknown a priori. Furthermore, the proposed method provides an optimal window whereby an optimal detection and characterization of events is found. The detection of events can be applied in real-time.

  15. Automatic characterization of sleep need dissipation dynamics using a single EEG signal.

    Science.gov (United States)

    Garcia-Molina, Gary; Bellesi, Michele; Riedner, Brady; Pastoor, Sander; Pfundtner, Stefan; Tononi, Giulio

    2015-01-01

    In the two-process model of sleep regulation, slow-wave activity (SWA, i.e. the EEG power in the 0.5-4 Hz frequency band) is considered a direct indicator of sleep need. SWA builds up during non-rapid eye movement (NREM) sleep, declines before the onset of rapid-eye-movement (REM) sleep, remains low during REM and the level of increase in successive NREM episodes gets progressively lower. Sleep need dissipates with a speed that is proportional to SWA and can be characterized in terms of the initial sleep need, and the decay rate. The goal in this paper is to automatically characterize sleep need from a single EEG signal acquired at a frontal location. To achieve this, a highly specific and reasonably sensitive NREM detection algorithm is proposed that leverages the concept of a single-class Kernel-based classifier. Using automatic NREM detection, we propose a method to estimate the decay rate and the initial sleep need. This method was tested on experimental data from 8 subjects who recorded EEG during three nights at home. We found that on average the estimates of the decay rate and the initial sleep need have higher values when automatic NREM detection was used as compared to manual NREM annotation. However, the average variability of these estimates across multiple nights of the same subject was lower when the automatic NREM detection classifier was used. While this method slightly over estimates the sleep need parameters, the reduced variability across subjects makes it more effective for within subject statistical comparisons of a given sleep intervention.

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

    OpenAIRE

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

    2017-01-01

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

  17. Predicting seizure by modeling synaptic plasticity based on EEG signals - a case study of inherited epilepsy

    Science.gov (United States)

    Zhang, Honghui; Su, Jianzhong; Wang, Qingyun; Liu, Yueming; Good, Levi; Pascual, Juan M.

    2018-03-01

    This paper explores the internal dynamical mechanisms of epileptic seizures through quantitative modeling based on full brain electroencephalogram (EEG) signals. Our goal is to provide seizure prediction and facilitate treatment for epileptic patients. Motivated by an earlier mathematical model with incorporated synaptic plasticity, we studied the nonlinear dynamics of inherited seizures through a differential equation model. First, driven by a set of clinical inherited electroencephalogram data recorded from a patient with diagnosed Glucose Transporter Deficiency, we developed a dynamic seizure model on a system of ordinary differential equations. The model was reduced in complexity after considering and removing redundancy of each EEG channel. Then we verified that the proposed model produces qualitatively relevant behavior which matches the basic experimental observations of inherited seizure, including synchronization index and frequency. Meanwhile, the rationality of the connectivity structure hypothesis in the modeling process was verified. Further, through varying the threshold condition and excitation strength of synaptic plasticity, we elucidated the effect of synaptic plasticity to our seizure model. Results suggest that synaptic plasticity has great effect on the duration of seizure activities, which support the plausibility of therapeutic interventions for seizure control.

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

    Science.gov (United States)

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

    2016-05-01

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

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

    Science.gov (United States)

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

    2016-02-01

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

  20. Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience

    Directory of Open Access Journals (Sweden)

    George J. A. Jiang

    2015-01-01

    Full Text Available Electroencephalogram (EEG signals, as it can express the human brain’s activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA. Bispectral (BIS index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD method and analyzed using sample entropy (SampEn analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN model through using expert assessment of consciousness level (EACL which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

  1. Autonomous data acquisition system for Paks NPP process noise signals

    International Nuclear Information System (INIS)

    Lipcsei, S.; Kiss, S.; Czibok, T.; Dezso, Z.; Horvath, Cs.

    2005-01-01

    A prototype of a new concept noise diagnostics data acquisition system has been developed recently to renew the aged present system. This new system is capable of collecting the whole available noise signal set simultaneously. Signal plugging and data acquisition are performed by autonomous systems (installed at each reactor unit) that are controlled through the standard plant network from a central computer installed at a suitable location. Experts can use this central unit to process and archive data series downloaded from the reactor units. This central unit also provides selected noise diagnostics information for other departments. The paper describes the hardware and software architecture of the new system in detail, emphasising the potential benefits of the new approach. (author)

  2. Wearable System for Acquisition and Monitoring of Biological Signals

    Science.gov (United States)

    Piccinini, D. J.; Andino, N. B.; Ponce, S. D.; Roberti, MA; López, y. N.

    2016-04-01

    This paper presents a modular, wearable system for acquisition and wireless transmission of biological signals. Configurable slaves for different signals (such as ECG, EMG, inertial sensors, and temperature) based in the ADS1294 Medical Analog Front End are connected to a Master, based in the CC3200 microcontroller, both from Texas Instruments. The slaves are configurable according to the specific application, providing versatility to the wearable system. The battery consumption is reduced, through a couple of Li-ion batteries and the circuit has also a battery charger. A custom made box was designed and fabricated in a 3D printer, preserving the requirements of low cost, low weight and safety recommendations.

  3. Epileptic seizure detection from EEG signals with phase-amplitude cross-frequency coupling and support vector machine

    Science.gov (United States)

    Liu, Yang; Wang, Jiang; Cai, Lihui; Chen, Yingyuan; Qin, Yingmei

    2018-03-01

    As a pattern of cross-frequency coupling (CFC), phase-amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB-MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

  4. Analyzing power spectral of electroencephalogram (EEG) signal to identify motoric arm movement using EMOTIV EPOC+

    Science.gov (United States)

    Bustomi, A.; Wijaya, S. K.; Prawito

    2017-07-01

    Rehabilitation of motoric dysfunction from the body becomes the main objective of developing Brain Computer Interface (BCI) technique, especially in the field of medical rehabilitation technology. BCI technology based on electrical activity of the brain, allow patient to be able to restore motoric disfunction of the body and help them to overcome the shortcomings mobility. In this study, EEG signal phenomenon was obtained from EMOTIV EPOC+, the signals were generated from the imagery of lifting arm, and look for any correlation between the imagery of motoric muscle movement against the recorded signals. The signals processing were done in the time-frequency domain, using Wavelet relative power (WRP) as feature extraction, and Support vector machine (SVM) as the classifier. In this study, it was obtained the result of maximum accuracy of 81.3 % using 8 channel (AF3, F7, F3, FC5, FC6, F4, F8, and AF4), 6 channel remaining on EMOTIV EPOC + does not contribute to the improvement of the accuracy of the classification system

  5. A hybrid BCI web browser based on EEG and EOG signals.

    Science.gov (United States)

    Shenghong He; Tianyou Yu; Zhenghui Gu; Yuanqing Li

    2017-07-01

    In this study, we propose a new web browser based on a hybrid brain computer interface (BCI) combining electroencephalographic (EEG) and electrooculography (EOG) signals. Specifically, the user can control the horizontal movement of the mouse by imagining left/right hand motion, and control the vertical movement of the mouse, select/reject a target, or input text in an edit box by blinking eyes in synchrony with the flashes of the corresponding buttons on the GUI. Based on mouse control, target selection and text input, the user can open a web page of interest, select an intended target in the web and read the page content. An online experiment was conducted involving five healthy subjects. The experimental results demonstrated the effectiveness of the proposed method.

  6. Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain.

    Science.gov (United States)

    Zhuang, Ning; Zeng, Ying; Tong, Li; Zhang, Chi; Zhang, Hanming; Yan, Bin

    2017-01-01

    This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) automatically. Multidimensional information of IMF is utilized as features, the first difference of time series, the first difference of phase, and the normalized energy. The performance of the proposed method is verified on a publicly available emotional database. The results show that the three features are effective for emotion recognition. The role of each IMF is inquired and we find that high frequency component IMF1 has significant effect on different emotional states detection. The informative electrodes based on EMD strategy are analyzed. In addition, the classification accuracy of the proposed method is compared with several classical techniques, including fractal dimension (FD), sample entropy, differential entropy, and discrete wavelet transform (DWT). Experiment results on DEAP datasets demonstrate that our method can improve emotion recognition performance.

  7. Identification Of The Epileptogenic Zone From Stereo-EEG Signals: A Connectivity-Graph Theory Approach

    Directory of Open Access Journals (Sweden)

    Ferruccio ePanzica

    2013-11-01

    Full Text Available In the context of focal drug-resistant epilepsies, the surgical resection of the epileptogenic zone (EZ, the cortical region responsible for the onset, early seizures organization and propagation, may be the only therapeutic option for reducing or suppressing seizures. The rather high rate of failure in epilepsy surgery of extra-temporal epilepsies highlights that the precise identification of the EZ, mandatory objective to achieve seizure freedom, is still an unsolved problem that requires more sophisticated methods of investigation.Despite the wide range of non-invasive investigations, intracranial stereo-EEG (SEEG recordings still represent, in many patients, the gold standard for the EZ identification. In this contest, the EZ localization is still based on visual analysis of SEEG, inevitably affected by the drawback of subjectivity and strongly time-consuming. Over the last years, considerable efforts have been made to develop advanced signal analysis techniques able to improve the identification of the EZ. Particular attention has been paid to those methods aimed at quantifying and characterising the interactions and causal relationships between neuronal populations, since is nowadays well assumed that epileptic phenomena are associated with abnormal changes in brain synchronisation mechanisms, and initial evidence has shown the suitability of this approach for the EZ localisation. The aim of this review is to provide an overview of the different EEG signal processing methods applied to study connectivity between distinct brain cortical regions, namely in focal epilepsies. In addition, with the aim of localizing the EZ, the approach based on graph theory will be described, since the study of the topological properties of the networks has strongly improved the study of brain connectivity mechanisms.

  8. A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal.

    Science.gov (United States)

    Oosugi, Naoya; Kitajo, Keiichi; Hasegawa, Naomi; Nagasaka, Yasuo; Okanoya, Kazuo; Fujii, Naotaka

    2017-09-01

    Blind source separation (BSS) algorithms extract neural signals from electroencephalography (EEG) data. However, it is difficult to quantify source separation performance because there is no criterion to dissociate neural signals and noise in EEG signals. This study develops a method for evaluating BSS performance. The idea is neural signals in EEG can be estimated by comparison with simultaneously measured electrocorticography (ECoG). Because the ECoG electrodes cover the majority of the lateral cortical surface and should capture most of the original neural sources in the EEG signals. We measured real EEG and ECoG data and developed an algorithm for evaluating BSS performance. First, EEG signals are separated into EEG components using the BSS algorithm. Second, the EEG components are ranked using the correlation coefficients of the ECoG regression and the components are grouped into subsets based on their ranks. Third, canonical correlation analysis estimates how much information is shared between the subsets of the EEG components and the ECoG signals. We used our algorithm to compare the performance of BSS algorithms (PCA, AMUSE, SOBI, JADE, fastICA) via the EEG and ECoG data of anesthetized nonhuman primates. The results (Best case >JADE = fastICA >AMUSE = SOBI ≥ PCA >random separation) were common to the two subjects. To encourage the further development of better BSS algorithms, our EEG and ECoG data are available on our Web site (http://neurotycho.org/) as a common testing platform. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  9. Wireless Data Acquisition of Transient Signals for Mobile Spectrometry Applications.

    Science.gov (United States)

    Trzcinski, Peter; Weagant, Scott; Karanassios, Vassili

    2016-05-01

    Wireless data acquisition using smartphones or handhelds offers increased mobility, it provides reduced size and weight, it has low electrical power requirements, and (in some cases) it has an ability to access the internet. Thus, it is well suited for mobile spectrometry applications using miniaturized, field-portable spectrometers, or detectors for chemical analysis in the field (i.e., on-site). There are four main wireless communications standards that can be used for wireless data acquisition, namely ZigBee, Bluetooth, Wi-Fi, and UWB (ultra-wide band). These are briefly reviewed and are evaluated for applicability to data acquisition of transient signals (i.e., time-domain) in the field (i.e., on-site) from a miniaturized, field-portable photomultiplier tube detector and from a photodiode array detector installed in a miniaturized, field-portable fiber optic spectrometer. These are two of the most widely used detectors for optical measurements in the ultraviolet-visible range of the spectrum. A miniaturized, 3D-printed, battery-operated microplasma-on-a-chip was used for generation of transient optical emission signals. Elemental analysis from liquid microsamples, a microplasma, and a handheld or a smartphone will be used as examples. Development and potential applicability of wireless data acquisition of transient optical emission signals for taking part of the lab to the sample types of mobile, field-portable spectrometry applications will be discussed. The examples presented are drawn from past and ongoing work in the authors' laboratory. A handheld or a smartphone were used as the mobile computing devices of choice. © The Author(s) 2016.

  10. Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task

    Directory of Open Access Journals (Sweden)

    Noor Kamal Al-Qazzaz

    2015-11-01

    Full Text Available We performed a comparative study to select the efficient mother wavelet (MWT basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM task recorded through electro-encephalography (EEG. Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20, Symlets (sym1–sym20, and Coiflets (coif1–coif5. Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions.

  11. A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals.

    Science.gov (United States)

    Tsiouris, Κostas Μ; Pezoulas, Vasileios C; Zervakis, Michalis; Konitsiotis, Spiros; Koutsouris, Dimitrios D; Fotiadis, Dimitrios I

    2018-05-17

    The electroencephalogram (EEG) is the most prominent means to study epilepsy and capture changes in electrical brain activity that could declare an imminent seizure. In this work, Long Short-Term Memory (LSTM) networks are introduced in epileptic seizure prediction using EEG signals, expanding the use of deep learning algorithms with convolutional neural networks (CNN). A pre-analysis is initially performed to find the optimal architecture of the LSTM network by testing several modules and layers of memory units. Based on these results, a two-layer LSTM network is selected to evaluate seizure prediction performance using four different lengths of preictal windows, ranging from 15 min to 2 h. The LSTM model exploits a wide range of features extracted prior to classification, including time and frequency domain features, between EEG channels cross-correlation and graph theoretic features. The evaluation is performed using long-term EEG recordings from the open CHB-MIT Scalp EEG database, suggest that the proposed methodology is able to predict all 185 seizures, providing high rates of seizure prediction sensitivity and low false prediction rates (FPR) of 0.11-0.02 false alarms per hour, depending on the duration of the preictal window. The proposed LSTM-based methodology delivers a significant increase in seizure prediction performance compared to both traditional machine learning techniques and convolutional neural networks that have been previously evaluated in the literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2016-03-01

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

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

    Science.gov (United States)

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

    2018-03-01

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

  14. Visual feature integration indicated by pHase-locked frontal-parietal EEG signals.

    Science.gov (United States)

    Phillips, Steven; Takeda, Yuji; Singh, Archana

    2012-01-01

    The capacity to integrate multiple sources of information is a prerequisite for complex cognitive ability, such as finding a target uniquely identifiable by the conjunction of two or more features. Recent studies identified greater frontal-parietal synchrony during conjunctive than non-conjunctive (feature) search. Whether this difference also reflects greater information integration, rather than just differences in cognitive strategy (e.g., top-down versus bottom-up control of attention), or task difficulty is uncertain. Here, we examine the first possibility by parametrically varying the number of integrated sources from one to three and measuring phase-locking values (PLV) of frontal-parietal EEG electrode signals, as indicators of synchrony. Linear regressions, under hierarchical false-discovery rate control, indicated significant positive slopes for number of sources on PLV in the 30-38 Hz, 175-250 ms post-stimulus frequency-time band for pairs in the sagittal plane (i.e., F3-P3, Fz-Pz, F4-P4), after equating conditions for behavioural performance (to exclude effects due to task difficulty). No such effects were observed for pairs in the transverse plane (i.e., F3-F4, C3-C4, P3-P4). These results provide support for the idea that anterior-posterior phase-locking in the lower gamma-band mediates integration of visual information. They also provide a potential window into cognitive development, seen as developing the capacity to integrate more sources of information.

  15. Wavelet-based study of valence-arousal model of emotions on EEG signals with LabVIEW.

    Science.gov (United States)

    Guzel Aydin, Seda; Kaya, Turgay; Guler, Hasan

    2016-06-01

    This paper illustrates the wavelet-based feature extraction for emotion assessment using electroencephalogram (EEG) signal through graphical coding design. Two-dimensional (valence-arousal) emotion model was studied. Different emotions (happy, joy, melancholy, and disgust) were studied for assessment. These emotions were stimulated by video clips. EEG signals obtained from four subjects were decomposed into five frequency bands (gamma, beta, alpha, theta, and delta) using "db5" wavelet function. Relative features were calculated to obtain further information. Impact of the emotions according to valence value was observed to be optimal on power spectral density of gamma band. The main objective of this work is not only to investigate the influence of the emotions on different frequency bands but also to overcome the difficulties in the text-based program. This work offers an alternative approach for emotion evaluation through EEG processing. There are a number of methods for emotion recognition such as wavelet transform-based, Fourier transform-based, and Hilbert-Huang transform-based methods. However, the majority of these methods have been applied with the text-based programming languages. In this study, we proposed and implemented an experimental feature extraction with graphics-based language, which provides great convenience in bioelectrical signal processing.

  16. Analysis of EEG signals regularity in adults during video game play in 2D and 3D.

    Science.gov (United States)

    Khairuddin, Hamizah R; Malik, Aamir S; Mumtaz, Wajid; Kamel, Nidal; Xia, Likun

    2013-01-01

    Video games have long been part of the entertainment industry. Nonetheless, it is not well known how video games can affect us with the advancement of 3D technology. The purpose of this study is to investigate the EEG signals regularity when playing video games in 2D and 3D modes. A total of 29 healthy subjects (24 male, 5 female) with mean age of 21.79 (1.63) years participated. Subjects were asked to play a car racing video game in three different modes (2D, 3D passive and 3D active). In 3D passive mode, subjects needed to wear a passive polarized glasses (cinema type) while for 3D active, an active shutter glasses was used. Scalp EEG data was recorded during game play using 19-channel EEG machine and linked ear was used as reference. After data were pre-processed, the signal irregularity for all conditions was computed. Two parameters were used to measure signal complexity for time series data: i) Hjorth-Complexity and ii) Composite Permutation Entropy Index (CPEI). Based on these two parameters, our results showed that the complexity level increased from eyes closed to eyes open condition; and further increased in the case of 3D as compared to 2D game play.

  17. Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal.

    Science.gov (United States)

    Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Cumming, Paul; Mubin, Marizan

    2016-01-01

    Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37-52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model.

  18. Studies of the Coefficient of Variation of the Magnitude of EEG Signals

    National Research Council Canada - National Science Library

    Round, W

    2001-01-01

    ... out. The coefficient of variation (CoV), i.e. the standard deviation/mean, within 10 second epochs was found to be quite constant throughout the whole of the EEG recordings and was typically about 0.46...

  19. [Training cortical signals by means of a BMI-EEG system, its evolution and intervention. A case report].

    Science.gov (United States)

    Monge-Pereira, E; Casatorres Perez-Higueras, I; Fernandez-Gonzalez, P; Ibanez-Pereda, J; Serrano, J I; Molina-Rueda, F

    2017-04-16

    In the last years, new technologies such as the brain-machine interfaces (BMI) have been incorporated in the rehabilitation process of subjects with stroke. These systems are able to detect motion intention, analyzing the cortical signals using different techniques such as the electroencephalography (EEG). This information could guide different interfaces such as robotic devices, electrical stimulation or virtual reality. A 40 years-old man with stroke with two months from the injury participated in this study. We used a BMI based on EEG. The subject's motion intention was analyzed calculating the event-related desynchronization. The upper limb motor function was evaluated with the Fugl-Meyer Assessment and the participant's satisfaction was evaluated using the QUEST 2.0. The intervention using a physical therapist as an interface was carried out without difficulty. The BMI systems detect cortical changes in a subacute stroke subject. These changes are coherent with the evolution observed using the Fugl-Meyer Assessment.

  20. The analysis of the influence of fractal structure of stimuli on fractal dynamics in fixational eye movements and EEG signal

    Science.gov (United States)

    Namazi, Hamidreza; Kulish, Vladimir V.; Akrami, Amin

    2016-05-01

    One of the major challenges in vision research is to analyze the effect of visual stimuli on human vision. However, no relationship has been yet discovered between the structure of the visual stimulus, and the structure of fixational eye movements. This study reveals the plasticity of human fixational eye movements in relation to the ‘complex’ visual stimulus. We demonstrated that the fractal temporal structure of visual dynamics shifts towards the fractal dynamics of the visual stimulus (image). The results showed that images with higher complexity (higher fractality) cause fixational eye movements with lower fractality. Considering the brain, as the main part of nervous system that is engaged in eye movements, we analyzed the governed Electroencephalogram (EEG) signal during fixation. We have found out that there is a coupling between fractality of image, EEG and fixational eye movements. The capability observed in this research can be further investigated and applied for treatment of different vision disorders.

  1. FireSignal - Data Acquisition and Control System Software

    International Nuclear Information System (INIS)

    Neto, A.; Fernandes, H.; Duarte, A.; Carvalho, B.; Sousa, J.; Valcarcel, D.; Varandas, C.; Hron, M.

    2006-01-01

    Control of fusion devices requires good, non-ambiguous, easy to use user-interfaces to configure hardware devices. To solve this problem a highly generic system for data control and acquisition has been developed. Among the main features it allows remote hardware configuration, shot launching, data sharing between connected users and experiment monitoring. The system is fully distributed: the hardware driver nodes, clients and server are completely independent from each other and might be running in different operating systems and programmed in different languages. All the communication is provided through the Common Object Request Broker Architecture (CORBA) protocol. FireSignal was designed from the beginning to be as independent as possible from any kind of constraints as it's a plugin based system. Database, data viewers and the security system are some examples of what can easily be changed and adapted to the target machine's needs. All hardware is described in eXtendend Markup Language (XML) and from this information the FireSignal client application can build automatically Graphical User Interfaces (GUI) and validate the user's parameter configuration. Any type of hardware can be integrated in the system as long as it is described in XML and the respective driver is developed. Any modern programming language can be used to develop these drivers, and currently we use Python and Java generic drivers. All data storage and indexing is time stamped event-based s. Nodes are responsible for tagging the acquired samples with the absolute time stamps and to react to machine events. FireSignal is currently being used to control the ISTTOK/PT and CASTOR/CZ tokamaks. (author)

  2. Diagnostic Accuracy of microEEG: A Miniature, Wireless EEG Device

    OpenAIRE

    Grant, Arthur C.; Abdel-Baki, Samah G.; Omurtag, Ahmet; Sinert, Richard; Chari, Geetha; Malhotra, Schweta; Weedon, Jeremy; Fenton, Andre A.; Zehtabchi, Shahriar

    2014-01-01

    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. ...

  3. Spatial attention related SEP amplitude modulations covary with BOLD signal in S1--a simultaneous EEG--fMRI study.

    Science.gov (United States)

    Schubert, Ruth; Ritter, Petra; Wüstenberg, Torsten; Preuschhof, Claudia; Curio, Gabriel; Sommer, Werner; Villringer, Arno

    2008-11-01

    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.

  4. FireSignal-Data acquisition and control system software

    Energy Technology Data Exchange (ETDEWEB)

    Neto, A. [Associacao Euratom/IST, Centro de Fusao Nuclear, Av. Rovisco Pais, P-1049-001 Lisboa (Portugal)], E-mail: andre.neto@cfn.ist.utl.pt; Fernandes, H.; Duarte, A.; Carvalho, B.B.; Sousa, J.; Valcarcel, D.F. [Associacao Euratom/IST, Centro de Fusao Nuclear, Av. Rovisco Pais, P-1049-001 Lisboa (Portugal); Hron, M. [Asociace EURATOM IPP.CR, Prague (Czech Republic); Varandas, C.A.F. [Associacao Euratom/IST, Centro de Fusao Nuclear, Av. Rovisco Pais, P-1049-001 Lisboa (Portugal)

    2007-10-15

    Control of fusion experiments requires non-ambiguous, easy to use, user-interfaces to configure hardware devices. With that aim, a highly generic system for data control and acquisition has been developed. Among the main features it allows remote hardware configuration, shot launching, data sharing between connected users and experiment monitoring. The system is fully distributed: the hardware driver nodes, clients and servers are completely independent from each other and might run in different operating systems and programmed in different languages. All the communication is provided through the Common Object Request Broker Architecture (CORBA) protocol. FireSignal is designed to be as independent as possible from any kind of constraints as it is a plugin based system. Database, data viewers and the security system are some examples of what can easily be changed and adapted to the target machine's needs. In this system, every hardware is described in eXtensible Markup Language (XML) and with this information Graphical User Interfaces (GUI) are automatically built and user's parameter configuration validated. Any type of hardware device can be integrated in the system as long as it is described in XML and the respective driver developed. Any modern programming language can be used to develop these drivers. Currently Python and Java generic drivers are used. Data storage and indexing is time stamp event-based. Nodes are responsible for tagging the acquired samples with the absolute time stamps and to react to machine events. FireSignal is currently being used to control the ISTTOK/PT and CASTOR/CZ tokamaks.

  5. FireSignal-Data acquisition and control system software

    International Nuclear Information System (INIS)

    Neto, A.; Fernandes, H.; Duarte, A.; Carvalho, B.B.; Sousa, J.; Valcarcel, D.F.; Hron, M.; Varandas, C.A.F.

    2007-01-01

    Control of fusion experiments requires non-ambiguous, easy to use, user-interfaces to configure hardware devices. With that aim, a highly generic system for data control and acquisition has been developed. Among the main features it allows remote hardware configuration, shot launching, data sharing between connected users and experiment monitoring. The system is fully distributed: the hardware driver nodes, clients and servers are completely independent from each other and might run in different operating systems and programmed in different languages. All the communication is provided through the Common Object Request Broker Architecture (CORBA) protocol. FireSignal is designed to be as independent as possible from any kind of constraints as it is a plugin based system. Database, data viewers and the security system are some examples of what can easily be changed and adapted to the target machine's needs. In this system, every hardware is described in eXtensible Markup Language (XML) and with this information Graphical User Interfaces (GUI) are automatically built and user's parameter configuration validated. Any type of hardware device can be integrated in the system as long as it is described in XML and the respective driver developed. Any modern programming language can be used to develop these drivers. Currently Python and Java generic drivers are used. Data storage and indexing is time stamp event-based. Nodes are responsible for tagging the acquired samples with the absolute time stamps and to react to machine events. FireSignal is currently being used to control the ISTTOK/PT and CASTOR/CZ tokamaks

  6. Radiation-hardened fast acquisition/weak signal tracking system and method

    Science.gov (United States)

    Winternitz, Luke (Inventor); Boegner, Gregory J. (Inventor); Sirotzky, Steve (Inventor)

    2009-01-01

    A global positioning system (GPS) receiver and method of acquiring and tracking GPS signals comprises an antenna adapted to receive GPS signals; an analog radio frequency device operatively connected to the antenna and adapted to convert the GPS signals from an analog format to a digital format; a plurality of GPS signal tracking correlators operatively connected to the analog RF device; a GPS signal acquisition component operatively connected to the analog RF device and the plurality of GPS signal tracking correlators, wherein the GPS signal acquisition component is adapted to calculate a maximum vector on a databit correlation grid; and a microprocessor operatively connected to the plurality of GPS signal tracking correlators and the GPS signal acquisition component, wherein the microprocessor is adapted to compare the maximum vector with a predetermined correlation threshold to allow the GPS signal to be fully acquired and tracked.

  7. Kurtosis based blind source extraction of complex noncircular signals with application in EEG artifact removal in real-time

    Directory of Open Access Journals (Sweden)

    Soroush eJavidi

    2011-10-01

    Full Text Available A new class of complex domain blind source extraction (BSE algorithms suitable for the extraction of both circular and noncircular complex signals is proposed. This is achieved through sequential extraction based on the degree of kurtosis and in the presence of noncircular measurement noise. The existence and uniqueness analysis of the solution is followed by a study of fast converging variants of the algorithm. The performance is first assessed through simulations on well understood benchmark signals, followed by a case study on real-time artifact removal from EEG signals, verified using both qualitative and quantitative metrics. The results illustrate the power of the proposed approach in real-time blind extraction of general complex-valued sources.

  8. Modelling cardiac signal as a confound in EEG-fMRI and its application in focal epilepsy studies

    DEFF Research Database (Denmark)

    Liston, A. D.; Ellegaard Lund, Torben; Salek-Haddadi, A

    2006-01-01

    effects to be modelled, as effects of no interest. Our model is based on an over-complete basis set covering a linear relationship between cardiac-related MR signal and the phase of the cardiac cycle or time after pulse (TAP). This method showed that, on average, 24.6 +/- 10.9% of grey matter voxels......Cardiac noise has been shown to reduce the sensitivity of functional Magnetic Resonance Imaging (fMRI) to an experimental effect due to its confounding presence in the blood oxygenation level-dependent (BOLD) signal. Its effect is most severe in particular regions of the brain and a method is yet...... to take it into account in routine fMRI analysis. This paper reports the development of a general and robust technique to improve the reliability of EEG-fMRI studies to BOLD signal correlated with interictal epileptiform discharges (IEDs). In these studies, ECG is routinely recorded, enabling cardiac...

  9. Low Computational Signal Acquisition for GNSS Receivers Using a Resampling Strategy and Variable Circular Correlation Time

    Directory of Open Access Journals (Sweden)

    Yeqing Zhang

    2018-02-01

    Full Text Available For the objective of essentially decreasing computational complexity and time consumption of signal acquisition, this paper explores a resampling strategy and variable circular correlation time strategy specific to broadband multi-frequency GNSS receivers. In broadband GNSS receivers, the resampling strategy is established to work on conventional acquisition algorithms by resampling the main lobe of received broadband signals with a much lower frequency. Variable circular correlation time is designed to adapt to different signal strength conditions and thereby increase the operation flexibility of GNSS signal acquisition. The acquisition threshold is defined as the ratio of the highest and second highest correlation results in the search space of carrier frequency and code phase. Moreover, computational complexity of signal acquisition is formulated by amounts of multiplication and summation operations in the acquisition process. Comparative experiments and performance analysis are conducted on four sets of real GPS L2C signals with different sampling frequencies. The results indicate that the resampling strategy can effectively decrease computation and time cost by nearly 90–94% with just slight loss of acquisition sensitivity. With circular correlation time varying from 10 ms to 20 ms, the time cost of signal acquisition has increased by about 2.7–5.6% per millisecond, with most satellites acquired successfully.

  10. Low Computational Signal Acquisition for GNSS Receivers Using a Resampling Strategy and Variable Circular Correlation Time

    Science.gov (United States)

    Zhang, Yeqing; Wang, Meiling; Li, Yafeng

    2018-01-01

    For the objective of essentially decreasing computational complexity and time consumption of signal acquisition, this paper explores a resampling strategy and variable circular correlation time strategy specific to broadband multi-frequency GNSS receivers. In broadband GNSS receivers, the resampling strategy is established to work on conventional acquisition algorithms by resampling the main lobe of received broadband signals with a much lower frequency. Variable circular correlation time is designed to adapt to different signal strength conditions and thereby increase the operation flexibility of GNSS signal acquisition. The acquisition threshold is defined as the ratio of the highest and second highest correlation results in the search space of carrier frequency and code phase. Moreover, computational complexity of signal acquisition is formulated by amounts of multiplication and summation operations in the acquisition process. Comparative experiments and performance analysis are conducted on four sets of real GPS L2C signals with different sampling frequencies. The results indicate that the resampling strategy can effectively decrease computation and time cost by nearly 90–94% with just slight loss of acquisition sensitivity. With circular correlation time varying from 10 ms to 20 ms, the time cost of signal acquisition has increased by about 2.7–5.6% per millisecond, with most satellites acquired successfully. PMID:29495301

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

    Science.gov (United States)

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

    2010-04-27

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

  12. Nuclear spectrometry signal acquisition and processing system based on LabVIEW and C

    International Nuclear Information System (INIS)

    Chen Xiaojun; Fang Fang; Chen Mingchi; Jiang Zancheng; Wang Min

    2008-01-01

    The process of designing nuclear spectrometry signal acquisition and processing system based on virtual instrument technology is showed in this article. For the deficiency of LabVIEW in big data analyzing and processing, a method is presented in which C programmer is inserted and applied in signal smoothing, peak searching and area of the peak calculating. A complete nuclear spectrometry signal acquisition, processing and document management system is implemented. (authors)

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

  14. Data acquisition system of 16-channel EEG based on ATSAM3X8E ARM Cortex-M3 32-bit microcontroller and ADS1299

    Science.gov (United States)

    Toresano, L. O. H. Z.; Wijaya, S. K.; Prawito, Sudarmaji, A.; Badri, C.

    2017-07-01

    The prototype of the EEG (electroencephalogram) instrumentation systems has been developed based on 32-bit microcontrollers of Cortex-M3 ATSAM3X8E and Analog Front-End (AFE) ADS1299 (Texas Instruments, USA), and also consists of 16-channel dry-electrodes in the form of EEG head-caps. The ADS1299-AFE has been designed in a double-layer format PCB (Print Circuit Board) with daisy-chain configuration. The communication protocol of the prototype was based on SPI (Serial Peripheral Interface) and tested using USB SPI-Logic Analyzer Hantek4032L (Qingdao Hantek Electronic, China). The acquired data of the 16-channel from this prototype has been successfully transferred to a PC (Personal Computer) with accuracy greater than 91 %. The data acquisition system has been visualized with time-domain format in the multi-graph plotter, the frequency-domain based on FFT (Fast Fourier Transform) calculation, and also brain-mapping display of 16-channel. The GUI (Graphical User Interface) has been developed based on OpenBCI (Brain Computer Interface) using Java Processing and also can be stored of data in the *.txt format. Instrumentation systems have been tested in the frequency range of 1-50 Hz using MiniSim 330 EEG Simulator (NETECH, USA). The validation process has been done with different frequency of 0.1 Hz, 2 Hz, 5 Hz, and 50 Hz, and difference voltage amplitudes of 10 µV, 30 µV, 50 µV, 100 µV, 500 µV, 1 mV, 2 mV and 2.5 mV. However, the acquisition system was not optimal at a frequency of 0.1 Hz and for amplitude potentials of over 1 mV had differences of the order 10 µV.

  15. Comparison of Amplitude-Integrated EEG and Conventional EEG in a Cohort of Premature Infants.

    Science.gov (United States)

    Meledin, Irina; Abu Tailakh, Muhammad; Gilat, Shlomo; Yogev, Hagai; Golan, Agneta; Novack, Victor; Shany, Eilon

    2017-03-01

    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 continuity and amplitude.

  16. [Detection of quadratic phase coupling between EEG signal components by nonparamatric and parametric methods of bispectral analysis].

    Science.gov (United States)

    Schmidt, K; Witte, H

    1999-11-01

    Recently the assumption of the independence of individual frequency components in a signal has been rejected, for example, for the EEG during defined physiological states such as sleep or sedation [9, 10]. Thus, the use of higher-order spectral analysis capable of detecting interrelations between individual signal components has proved useful. The aim of the present study was to investigate the quality of various non-parametric and parametric estimation algorithms using simulated as well as true physiological data. We employed standard algorithms available for the MATLAB. The results clearly show that parametric bispectral estimation is superior to non-parametric estimation in terms of the quality of peak localisation and the discrimination from other peaks.

  17. MATLAB simulation software used for the PhD thesis "Acquisition of Multi-Band Signals via Compressed Sensing

    DEFF Research Database (Denmark)

    2014-01-01

    MATLAB simulation software used for the PhD thesis "Acquisition of Multi-Band Signals via Compressed Sensing......MATLAB simulation software used for the PhD thesis "Acquisition of Multi-Band Signals via Compressed Sensing...

  18. EEG-Informed fMRI: A Review of Data Analysis Methods

    Science.gov (United States)

    Abreu, Rodolfo; Leal, Alberto; Figueiredo, Patrícia

    2018-01-01

    The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest. PMID:29467634

  19. EEG-Informed fMRI: A Review of Data Analysis Methods

    Directory of Open Access Journals (Sweden)

    Rodolfo Abreu

    2018-02-01

    Full Text Available The simultaneous acquisition of electroencephalography (EEG with functional magnetic resonance imaging (fMRI is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.

  20. Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia

    Directory of Open Access Journals (Sweden)

    Jiann-Shing Shieh

    2013-08-01

    Full Text Available EEG (Electroencephalography signals can express the human awareness activities and consequently it can indicate the depth of anesthesia. On the other hand, Bispectral-index (BIS is often used as an indicator to assess the depth of anesthesia. This study is aimed at using an advanced signal processing method to analyze EEG signals and compare them with existing BIS indexes from a commercial product (i.e., IntelliVue MP60 BIS module. Multivariate empirical mode decomposition (MEMD algorithm is utilized to filter the EEG signals. A combination of two MEMD components (IMF2 + IMF3 is used to express the raw EEG. Then, sample entropy algorithm is used to calculate the complexity of the patients’ EEG signal. Furthermore, linear regression and artificial neural network (ANN methods were used to model the sample entropy using BIS index as the gold standard. ANN can produce better target value than linear regression. The correlation coefficient is 0.790 ± 0.069 and MAE is 8.448 ± 1.887. In conclusion, the area under the receiver operating characteristic (ROC curve (AUC of sample entropy value using ANN and MEMD is 0.969 ± 0.028 while the AUC of sample entropy value without filter is 0.733 ± 0.123. It means the MEMD method can filter out noise of the brain waves, so that the sample entropy of EEG can be closely related to the depth of anesthesia. Therefore, the resulting index can be adopted as the reference for the physician, in order to reduce the risk of surgery.

  1. Low level signal data acquisition for the MFTF-B superconducting magnet system

    International Nuclear Information System (INIS)

    Montoya, C.R.

    1984-01-01

    Acquisition of low level signals from sensors mounted on the superconducting magnets in the Tandem Mirror Fusion Test Facility (MFTF-B) impose very strict requirements on the magnet signal conditioning and data acquisition system. Of the various types of sensors required, thermocouples and strain gages produce very low level outputs. These low level outputs must be accurately measured in the harsh environment of slowly varying magnetic fields, cryogenic temperatures, high vacuum, 80 kV pulse power, 60 Hz, 17 MHz and 28, 35, and 56 GHz electrical noise and possible neutron radiation. Successful measurements require careful attention to grounding, shielding, signal handling and processing in the data acquisition system. The magnet instrumentation system provides a means of effectively measuring both low level signals and high level signals from all types of sensors. Various methods involved in the design and implementation of the system for signal conditioning and data gathering will be presented

  2. An overview of data acquisition, signal coding and data analysis techniques for MST radars

    Science.gov (United States)

    Rastogi, P. K.

    1986-01-01

    An overview is given of the data acquisition, signal processing, and data analysis techniques that are currently in use with high power MST/ST (mesosphere stratosphere troposphere/stratosphere troposphere) radars. This review supplements the works of Rastogi (1983) and Farley (1984) presented at previous MAP workshops. A general description is given of data acquisition and signal processing operations and they are characterized on the basis of their disparate time scales. Then signal coding, a brief description of frequently used codes, and their limitations are discussed, and finally, several aspects of statistical data processing such as signal statistics, power spectrum and autocovariance analysis, outlier removal techniques are discussed.

  3. Electroencephalography (EEG) Based Control in Assistive Mobile Robots: A Review

    International Nuclear Information System (INIS)

    Krishnan, N Murali; Mariappan, Muralindran; Muthukaruppan, Karthigayan; Hijazi, Mohd Hanafi Ahmad; Kitt, Wong Wei

    2016-01-01

    Recently, EEG based control in assistive robot usage has been gradually increasing in the area of biomedical field for giving quality and stress free life for disabled and elderly people. This study reviews the deployment of EGG based control in assistive robots, especially for those who in need and neurologically disabled. The main objective of this paper is to describe the methods used for (i) EEG data acquisition and signal preprocessing, (ii) feature extraction and (iii) signal classification methods. Besides that, this study presents the specific research challenges in the designing of these control systems and future research directions. (paper)

  4. Low-level-signal data acquisition for the MFTF superconducting-magnet system

    International Nuclear Information System (INIS)

    Montoya, C.R.

    1981-01-01

    Acquisition of low level signals from sensors mounted on the superconducting yin-yang magnet in the Mirror Fusion Test Facility (MFTF) imposes very strict requirements on the magnet signal conditioning and data acquisition system. Of the various types of sensors required, thermocouples, strain gages, and voltage taps produce very low level outputs. These low level outputs must be accurately measured in the harsh environment of slowly varying magnetic fields, cryogenic temperatures, high vacuum, pulse power and 60 Hz electrical noise, possible neutron radiation, and high common mode voltage resulting from superconducting magnet quench. Successful measurements require careful attention to grounding, shielding, signal handling and processing in the data acquisition system. The magnet instrumentation system provides a means of effectively measuring both low level signals and high level signals from all types of sensors

  5. A Brain–Computer Interface for Potential Nonverbal Facial Communication Based on EEG Signals Related to Specific Emotions

    Directory of Open Access Journals (Sweden)

    Koji eKashihara

    2014-08-01

    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.

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

    Science.gov (United States)

    Kashihara, Koji

    2014-01-01

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

  7. Single-trial EEG-informed fMRI reveals spatial dependency of BOLD signal on early and late IC-ERP amplitudes during face recognition.

    Science.gov (United States)

    Wirsich, Jonathan; Bénar, Christian; Ranjeva, Jean-Philippe; Descoins, Médéric; Soulier, Elisabeth; Le Troter, Arnaud; Confort-Gouny, Sylviane; Liégeois-Chauvel, Catherine; Guye, Maxime

    2014-10-15

    Simultaneous EEG-fMRI has opened up new avenues for improving the spatio-temporal resolution of functional brain studies. However, this method usually suffers from poor EEG quality, especially for evoked potentials (ERPs), due to specific artifacts. As such, the use of EEG-informed fMRI analysis in the context of cognitive studies has particularly focused on optimizing narrow ERP time windows of interest, which ignores the rich diverse temporal information of the EEG signal. Here, we propose to use simultaneous EEG-fMRI to investigate the neural cascade occurring during face recognition in 14 healthy volunteers by using the successive ERP peaks recorded during the cognitive part of this process. N170, N400 and P600 peaks, commonly associated with face recognition, were successfully and reproducibly identified for each trial and each subject by using a group independent component analysis (ICA). For the first time we use this group ICA to extract several independent components (IC) corresponding to the sequence of activation and used single-trial peaks as modulation parameters in a general linear model (GLM) of fMRI data. We obtained an occipital-temporal-frontal stream of BOLD signal modulation, in accordance with the three successive IC-ERPs providing an unprecedented spatio-temporal characterization of the whole cognitive process as defined by BOLD signal modulation. By using this approach, the pattern of EEG-informed BOLD modulation provided improved characterization of the network involved than the fMRI-only analysis or the source reconstruction of the three ERPs; the latter techniques showing only two regions in common localized in the occipital lobe. Copyright © 2014 Elsevier Inc. All rights reserved.

  8. Full-field wrist pulse signal acquisition and analysis by 3D Digital Image Correlation

    Science.gov (United States)

    Xue, Yuan; Su, Yong; Zhang, Chi; Xu, Xiaohai; Gao, Zeren; Wu, Shangquan; Zhang, Qingchuan; Wu, Xiaoping

    2017-11-01

    Pulse diagnosis is an essential part in four basic diagnostic methods (inspection, listening, inquiring and palpation) in traditional Chinese medicine, which depends on longtime training and rich experience, so computerized pulse acquisition has been proposed and studied to ensure the objectivity. To imitate the process that doctors using three fingertips with different pressures to feel fluctuations in certain areas containing three acupoints, we established a five dimensional pulse signal acquisition system adopting a non-contacting optical metrology method, 3D digital image correlation, to record the full-field displacements of skin fluctuations under different pressures. The system realizes real-time full-field vibration mode observation with 10 FPS. The maximum sample frequency is 472 Hz for detailed post-processing. After acquisition, the signals are analyzed according to the amplitude, pressure, and pulse wave velocity. The proposed system provides a novel optical approach for digitalizing pulse diagnosis and massive pulse signal data acquisition for various types of patients.

  9. Close relationship between fMRI signals and transient heart rate changes accompanying K-complex. Simultaneous EEG/fMRI study

    International Nuclear Information System (INIS)

    Kan, Shigeyuki; Koike, Takahiko; Miyauchi, Satoru; Misaki, Masaya

    2009-01-01

    Combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) allows the investigation of spontaneous activities in the human brain. Recently, by using this technique, increases in fMRI signal accompanying transient EEG activities such as sleep spindles and slow waves were reported. Although these fMRI signal increases appear to arise as a result of the neural activities being reflected in the EEG, when the influence of physiological activities upon fMRI signals are taken into consideration, it is highly controversial that fMRI signal increases accompanying transient EEG activities reflect actual neural activities. In the present study, we conducted simultaneous fMRI and polysomnograph recording of 18 normal adults, to study the effect of transient heart rate changes after a K-complex on fMRI signals. Significant fMRI signal increase was observed in the cerebellum, the ventral thalamus, the dorsal part of the brainstem, the periventricular white matter and the ventricle (quadrigeminal cistern). On the other hand, significant fMRI signal decrease was observed only in the right insula. Moreover, intensities of fMRI signal increase that was accompanied by a K-complex correlated positively with the magnitude of heart rate changes after a K-complex. Previous studies have reported that K-complex is closely related with sympathetic nervous activity and that the attributes of perfusion regulation in the brain differ during wakefulness and sleep. By taking these findings into consideration, our present results indicate that a close relationship exists between a K-complex and the changes in cardio- and neurovascular regulations that are mediated by the autonomic nervous system during sleep; further, these results indicate that transient heart rate changes after a K-complex can affect the fMRI signal generated in certain brain regions. (author)

  10. Amplitude spectrum EEG signal evidence for the dissociation of motor and perceptual spatial working memory in the human brain.

    Science.gov (United States)

    Smyrnis, Nikolaos; Protopapa, Foteini; Tsoukas, Evangelos; Balogh, Allison; Siettos, Constantinos I; Evdokimidis, Ioannis

    2014-02-01

    This study investigated the question whether spatial working memory related to movement plans (motor working memory) and spatial working memory related to spatial attention and perceptual processes (perceptual spatial working memory) share the same neurophysiological substrate or there is evidence for separate motor and perceptual working memory streams of processing. Towards this aim, ten healthy human subjects performed delayed responses to visual targets presented at different spatial locations. Two tasks were attained, one in which the spatial location of the target was the goal for a pointing movement and one in which the spatial location of the target was used for a perceptual (yes or no) change detection. Each task involved two conditions: a memory condition in which the target remained visible only for the first 250 ms of the delay period and a delay condition in which the target location remained visible throughout the delay period. The amplitude spectrum analysis of the EEG revealed that the alpha (8-12 Hz) band signal was smaller, while the beta (13-30 Hz) and gamma (30-45 Hz) band signals were larger in the memory compared to the non-memory condition. The alpha band signal difference was confined to the frontal midline area; the beta band signal difference extended over the right hemisphere and midline central area, and the gamma band signal difference was confined to the right occipitoparietal area. Importantly, both in beta and gamma bands, we observed a significant increase in the movement-related compared to the perceptual-related memory-specific amplitude spectrum signal in the central midline area. This result provides clear evidence for the dissociation of motor and perceptual spatial working memory.

  11. Characterization of Harmonic Signal Acquisition with Parallel Dipole and Multipole Detectors

    Science.gov (United States)

    Park, Sung-Gun; Anderson, Gordon A.; Bruce, James E.

    2018-04-01

    Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) is a powerful instrument for the study of complex biological samples due to its high resolution and mass measurement accuracy. However, the relatively long signal acquisition periods needed to achieve high resolution can serve to limit applications of FTICR-MS. The use of multiple pairs of detector electrodes enables detection of harmonic frequencies present at integer multiples of the fundamental cyclotron frequency, and the obtained resolving power for a given acquisition period increases linearly with the order of harmonic signal. However, harmonic signal detection also increases spectral complexity and presents challenges for interpretation. In the present work, ICR cells with independent dipole and harmonic detection electrodes and preamplifiers are demonstrated. A benefit of this approach is the ability to independently acquire fundamental and multiple harmonic signals in parallel using the same ions under identical conditions, enabling direct comparison of achieved performance as parameters are varied. Spectra from harmonic signals showed generally higher resolving power than spectra acquired with fundamental signals and equal signal duration. In addition, the maximum observed signal to noise (S/N) ratio from harmonic signals exceeded that of fundamental signals by 50 to 100%. Finally, parallel detection of fundamental and harmonic signals enables deconvolution of overlapping harmonic signals since observed fundamental frequencies can be used to unambiguously calculate all possible harmonic frequencies. Thus, the present application of parallel fundamental and harmonic signal acquisition offers a general approach to improve utilization of harmonic signals to yield high-resolution spectra with decreased acquisition time. [Figure not available: see fulltext.

  12. DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

    Science.gov (United States)

    Lawhern, Vernon; Hairston, W David; Robbins, Kay

    2013-01-01

    Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration.

  13. DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

    Directory of Open Access Journals (Sweden)

    Vernon Lawhern

    Full Text Available Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG data as an additional illustration.

  14. EEG Controlled Wheelchair

    Directory of Open Access Journals (Sweden)

    Swee Sim Kok

    2016-01-01

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

  15. Method for signal conditioning and data acquisition system, based on variable amplification and feedback technique

    Energy Technology Data Exchange (ETDEWEB)

    Conti, Livio, E-mail: livio.conti@uninettunouniversity.net [Facoltà di Ingegneria, Università Telematica Internazionale Uninettuno, Corso Vittorio Emanuele II 39, 00186 Rome, Italy INFN Sezione Roma Tor Vergata, Via della Ricerca Scientifica 1, 00133 Rome (Italy); Sgrigna, Vittorio [Dipartimento di Matematica e Fisica, Università Roma Tre, 84 Via della Vasca Navale, I-00146 Rome (Italy); Zilpimiani, David [National Institute of Geophysics, Georgian Academy of Sciences, 1 M. Alexidze St., 009 Tbilisi, Georgia (United States); Assante, Dario [Facoltà di Ingegneria, Università Telematica Internazionale Uninettuno, Corso Vittorio Emanuele II 39, 00186 Rome, Italy INFN Sezione Roma Tor Vergata, Via della Ricerca Scientifica 1, 00133 Rome (Italy)

    2014-08-21

    An original method of signal conditioning and adaptive amplification is proposed for data acquisition systems of analog signals, conceived to obtain a high resolution spectrum of any input signal. The procedure is based on a feedback scheme of the signal amplification with aim at maximizing the dynamic range and resolution of the data acquisition system. The paper describes the signal conditioning, digitization, and data processing procedures applied to an a priori unknown signal in order to enucleate its amplitude and frequency content for applications in different environments: on the ground, in space, or in the laboratory. An electronic board of the conditioning module has also been constructed and described. In the paper are also discussed the main fields of application and advantages of the method with respect to those known today.

  16. Method for signal conditioning and data acquisition system, based on variable amplification and feedback technique

    International Nuclear Information System (INIS)

    Conti, Livio; Sgrigna, Vittorio; Zilpimiani, David; Assante, Dario

    2014-01-01

    An original method of signal conditioning and adaptive amplification is proposed for data acquisition systems of analog signals, conceived to obtain a high resolution spectrum of any input signal. The procedure is based on a feedback scheme of the signal amplification with aim at maximizing the dynamic range and resolution of the data acquisition system. The paper describes the signal conditioning, digitization, and data processing procedures applied to an a priori unknown signal in order to enucleate its amplitude and frequency content for applications in different environments: on the ground, in space, or in the laboratory. An electronic board of the conditioning module has also been constructed and described. In the paper are also discussed the main fields of application and advantages of the method with respect to those known today

  17. Optical signal acquisition and processing in future accelerator diagnostics

    International Nuclear Information System (INIS)

    Jackson, G.P.; Elliott, A.

    1992-01-01

    Beam detectors such as striplines and wall current monitors rely on matched electrical networks to transmit and process beam information. Frequency bandwidth, noise immunity, reflections, and signal to noise ratio are considerations that require compromises limiting the quality of the measurement. Recent advances in fiber optics related technologies have made it possible to acquire and process beam signals in the optical domain. This paper describes recent developments in the application of these technologies to accelerator beam diagnostics. The design and construction of an optical notch filter used for a stochastic cooling system is used as an example. Conceptual ideas for future beam detectors are also presented

  18. Adaptive electric potential sensors for smart signal acquisition and processing

    International Nuclear Information System (INIS)

    Prance, R J; Beardsmore-Rust, S; Prance, H; Harland, C J; Stiffell, P B

    2007-01-01

    Current applications of the Electric Potential Sensor operate in a strongly (capacitively) coupled limit, with the sensor physically close to or touching the source. This mode of operation screens the sensor effectively from the majority of external noise. To date however the full capability of these sensors operating in a remote mode has not been realised outside of a screened environment (Faraday cage). This paper describes the results of preliminary work in tailoring the response of the sensors to particular signals and so reject background noise, thereby enhancing both the dynamic range and signal to noise ratio significantly

  19. Adaptive electric potential sensors for smart signal acquisition and processing

    Science.gov (United States)

    Prance, R. J.; Beardsmore-Rust, S.; Prance, H.; Harland, C. J.; Stiffell, P. B.

    2007-07-01

    Current applications of the Electric Potential Sensor operate in a strongly (capacitively) coupled limit, with the sensor physically close to or touching the source. This mode of operation screens the sensor effectively from the majority of external noise. To date however the full capability of these sensors operating in a remote mode has not been realised outside of a screened environment (Faraday cage). This paper describes the results of preliminary work in tailoring the response of the sensors to particular signals and so reject background noise, thereby enhancing both the dynamic range and signal to noise ratio significantly.

  20. Design of General-purpose Industrial signal acquisition system in a large scientific device

    Science.gov (United States)

    Ren, Bin; Yang, Lei

    2018-02-01

    In order to measure the industrial signal of a large scientific device experiment, a set of industrial data general-purpose acquisition system has been designed. It can collect 4~20mA current signal and 0~10V voltage signal. Through the practical experiments, it shows that the system is flexible, reliable, convenient and economical, and the system has characters of high definition and strong anti-interference ability. Thus, the system fully meets the design requirements..

  1. JACoW Lightweight acquisition system for analogue signals

    CERN Document Server

    Bielawski, Bartosz

    2018-01-01

    In a complex machine such as a particle accelerator there are thousands of analogue signals that need monitoring and even more signals that could be used for debugging or as a tool for detecting symptoms of potentially avoidable problems. Usually it is not feasible to acquire and monitor all of these signals not only because of the cost but also because of cabling and space required. The Radio Frequency system in the Large Hadron Collider (LHC) is protected by multiple hardware interlocks that ensure safe operation of klystrons, superconducting cavities and all the other equipment. In parallel, a diagnostic system has been deployed to monitor the health of the klystrons. Due to the limited amount of space and the moderate number of signals to be monitored, a standard approach with a full VME or Compact PCI crate has not been selected. Instead, small embedded industrial computers with Universal Serial Bus (USB) oscilloscopes chosen for the specific application have been installed. This cost effective, rapidly ...

  2. An method of verify period signal based on data acquisition card

    International Nuclear Information System (INIS)

    Zeng Shaoli

    2005-01-01

    This paper introduces an method to verify index voltage of Period Signal Generator by using data acquisition card. which it's error is less 0.5%. A corresponding Win32's program, which use voluntarily developed VxD to control data acquisition card direct I/O and multi thread technique for gain the best time scale precision, has developed in Windows platform. The program will real time collect inda voltage data and auto measure period. (authors)

  3. Pseudo Asynchronous Level Crossing adc for ecg Signal Acquisition.

    Science.gov (United States)

    Marisa, T; Niederhauser, T; Haeberlin, A; Wildhaber, R A; Vogel, R; Goette, J; Jacomet, M

    2017-02-07

    A new pseudo asynchronous level crossing analogue-to-digital converter (adc) architecture targeted for low-power, implantable, long-term biomedical sensing applications is presented. In contrast to most of the existing asynchronous level crossing adc designs, the proposed design has no digital-to-analogue converter (dac) and no continuous time comparators. Instead, the proposed architecture uses an analogue memory cell and dynamic comparators. The architecture retains the signal activity dependent sampling operation by generating events only when the input signal is changing. The architecture offers the advantages of smaller chip area, energy saving and fewer analogue system components. Beside lower energy consumption the use of dynamic comparators results in a more robust performance in noise conditions. Moreover, dynamic comparators make interfacing the asynchronous level crossing system to synchronous processing blocks simpler. The proposed adc was implemented in [Formula: see text] complementary metal-oxide-semiconductor (cmos) technology, the hardware occupies a chip area of 0.0372 mm 2 and operates from a supply voltage of [Formula: see text] to [Formula: see text]. The adc's power consumption is as low as 0.6 μW with signal bandwidth from [Formula: see text] to [Formula: see text] and achieves an equivalent number of bits (enob) of up to 8 bits.

  4. sEMG Signal Acquisition Strategy towards Hand FES Control

    Directory of Open Access Journals (Sweden)

    Cinthya Lourdes Toledo-Peral

    2018-01-01

    Full Text Available Due to damage of the nervous system, patients experience impediments in their daily life: severe fatigue, tremor or impaired hand dexterity, hemiparesis, or hemiplegia. Surface electromyography (sEMG signal analysis is used to identify motion; however, standardization of electrode placement and classification of sEMG patterns are major challenges. This paper describes a technique used to acquire sEMG signals for five hand motion patterns from six able-bodied subjects using an array of recording and stimulation electrodes placed on the forearm and its effects over functional electrical stimulation (FES and volitional sEMG combinations, in order to eventually control a sEMG-driven FES neuroprosthesis for upper limb rehabilitation. A two-part protocol was performed. First, personalized templates to place eight sEMG bipolar channels were designed; with these data, a universal template, called forearm electrode set (FELT, was built. Second, volitional and evoked movements were recorded during FES application. 95% classification accuracy was achieved using two sessions per movement. With the FELT, it was possible to perform FES and sEMG recordings simultaneously. Also, it was possible to extract the volitional and evoked sEMG from the raw signal, which is highly important for closed-loop FES control.

  5. Instantaneous 3D EEG Signal Analysis Based on Empirical Mode Decomposition and the Hilbert–Huang Transform Applied to Depth of Anaesthesia

    Directory of Open Access Journals (Sweden)

    Mu-Tzu Shih

    2015-02-01

    Full Text Available Depth of anaesthesia (DoA is an important measure for assessing the degree to which the central nervous system of a patient is depressed by a general anaesthetic agent, depending on the potency and concentration with which anaesthesia is administered during surgery. We can monitor the DoA by observing the patient’s electroencephalography (EEG signals during the surgical procedure. Typically high frequency EEG signals indicates the patient is conscious, while low frequency signals mean the patient is in a general anaesthetic state. If the anaesthetist is able to observe the instantaneous frequency changes of the patient’s EEG signals during surgery this can help to better regulate and monitor DoA, reducing surgical and post-operative risks. This paper describes an approach towards the development of a 3D real-time visualization application which can show the instantaneous frequency and instantaneous amplitude of EEG simultaneously by using empirical mode decomposition (EMD and the Hilbert–Huang transform (HHT. HHT uses the EMD method to decompose a signal into so-called intrinsic mode functions (IMFs. The Hilbert spectral analysis method is then used to obtain instantaneous frequency data. The HHT provides a new method of analyzing non-stationary and nonlinear time series data. We investigate this approach by analyzing EEG data collected from patients undergoing surgical procedures. The results show that the EEG differences between three distinct surgical stages computed by using sample entropy (SampEn are consistent with the expected differences between these stages based on the bispectral index (BIS, which has been shown to be quantifiable measure of the effect of anaesthetics on the central nervous system. Also, the proposed filtering approach is more effective compared to the standard filtering method in filtering out signal noise resulting in more consistent results than those provided by the BIS. The proposed approach is therefore

  6. New Directions in EEG Measurement: an Investigation into the Fidelity of Electrical Potential Sensor Signals

    Directory of Open Access Journals (Sweden)

    M. FATOORECHI

    2015-01-01

    Full Text Available Low frequency noise performance is the key indicator in determining the signal to noise ratio of a capacitively coupled sensor when used to acquire electroencephalogram signals. For this reason, a prototype Electric Potential Sensor device based on an auto-zero operational amplifier has been developed and evaluated. The absence of 1/f noise in these devices makes them ideal for use with signal frequencies ~10 Hz or less. The active electrodes are designed to be physically and electrically robust and chemically and biochemically inert. They are electrically insulated (anodized and have diameters of 12 mm or 18 mm. In both cases, the sensors are housed in inert stainless steel machined housings with the electronics fabricated in surface mount components on a printed circuit board compatible with epoxy potting compounds. Potted sensors are designed to be immersed in alcohol for sterilization purposes. A comparative study was conducted with a commercial wet gel electrode system. These studies comprised measurements of both free running electroencephalogram and Event Related Potentials. Quality of the recorded electroencephalogram was assessed using three methods of inspection of raw signal, comparing signal to noise ratios, and Event Related Potentials noise analysis. A strictly comparable signal to noise ratio was observed and the overall conclusion from these comparative studies is that the noise performance of the new sensor is appropriate.

  7. Analysis of Filter-Bank-Based Methods for Fast Serial Acquisition of BOC-Modulated Signals

    Directory of Open Access Journals (Sweden)

    Elena Simona Lohan

    2007-09-01

    Full Text Available Binary-offset-carrier (BOC signals, selected for Galileo and modernized GPS systems, pose significant challenges for the code acquisition, due to the ambiguities (deep fades which are present in the envelope of the correlation function (CF. This is different from the BPSK-modulated CDMA signals, where the main correlation lobe spans over 2-chip interval, without any ambiguities or deep fades. To deal with the ambiguities due to BOC modulation, one solution is to use lower steps of scanning the code phases (i.e., lower than the traditional step of 0.5 chips used for BPSK-modulated CDMA signals. Lowering the time-bin steps entails an increase in the number of timing hypotheses, and, thus, in the acquisition times. An alternative solution is to transform the ambiguous CF into an “unambiguous” CF, via adequate filtering of the signal. A generalized class of frequency-based unambiguous acquisition methods is proposed here, namely the filter-bank-based (FBB approaches. The detailed theoretical analysis of FBB methods is given for serial-search single-dwell acquisition in single path static channels and a comparison is made with other ambiguous and unambiguous BOC acquisition methods existing in the literature.

  8. A low noise photoelectric signal acquisition system applying in nuclear magnetic resonance gyroscope

    Science.gov (United States)

    Lu, Qilin; Zhang, Xian; Zhao, Xinghua; Yang, Dan; Zhou, Binquan; Hu, Zhaohui

    2017-10-01

    The nuclear magnetic resonance gyroscope serves as a new generation of strong support for the development of high-tech weapons, it solves the core problem that limits the development of the long-playing seamless navigation and positioning. In the NMR gyroscope, the output signal with atomic precession frequency is detected by the probe light, the final crucial photoelectric signal of the probe light directly decides the quality of the gyro signal. But the output signal has high sensitivity, resolution and measurement accuracy for the photoelectric detection system. In order to detect the measured signal better, this paper proposed a weak photoelectric signal rapid acquisition system, which has high SNR and the frequency of responded signal is up to 100 KHz to let the weak output signal with high frequency of the NMR gyroscope can be detected better.

  9. A Novel Method of Early Diagnosis of Alzheimer’s Disease Based on EEG Signals

    Directory of Open Access Journals (Sweden)

    Dhiya Al-Jumeily

    2015-01-01

    Full Text Available Studies have reported that electroencephalogram signals in Alzheimer’s disease patients usually have less synchronization than those of healthy subjects. Changes in electroencephalogram signals start at early stage but, clinically, these changes are not easily detected. To detect this perturbation, three neural synchrony measurement techniques: phase synchrony, magnitude squared coherence, and cross correlation are applied to three different databases of mild Alzheimer’s disease patients and healthy subjects. We have compared the right and left temporal lobes of the brain with the rest of the brain areas (frontal, central, and occipital as temporal regions are relatively the first ones to be affected by Alzheimer’s disease. Moreover, electroencephalogram signals are further classified into five different frequency bands (delta, theta, alpha beta, and gamma because each frequency band has its own physiological significance in terms of signal evaluation. A new approach using principal component analysis before applying neural synchrony measurement techniques has been presented and compared with Average technique. The simulation results indicated that applying principal component analysis before synchrony measurement techniques shows significantly better results as compared to the lateral one. At the end, all the aforementioned techniques are assessed by a statistical test (Mann-Whitney U test to compare the results.

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

    Directory of Open Access Journals (Sweden)

    Morteza Behnam

    2015-08-01

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

  11. Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing

    Directory of Open Access Journals (Sweden)

    Denis Delisle-Rodriguez

    2017-11-01

    Full Text Available This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs based on Canonical Correlation Analysis (CCA to recognize 40 targets of steady-state visual evoked potential (SSVEP, providing an accuracy (ACC of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly ( p < 0.01 improved for most of the subjects ( A C C ≥ 74.79 % , when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry.

  12. Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients.

    Science.gov (United States)

    Shu, Xiaokang; Chen, Shugeng; Yao, Lin; Sheng, Xinjun; Zhang, Dingguo; Jiang, Ning; Jia, Jie; Zhu, Xiangyang

    2018-01-01

    Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10-50%) of subjects are BCI-inefficient users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI) and cortical activation strength (CAS), to predict MI-BCI performance. Twenty-four stroke patients and 10 healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG) signals during paretic hand MI tasks (5 trials; approximately 1 min). LI values exhibited a statistically significant correlation with two-class BCI (left vs. right) performance (r = -0.732, p discrimination of BCI-inefficient users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-inefficiency phenomenon in stroke patients.

  13. Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients

    Science.gov (United States)

    Shu, Xiaokang; Chen, Shugeng; Yao, Lin; Sheng, Xinjun; Zhang, Dingguo; Jiang, Ning; Jia, Jie; Zhu, Xiangyang

    2018-01-01

    Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10–50%) of subjects are BCI-inefficient users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI) and cortical activation strength (CAS), to predict MI-BCI performance. Twenty-four stroke patients and 10 healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG) signals during paretic hand MI tasks (5 trials; approximately 1 min). LI values exhibited a statistically significant correlation with two-class BCI (left vs. right) performance (r = −0.732, p discrimination of BCI-inefficient users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-inefficiency phenomenon in stroke patients. PMID:29515363

  14. Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

    Science.gov (United States)

    Bascil, M Serdar; Tesneli, Ahmet Y; Temurtas, Feyzullah

    2016-09-01

    Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.

  15. The Design of Wireless Data Acquisition and Remote Transmission Interface in Micro-seismic Signals

    Directory of Open Access Journals (Sweden)

    Huan-Huan BIAN

    2014-02-01

    Full Text Available The micro-seismic signal acquisition and transmission is an important key part in geological prospecting. This paper describes a bran-new solution of micro-seismic signal acquisition and remote transmission using Zigbee technique and wireless data transmission technique. The hardware such as front-end data acquisition interface made up by Zigbee wireless networking technique, remote data transmission solution composed of general packet radio service (or GPRS for short technique and interface between Zigbee and GPRS is designed in detail. Meanwhile the corresponding software of the system is given out. The solution solves the numerous practical problems nagged by complex and terrible environment faced using micro-seismic prospecting. The experimental results demonstrate that the method using Zigbee wireless network communication technique GPRS wireless packet switching technique is efficient, reliable and flexible.

  16. Adaptive autoregressive identification with spectral power decomposition for studying movement-related activity in scalp EEG signals and basal ganglia local field potentials

    Science.gov (United States)

    Foffani, Guglielmo; Bianchi, Anna M.; Priori, Alberto; Baselli, Giuseppe

    2004-09-01

    We propose a method that combines adaptive autoregressive (AAR) identification and spectral power decomposition for the study of movement-related spectral changes in scalp EEG signals and basal ganglia local field potentials (LFPs). This approach introduces the concept of movement-related poles, allowing one to study not only the classical event-related desynchronizations (ERD) and synchronizations (ERS), which correspond to modulations of power, but also event-related modulations of frequency. We applied the method to analyze movement-related EEG signals and LFPs contemporarily recorded from the sensorimotor cortex, the globus pallidus internus (GPi) and the subthalamic nucleus (STN) in a patient with Parkinson's disease who underwent stereotactic neurosurgery for the implant of deep brain stimulation (DBS) electrodes. In the AAR identification we compared the whale and the exponential forgetting factors, showing that the whale forgetting provides a better disturbance rejection and it is therefore more suitable to investigate movement-related brain activity. Movement-related power modulations were consistent with previous studies. In addition, movement-related frequency modulations were observed from both scalp EEG signals and basal ganglia LFPs. The method therefore represents an effective approach to the study of movement-related brain activity.

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

    Directory of Open Access Journals (Sweden)

    Ali MOMENNEZHAD

    2014-06-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  19. Target acquisition performance : Effects of target aspect angle, dynamic imaging and signal processing

    NARCIS (Netherlands)

    Beintema, J.A.; Bijl, P.; Hogervorst, M.A.; Dijk, J.

    2008-01-01

    In an extensive Target Acquisition (TA) performance study, we recorded static and dynamic imagery of a set of military and civilian two-handheld objects at a range of distances and aspect angles with an under-sampled uncooled thermal imager. Next, we applied signal processing techniques including

  20. Joint time-frequency analysis of EEG signals based on a phase-space interpretation of the recording process

    Science.gov (United States)

    Testorf, M. E.; Jobst, B. C.; Kleen, J. K.; Titiz, A.; Guillory, S.; Scott, R.; Bujarski, K. A.; Roberts, D. W.; Holmes, G. L.; Lenck-Santini, P.-P.

    2012-10-01

    Time-frequency transforms are used to identify events in clinical EEG data. Data are recorded as part of a study for correlating the performance of human subjects during a memory task with pathological events in the EEG, called spikes. The spectrogram and the scalogram are reviewed as tools for evaluating spike activity. A statistical evaluation of the continuous wavelet transform across trials is used to quantify phase-locking events. For simultaneously improving the time and frequency resolution, and for representing the EEG of several channels or trials in a single time-frequency plane, a multichannel matching pursuit algorithm is used. Fundamental properties of the algorithm are discussed as well as preliminary results, which were obtained with clinical EEG data.

  1. Classification of EEG signals to identify variations in attention during motor task execution.

    Science.gov (United States)

    Aliakbaryhosseinabadi, Susan; Kamavuako, Ernest Nlandu; Jiang, Ning; Farina, Dario; Mrachacz-Kersting, Natalie

    2017-06-01

    Brain-computer interface (BCI) systems in neuro-rehabilitation use brain signals to control external devices. User status such as attention affects BCI performance; thus detecting the user's attention drift due to internal or external factors is essential for high detection accuracy. An auditory oddball task was applied to divert the users' attention during a simple ankle dorsiflexion movement. Electroencephalogram signals were recorded from eighteen channels. Temporal and time-frequency features were projected to a lower dimension space and used to analyze the effect of two attention levels on motor tasks in each participant. Then, a global feature distribution was constructed with the projected time-frequency features of all participants from all channels and applied for attention classification during motor movement execution. Time-frequency features led to significantly better classification results with respect to the temporal features, particularly for electrodes located over the motor cortex. Motor cortex channels had a higher accuracy in comparison to other channels in the global discrimination of attention level. Previous methods have used the attention to a task to drive external devices, such as the P300 speller. However, here we focus for the first time on the effect of attention drift while performing a motor task. It is possible to explore user's attention variation when performing motor tasks in synchronous BCI systems with time-frequency features. This is the first step towards an adaptive real-time BCI with an integrated function to reveal attention shifts from the motor task. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Patient-Specific Seizure Detection in Long-Term EEG Using Signal-Derived Empirical Mode Decomposition (EMD)-based Dictionary Approach.

    Science.gov (United States)

    Kaleem, Muhammad; Gurve, Dharmendra; Guergachi, Aziz; Krishnan, Sridhar

    2018-06-25

    The objective of the work described in this paper is development of a computationally efficient methodology for patient-specific automatic seizure detection in long-term multi-channel EEG recordings. Approach: A novel patient-specific seizure detection approach based on signal-derived Empirical Mode Decomposition (EMD)-based dictionary approach is proposed. For this purpose, we use an empirical framework for EMD-based dictionary creation and learning, inspired by traditional dictionary learning methods, in which the EMD-based dictionary is learned from the multi-channel EEG data being analyzed for automatic seizure detection. We present the algorithm for dictionary creation and learning, whose purpose is to learn dictionaries with a small number of atoms. Using training signals belonging to seizure and non-seizure classes, an initial dictionary, termed as the raw dictionary, is formed. The atoms of the raw dictionary are composed of intrinsic mode functions obtained after decomposition of the training signals using the empirical mode decomposition algorithm. The raw dictionary is then trained using a learning algorithm, resulting in a substantial decrease in the number of atoms in the trained dictionary. The trained dictionary is then used for automatic seizure detection, such that coefficients of orthogonal projections of test signals against the trained dictionary form the features used for classification of test signals into seizure and non-seizure classes. Thus no hand-engineered features have to be extracted from the data as in traditional seizure detection approaches. Main results: The performance of the proposed approach is validated using the CHB-MIT benchmark database, and averaged accuracy, sensitivity and specificity values of 92.9%, 94.3% and 91.5%, respectively, are obtained using support vector machine classifier and five-fold cross-validation method. These results are compared with other approaches using the same database, and the suitability

  3. PyEEG: an open source Python module for EEG/MEG feature extraction.

    Science.gov (United States)

    Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

    2011-01-01

    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.

  4. Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients

    Directory of Open Access Journals (Sweden)

    Xiaokang Shu

    2018-02-01

    Full Text Available Motor imagery (MI based brain-computer interface (BCI has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10–50% of subjects are BCI-inefficient users (accuracy less than 70%. Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI and cortical activation strength (CAS, to predict MI-BCI performance. Twenty-four stroke patients and 10 healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG signals during paretic hand MI tasks (5 trials; approximately 1 min. LI values exhibited a statistically significant correlation with two-class BCI (left vs. right performance (r = −0.732, p < 0.001, and CAS values exhibited a statistically significant correlation with brain-switch BCI (task vs. idle performance (r = 0.641, p < 0.001. Furthermore, the BCI-inefficient users were successfully recognized with a sensitivity of 88.2% and a specificity of 85.7% in the two-class BCI. The brain-switch BCI achieved a sensitivity of 100.0% and a specificity of 87.5% in the discrimination of BCI-inefficient users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-inefficiency phenomenon in stroke patients.

  5. Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis

    Science.gov (United States)

    Javed, Amna; Tiwana, Mohsin I.; Khan, Umar Shahbaz

    2018-01-01

    Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8–30 Hz) containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%. PMID:29888252

  6. Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis

    Directory of Open Access Journals (Sweden)

    Nasir Rashid

    2018-01-01

    Full Text Available Brain Computer Interface (BCI determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement. Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves and Beta Rhythms (8–30 Hz containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.

  7. Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis.

    Science.gov (United States)

    Rashid, Nasir; Iqbal, Javaid; Javed, Amna; Tiwana, Mohsin I; Khan, Umar Shahbaz

    2018-01-01

    Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8-30 Hz) containing most of the movement data were retained through filtering using "Arduino Uno" microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.

  8. Open Ephys electroencephalography (Open Ephys  +  EEG): a modular, low-cost, open-source solution to human neural recording.

    Science.gov (United States)

    Black, Christopher; Voigts, Jakob; Agrawal, Uday; Ladow, Max; Santoyo, Juan; Moore, Christopher; Jones, Stephanie

    2017-06-01

    Electroencephalography (EEG) offers a unique opportunity to study human neural activity non-invasively with millisecond resolution using minimal equipment in or outside of a lab setting. EEG can be combined with a number of techniques for closed-loop experiments, where external devices are driven by specific neural signals. However, reliable, commercially available EEG systems are expensive, often making them impractical for individual use and research development. Moreover, by design, a majority of these systems cannot be easily altered to the specification needed by the end user. We focused on mitigating these issues by implementing open-source tools to develop a new EEG platform to drive down research costs and promote collaboration and innovation. Here, we present methods to expand the open-source electrophysiology system, Open Ephys (www.openephys.org), to include human EEG recordings. We describe the equipment and protocol necessary to interface various EEG caps with the Open Ephys acquisition board, and detail methods for processing data. We present applications of Open Ephys  +  EEG as a research tool and discuss how this innovative EEG technology lays a framework for improved closed-loop paradigms and novel brain-computer interface experiments. The Open Ephys  +  EEG system can record reliable human EEG data, as well as human EMG data. A side-by-side comparison of eyes closed 8-14 Hz activity between the Open Ephys  +  EEG system and the Brainvision ActiCHamp EEG system showed similar average power and signal to noise. Open Ephys  +  EEG enables users to acquire high-quality human EEG data comparable to that of commercially available systems, while maintaining the price point and extensibility inherent to open-source systems.

  9. Open Ephys electroencephalography (Open Ephys  +  EEG): a modular, low-cost, open-source solution to human neural recording

    Science.gov (United States)

    Black, Christopher; Voigts, Jakob; Agrawal, Uday; Ladow, Max; Santoyo, Juan; Moore, Christopher; Jones, Stephanie

    2017-06-01

    Objective. Electroencephalography (EEG) offers a unique opportunity to study human neural activity non-invasively with millisecond resolution using minimal equipment in or outside of a lab setting. EEG can be combined with a number of techniques for closed-loop experiments, where external devices are driven by specific neural signals. However, reliable, commercially available EEG systems are expensive, often making them impractical for individual use and research development. Moreover, by design, a majority of these systems cannot be easily altered to the specification needed by the end user. We focused on mitigating these issues by implementing open-source tools to develop a new EEG platform to drive down research costs and promote collaboration and innovation. Approach. Here, we present methods to expand the open-source electrophysiology system, Open Ephys (www.openephys.org), to include human EEG recordings. We describe the equipment and protocol necessary to interface various EEG caps with the Open Ephys acquisition board, and detail methods for processing data. We present applications of Open Ephys  +  EEG as a research tool and discuss how this innovative EEG technology lays a framework for improved closed-loop paradigms and novel brain-computer interface experiments. Main results. The Open Ephys  +  EEG system can record reliable human EEG data, as well as human EMG data. A side-by-side comparison of eyes closed 8-14 Hz activity between the Open Ephys  +  EEG system and the Brainvision ActiCHamp EEG system showed similar average power and signal to noise. Significance. Open Ephys  +  EEG enables users to acquire high-quality human EEG data comparable to that of commercially available systems, while maintaining the price point and extensibility inherent to open-source systems.

  10. Automatic calibration and signal switching system for the particle beam fusion research data acquisition facility

    Energy Technology Data Exchange (ETDEWEB)

    Boyer, W.B.

    1979-09-01

    This report describes both the hardware and software components of an automatic calibration and signal system (Autocal) for the data acquisition system for the Sandia particle beam fusion research accelerators Hydra, Proto I, and Proto II. The Autocal hardware consists of off-the-shelf commercial equipment. The various hardware components, special modifications and overall system configuration are described. Special software has been developed to support the Autocal hardware. Software operation and maintenance are described.

  11. IN-SITU TEST OF PRESSURE PIPELINE VIBRATION BASED ON DATA ACQUISITION AND SIGNAL PROCESSING

    OpenAIRE

    Hou, Huimin; Xu, Cundong; Liu, Hui; Wang, Rongrong; Jie, Junkun; Ding, Lianying

    2015-01-01

    Pipeline vibration of high frequency and large amplitude is an important factor that impacts the safe operation of pumping station and the efficiency of the pumps. Through conducting the vibration in-situ test of pipeline system in the pumping station, we can objectively analyze the mechanism of pipeline vibration and evaluate the stability of pipeline operation. By using DASP (data acquisition & signal processing) in the in-situ test on the 2# pipeline of the third pumping station in the gen...

  12. Automatic calibration and signal switching system for the particle beam fusion research data acquisition facility

    International Nuclear Information System (INIS)

    Boyer, W.B.

    1979-09-01

    This report describes both the hardware and software components of an automatic calibration and signal system (Autocal) for the data acquisition system for the Sandia particle beam fusion research accelerators Hydra, Proto I, and Proto II. The Autocal hardware consists of off-the-shelf commercial equipment. The various hardware components, special modifications and overall system configuration are described. Special software has been developed to support the Autocal hardware. Software operation and maintenance are described

  13. Viability of a Bioelectrical Signal Acquisition System Energized by Cellphone with NFC.

    Science.gov (United States)

    Kay, Marcel Seiji; Iaione, Fábio

    2015-01-01

    Currently, smartphones are used in various systems in the medical field due to the presence of various features, notably Near Field Communication (NFC). NFC utilizes communication technology and an energy supply based on electromagnetic induction. One of the most common medical tests is the electrocardiogram (ECG), through which various heart diseases can be diagnosed. The objective of this study is to evaluate the feasibility of providing power to a bioelectrical signal acquisition module using a mobile phone with NFC. After testing it was indicated that it is possible to construct a passive module to acquire ECG signals using NFC mobile phone.

  14. PN Sequence Preestimator Scheme for DS-SS Signal Acquisition Using Block Sequence Estimation

    Directory of Open Access Journals (Sweden)

    Sang Kyu Park

    2005-03-01

    Full Text Available An m-sequence (PN sequence preestimator scheme for direct-sequence spread spectrum (DS-SS signal acquisition by using block sequence estimation (BSE is proposed and analyzed. The proposed scheme consists of an estimator and a verifier which work according to the PN sequence chip clock, and provides not only the enhanced chip estimates with a threshold decision logic and one-chip error correction among the first m received chips, but also the reliability check of the estimates with additional decision logic. The probabilities of the estimator and verifier operations are calculated. With these results, the detection, the false alarm, and the missing probabilities of the proposed scheme are derived. In addition, using a signal flow graph, the average acquisition time is calculated. The proposed scheme can be used as a preestimator and easily implemented by changing the internal signal path of a generally used digital matched filter (DMF correlator or any other correlator that has a lot of sampling data memories for sampled PN sequence. The numerical results show rapid acquisition performance in a relatively good CNR.

  15. Evaluation of high performance data acquisition boards for simultaneous sampling of fast signals from PET detectors

    International Nuclear Information System (INIS)

    Judenhofer, Martin S; Pichler, Bernd J; Cherry, Simon R

    2005-01-01

    Detectors used for positron emission tomography (PET) provide fast, randomly distributed signals that need to be digitized for further processing. One possibility is to sample the signals at the peak initiated by a trigger from a constant fraction discriminator (CFD). For PET detectors, simultaneous acquisition of many channels is often important. To develop and evaluate novel PET detectors, a flexible, relatively low cost and high performance laboratory data acquisition (DAQ) system is therefore required. The use of dedicated DAQ systems, such as a multi-channel analysers (MCAs) or continuous sampling boards at high rates, is expensive. This work evaluates the suitability of well-priced peripheral component interconnect (PCI)-based 8-channel DAQ boards (PD2-MFS-8 2M/14 and PD2-MFS-8-500k/14, United Electronic Industries Inc., Canton, MA, USA) for signal acquisition from novel PET detectors. A software package was developed to access the board, measure basic board parameters, and to acquire, visualize, and analyse energy spectra and position profiles from block detectors. The performance tests showed that the boards input linearity is >99.2% and the standard deviation is 22 Na source was 14.9% (FWHM) at 511 keV and is slightly better than the result obtained with a high-end single channel MCA (8000A, Amptek, USA) using the same detector (16.8%). The crystals (1.2 x 1.2 x 12 mm 3 ) within a 9 x 9 LSO block detector could be clearly separated in an acquired position profile. Thus, these boards are well suited for data acquisition with novel detectors developed for nuclear imaging

  16. Signal displacement in spiral-in acquisitions: simulations and implications for imaging in SFG regions.

    Science.gov (United States)

    Brewer, Kimberly D; Rioux, James A; Klassen, Martyn; Bowen, Chris V; Beyea, Steven D

    2012-07-01

    Susceptibility field gradients (SFGs) cause problems for functional magnetic resonance imaging (fMRI) in regions like the orbital frontal lobes, leading to signal loss and image artifacts (signal displacement and "pile-up"). Pulse sequences with spiral-in k-space trajectories are often used when acquiring fMRI in SFG regions such as inferior/medial temporal cortex because it is believed that they have improved signal recovery and decreased signal displacement properties. Previously postulated theories explain differing reasons why spiral-in appears to perform better than spiral-out; however it is clear that multiple mechanisms are occurring in parallel. This study explores differences in spiral-in and spiral-out images using human and phantom empirical data, as well as simulations consistent with the phantom model. Using image simulations, the displacement of signal was characterized using point spread functions (PSFs) and target maps, the latter of which are conceptually inverse PSFs describing which spatial locations contribute signal to a particular voxel. The magnitude of both PSFs and target maps was found to be identical for spiral-out and spiral-in acquisitions, with signal in target maps being displaced from distant regions in both cases. However, differences in the phase of the signal displacement patterns that consequently lead to changes in the intervoxel phase coherence were found to be a significant mechanism explaining differences between the spiral sequences. The results demonstrate that spiral-in trajectories do preserve more total signal in SFG regions than spiral-out; however, spiral-in does not in fact exhibit decreased signal displacement. Given that this signal can be displaced by significant distances, its recovery may not be preferable for all fMRI applications. Copyright © 2012 Elsevier Inc. All rights reserved.

  17. Portable Amplifier Design for a Novel EEG Monitor in Point-of-Care Applications.

    Science.gov (United States)

    Luan, Bo; Sun, Mingui; Jia, Wenyan

    2012-01-01

    The Electroencephalography (EEG) is a common diagnostic tool for neurological diseases and dysfunctions, such as epilepsy and insomnia. However, the current EEG technology cannot be utilized quickly and conveniently at the point of care due to the complex skin preparation procedures required and the inconvenient EEG data acquisition systems. This work presents a portable amplifier design that integrates a set of skin screw electrodes and a wireless data link. The battery-operated amplifier contains an instrumentation amplifier, two noninverting amplifiers, two high-pass filters, and a low-pass filter. It is able to magnify the EEG signals over 10,000 times and has a high impedance, low noise, small size and low weight. Our electrode and amplifier are ideal for point-of-care applications, especially during transportation of patients suffering from traumatic brain injury or stroke.

  18. Design and implementation of a data acquisition system for the study of dynamics signals in the time Domain

    International Nuclear Information System (INIS)

    Daza, Arnoldo; Hinojosa, Ana; Diaz, Xavier; Neira, Oscar

    2011-01-01

    The purpose of this research was to design and implement a data acquisition system for the study of dynamic signals in the time in order of nanoseconds, through the use of electronic instrumentation to obtain the temporal profiles of light signals, such as pulses of light from a laser or fluorescent light signals. To carry out the design and implementation of the data acquisition system proposed, we used a rapid response transducer and a data acquisition card of high speed. As well, it was developed a graphical user interface for visualization, analysis and study of the data acquired. Thus, it was possible to perform the digitization of a pulse on the order of nanoseconds, as well as the acquisition of fluorescence signals of different wavelengths, from which to obtain theoretical and experimental valuable knowledge based on the physics of spectroscopy.

  19. Signal processing and general purpose data acquisition system for on-line tomographic measurements

    Science.gov (United States)

    Murari, A.; Martin, P.; Hemming, O.; Manduchi, G.; Marrelli, L.; Taliercio, C.; Hoffmann, A.

    1997-01-01

    New analog signal conditioning electronics and data acquisition systems have been developed for the soft x-ray and bolometric tomography diagnostic in the reverse field pinch experiment (RFX). For the soft x-ray detectors the analog signal processing includes a fully differential current to voltage conversion, with up to a 200 kHz bandwidth. For the bolometers, a 50 kHz carrier frequency amplifier allows a maximum bandwidth of 10 kHz. In both cases the analog signals are digitized with a 1 MHz sampling rate close to the diagnostic and are transmitted via a transparent asynchronous xmitter/receiver interface (TAXI) link to purpose built Versa Module Europa (VME) modules which perform data acquisition. A software library has been developed for data preprocessing and tomographic reconstruction. It has been written in C language and is self-contained, i.e., no additional mathematical library is required. The package is therefore platform-free: in particular it can perform online analysis in a real-time application, such as continuous display and feedback, and is portable for long duration fusion or other physical experiments. Due to the modular organization of the library, new preprocessing and analysis modules can be easily integrated in the environment. This software is implemented in RFX over three different platforms: open VMS, digital Unix, and VME 68040 CPU.

  20. Relationship of signal-to-noise ratio with acquisition parameters in MRI for a given contrast

    International Nuclear Information System (INIS)

    Bittoun, J.; Leroy-Willig, A.; Idy, I.; Halimi, P.; Syrota, A.; Desgrez, A.; Saint-Jalmes, H.

    1987-01-01

    The signal-to-noise ratio (SNR) is certainly the most important characteristic of medical images, since the spatial resolution and the visualization of contrast are dependent on its value. On the other hand, modifying an acquisition variable in magnetic resonance imaging, in order to improve spatial resolution for example, may induce a SNR loss and finally alter the image quality. We have studied a theoretical relation between SNR and 2DFT method acquisition variables with the exception of parameters such as TR, TE and TI; these parameters are determined by the desired contrast in order to confirm a diagnosis. According to this relation SNR is proportional to each dimension of the slice, and to the square root of the number of averaged signals; it is inversely proportional to the number of frequency points and to the square root of the number of phase points. This relation was experimentally verified with phantoms and on an MR system at 1.5 T. It was then plotted as a multiple-entry graph on which operators at the console can read the number of averaged signals necessary to compensate SNR loss induced by a modification of other parameters [fr

  1. Multicohort Genomewide Association Study Reveals a New Signal of Protection Against HIV-1 Acquisition

    Science.gov (United States)

    Limou, Sophie; Delaneau, Olivier; van Manen, Daniëlle; An, Ping; Sezgin, Efe; Le Clerc, Sigrid; Coulonges, Cédric; Troyer, Jennifer L.; Veldink, Jan H.; van den Berg, Leonard H.; Spadoni, Jean-Louis; Taing, Lieng; Labib, Taoufik; Montes, Matthieu; Delfraissy, Jean-François; Schachter, François; O’Brien, Stephen J.; Buchbinder, Susan; van Natta, Mark L.; Jabs, Douglas A.; Froguel, Philippe; Schuitemaker, Hanneke; Winkler, Cheryl A.

    2012-01-01

    Background. To date, only mutations in CCR5 have been shown to confer resistance to human immunodeficiency virus type 1 (HIV-1) infection, and these explain only a small fraction of the observed variability in HIV susceptibility. Methods. We performed a meta-analysis between 2 independent European genomewide association studies, each comparing HIV-1 seropositive cases with normal population controls known to be HIV uninfected, to identify single-nucleotide polymorphisms (SNPs) associated with the HIV-1 acquisition phenotype. SNPs exhibiting P < 10−5 in this first stage underwent second-stage analysis in 2 independent US cohorts of European descent. Results. After the first stage, a single highly significant association was revealed for the chromosome 8 rs6996198 with HIV-1 acquisition and was replicated in both second-stage cohorts. Across the 4 groups, the rs6996198-T allele was consistently associated with a significant reduced risk of HIV-1 infection, and the global meta-analysis reached genomewide significance: Pcombined = 7.76 × 10−8. Conclusions. We provide strong evidence of association for a common variant with HIV-1 acquisition in populations of European ancestry. This protective signal against HIV-1 infection is the first identified outside the CCR5 nexus. First clues point to a potential functional role for a nearby candidate gene, CYP7B1, but this locus warrants further investigation. PMID:22362864

  2. A harmonic coil measurement system based on a dynamic signal acquisition device

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, J.X., E-mail: zhoujx@ihep.ac.c [Institute of High Energy Physics, P.O. Box 918-9, Beijing 100049 (China); Li, L.; Yin, B.G.; Deng, C.D.; Kang, W.; Chen, Y.; Zhang, Z.; Fu, S.N. [Institute of High Energy Physics, P.O. Box 918-9, Beijing 100049 (China)

    2010-12-21

    A new harmonic coil measurement system based on a dynamic signal acquisition device has been successfully developed to check the field quality of the quadrupole magnet for the CSNS/RCS, which operates at the 25 Hz excitation cycle with a DC bias. It was designed to acquire multiple channels of data with a wide dynamic range of input signals, which are typically generated by a harmonic coil and an encoder. A dedicated algorithm was developed in LabView code to identify over specified intervals, synchronized to the coil's rotation in the magnetic field. Through full integration of hardware and software, the traditional device (PDI 5025) is replaced successfully. This paper summarizes the characteristics of the system and presents the results of DC measurements.

  3. Rapid anatomical brain imaging using spiral acquisition and an expanded signal model.

    Science.gov (United States)

    Kasper, Lars; Engel, Maria; Barmet, Christoph; Haeberlin, Maximilian; Wilm, Bertram J; Dietrich, Benjamin E; Schmid, Thomas; Gross, Simon; Brunner, David O; Stephan, Klaas E; Pruessmann, Klaas P

    2018-03-01

    We report the deployment of spiral acquisition for high-resolution structural imaging at 7T. Long spiral readouts are rendered manageable by an expanded signal model including static off-resonance and B 0 dynamics along with k-space trajectories and coil sensitivity maps. Image reconstruction is accomplished by inversion of the signal model using an extension of the iterative non-Cartesian SENSE algorithm. Spiral readouts up to 25 ms are shown to permit whole-brain 2D imaging at 0.5 mm in-plane resolution in less than a minute. A range of options is explored, including proton-density and T 2 * contrast, acceleration by parallel imaging, different readout orientations, and the extraction of phase images. Results are shown to exhibit competitive image quality along with high geometric consistency. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. ITI-signals and prelimbic cortex facilitate avoidance acquisition and reduce avoidance latencies, respectively, in male WKY rats

    Directory of Open Access Journals (Sweden)

    Kevin D Beck

    2014-11-01

    Full Text Available As a model of anxiety disorder vulnerability, male Wistar-Kyoto (WKY rats acquire lever-press avoidance behavior more readily than outbred Sprague Dawley rats, and their acquisition is enhanced by the presence of a discrete signal presented during the inter-trial intervals (ITIs, suggesting it is perceived as a safety signal. A series of experiments were conducted to determine if this is the case. Additional experiments investigated if the avoidance facilitation relies upon processing through medial prefrontal cortex (mPFC. The results suggest that the ITI-signal facilitates acquisition during the early stages of the avoidance acquisition process, when the rats are initially acquiring escape behavior and then transitioning to avoidance behavior. Post-avoidance introduction of the visual ITI-signal into other associative learning tasks failed to confirm that the visual stimulus had acquired the properties of a conditioned inhibitor. Shortening the signal from the entirety of the 3 min ITI to only the first 5 s of the 3 min ITI slowed acquisition during the first 4 sessions, suggesting the flashing light is not functioning as a feedback signal. The prelimbic (PL cortex showed greater activation during the period of training when the transition from escape responding to avoidance responding occurs. Only combined PL+infralimbic cortex lesions modestly slowed avoidance acquisition, but PL cortex lesions slowed avoidance response latencies. Thus, the flashing light ITI-signal is not likely perceived as a safety signal nor is it serving as a feedback signal. The functional role of the PL cortex appears to be to increase the drive towards responding to the threat of the warning signal. Hence, avoidance susceptibility displayed by male WKY rats may be driven, in part, both by external stimuli (ITI signal as well as by enhanced threat recognition to the warning signal via the PL cortex.

  5. EEG (Electroencephalogram)

    Science.gov (United States)

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

  6. On the optimal sampling of bandpass measurement signals through data acquisition systems

    International Nuclear Information System (INIS)

    Angrisani, L; Vadursi, M

    2008-01-01

    Data acquisition systems (DAS) play a fundamental role in a lot of modern measurement solutions. One of the parameters characterizing a DAS is its maximum sample rate, which imposes constraints on the signals that can be alias-free digitized. Bandpass sampling theory singles out separated ranges of admissible sample rates, which can be significantly lower than carrier frequency. But, how to choose the most convenient sample rate according to the purpose at hand? The paper proposes a method for the automatic selection of the optimal sample rate in measurement applications involving bandpass signals; the effects of sample clock instability and limited resolution are also taken into account. The method allows the user to choose the location of spectral replicas of the sampled signal in terms of normalized frequency, and the minimum guard band between replicas, thus introducing a feature that no DAS currently available on the market seems to offer. A number of experimental tests on bandpass digitally modulated signals are carried out to assess the concurrence of the obtained central frequency with the expected one

  7. Acquisition and deconvolution of seismic signals by different methods to perform direct ground-force measurements

    Science.gov (United States)

    Poletto, Flavio; Schleifer, Andrea; Zgauc, Franco; Meneghini, Fabio; Petronio, Lorenzo

    2016-12-01

    We present the results of a novel borehole-seismic experiment in which we used different types of onshore-transient-impulsive and non-impulsive-surface sources together with direct ground-force recordings. The ground-force signals were obtained by baseplate load cells located beneath the sources, and by buried soil-stress sensors installed in the very shallow-subsurface together with accelerometers. The aim was to characterize the source's emission by its complex impedance, function of the near-field vibrations and soil stress components, and above all to obtain appropriate deconvolution operators to remove the signature of the sources in the far-field seismic signals. The data analysis shows the differences in the reference measurements utilized to deconvolve the source signature. As downgoing waves, we process the signals of vertical seismic profiles (VSP) recorded in the far-field approximation by an array of permanent geophones cemented at shallow-medium depth outside the casing of an instrumented well. We obtain a significant improvement in the waveform of the radiated seismic-vibrator signals deconvolved by ground force, similar to that of the seismograms generated by the impulsive sources, and demonstrates that the results obtained by different sources present low values in their repeatability norm. The comparison evidences the potentiality of the direct ground-force measurement approach to effectively remove the far-field source signature in VSP onshore data, and to increase the performance of permanent acquisition installations for time-lapse application purposes.

  8. Assessing a novel polymer-wick based electrode for EEG neurophysiological research.

    Science.gov (United States)

    Pasion, Rita; Paiva, Tiago O; Pedrosa, Paulo; Gaspar, Hugo; Vasconcelos, Beatriz; Martins, Ana C; Amaral, Maria H; Nóbrega, João M; Páscoa, Ricardo; Fonseca, Carlos; Barbosa, Fernando

    2016-07-15

    The EEG technique has decades of valid applications in clinical and experimental neurophysiology. EEG equipment and data analysis methods have been characterized by remarkable developments, but the skin-to-electrode signal transfer remains a challenge for EEG recording. A novel quasi-dry system - the polymer wick-based electrode - was developed to overcome the limitations of conventional dry and wet silver/silver-chloride (Ag/AgCl) electrodes for EEG recording. Nine participants completed an auditory oddball protocol with simultaneous EEG acquisition using both the conventional Ag/AgCl and the wick electrodes. Wick system successfully recorded the expected P300 modulation. Standard ERP analysis, residual random noise analysis, and single-trial analysis of the P300 wave were performed in order to compare signal acquired by both electrodes. It was found that the novel wick electrode performed similarly to the conventional Ag/AgCl electrodes. The developed wick electrode appears to be a reliable alternative for EEG research, representing a promising halfway alternative between wet and dry electrodes. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

    OpenAIRE

    Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

    2011-01-01

    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 ...

  10. Characterisation and correction of signal fluctuations in successive acquisitions of microarray images

    Directory of Open Access Journals (Sweden)

    François Nicolas

    2009-03-01

    Full Text Available Abstract Background There are many sources of variation in dual labelled microarray experiments, including data acquisition and image processing. The final interpretation of experiments strongly relies on the accuracy of the measurement of the signal intensity. For low intensity spots in particular, accurately estimating gene expression variations remains a challenge as signal measurement is, in this case, highly subject to fluctuations. Results To evaluate the fluctuations in the fluorescence intensities of spots, we used series of successive scans, at the same settings, of whole genome arrays. We measured the decrease in fluorescence and we evaluated the influence of different parameters (PMT gain, resolution and chemistry of the slide on the signal variability, at the level of the array as a whole and by intensity interval. Moreover, we assessed the effect of averaging scans on the fluctuations. We found that the extent of photo-bleaching was low and we established that 1 the fluorescence fluctuation is linked to the resolution e.g. it depends on the number of pixels in the spot 2 the fluorescence fluctuation increases as the scanner voltage increases and, moreover, is higher for the red as opposed to the green fluorescence which can introduce bias in the analysis 3 the signal variability is linked to the intensity level, it is higher for low intensities 4 the heterogeneity of the spots and the variability of the signal and the intensity ratios decrease when two or three scans are averaged. Conclusion Protocols consisting of two scans, one at low and one at high PMT gains, or multiple scans (ten scans can introduce bias or be difficult to implement. We found that averaging two, or at most three, acquisitions of microarrays scanned at moderate photomultiplier settings (PMT gain is sufficient to significantly improve the accuracy (quality of the data and particularly those for spots having low intensities and we propose this as a general

  11. The IceCube data acquisition system: Signal capture, digitization,and timestamping

    Energy Technology Data Exchange (ETDEWEB)

    The IceCube Collaboration; Matis, Howard

    2009-03-02

    IceCube is a km-scale neutrino observatory under construction at the South Pole with sensors both in the deep ice (InIce) and on the surface (IceTop). The sensors, called Digital Optical Modules (DOMs), detect, digitize and timestamp the signals from optical Cherenkov-radiation photons. The DOM Main Board (MB) data acquisition subsystem is connected to the central DAQ in the IceCube Laboratory (ICL) by a single twisted copper wire-pair and transmits packetized data on demand. Time calibration ismaintained throughout the array by regular transmission to the DOMs of precisely timed analog signals, synchronized to a central GPS-disciplined clock. The design goals and consequent features, functional capabilities, and initial performance of the DOM MB, and the operation of a combined array of DOMs as a system, are described here. Experience with the first InIce strings and the IceTop stations indicates that the system design and performance goals have been achieved.

  12. EEG analyses with SOBI.

    Energy Technology Data Exchange (ETDEWEB)

    Glickman, Matthew R.; Tang, Akaysha (University of New Mexico, Albuquerque, NM)

    2009-02-01

    The motivating vision behind Sandia's MENTOR/PAL LDRD project has been that of systems which use real-time psychophysiological data to support and enhance human performance, both individually and of groups. Relevant and significant psychophysiological data being a necessary prerequisite to such systems, this LDRD has focused on identifying and refining such signals. The project has focused in particular on EEG (electroencephalogram) data as a promising candidate signal because it (potentially) provides a broad window on brain activity with relatively low cost and logistical constraints. We report here on two analyses performed on EEG data collected in this project using the SOBI (Second Order Blind Identification) algorithm to identify two independent sources of brain activity: one in the frontal lobe and one in the occipital. The first study looks at directional influences between the two components, while the second study looks at inferring gender based upon the frontal component.

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

    Science.gov (United States)

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

    2018-01-01

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

  14. EEG-fMRI Bayesian framework for neural activity estimation: a simulation study

    Science.gov (United States)

    Croce, Pierpaolo; Basti, Alessio; Marzetti, Laura; Zappasodi, Filippo; Del Gratta, Cosimo

    2016-12-01

    Objective. Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. Approach. We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). Main results. First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. Significance. The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.

  15. Parallelised photoacoustic signal acquisition using a Fabry-Perot sensor and a camera-based interrogation scheme

    Science.gov (United States)

    Saeb Gilani, T.; Villringer, C.; Zhang, E.; Gundlach, H.; Buchmann, J.; Schrader, S.; Laufer, J.

    2018-02-01

    Tomographic photoacoustic (PA) images acquired using a Fabry-Perot (FP) based scanner offer high resolution and image fidelity but can result in long acquisition times due to the need for raster scanning. To reduce the acquisition times, a parallelised camera-based PA signal detection scheme is developed. The scheme is based on using a sCMOScamera and FPI sensors with high homogeneity of optical thickness. PA signals were acquired using the camera-based setup and the signal to noise ratio (SNR) was measured. A comparison of the SNR of PA signal detected using 1) a photodiode in a conventional raster scanning detection scheme and 2) a sCMOS camera in parallelised detection scheme is made. The results show that the parallelised interrogation scheme has the potential to provide high speed PA imaging.

  16. Acquisition and extinction of human avoidance behavior: Attenuating effect of safety signals and associations with anxiety vulnerabilities

    Directory of Open Access Journals (Sweden)

    Jony eSheynin

    2014-09-01

    Full Text Available While avoidance behavior is often an adaptive strategy, exaggerated avoidance can be detrimental and result in the development of psychopathologies, such as anxiety disorders. A large animal literature shows that the acquisition and extinction of avoidance behavior in rodents depends on individual differences (e.g., sex, strain and might be modulated by the presence of environmental cues. However, there is a dearth of such reports in human literature, mainly due to the lack of adequate experimental paradigms. In the current study, we employed a computer-based task, where participants control a spaceship and attempt to gain points by shooting an enemy spaceship that appears on the screen. Warning signals predict on-screen aversive events; the participants can learn a protective response to escape or avoid these events. This task has been recently used to reveal facilitated acquisition of avoidance behavior in individuals with anxiety vulnerability, due to female sex or inhibited personality. Here, we extended the task to include an extinction phase, and tested the effect of signals that appeared during safe periods. Healthy young adults (n=122 were randomly assigned to a testing condition with or without such signals. Results showed that the addition of safety signals during the acquisition phase impaired acquisition (in females and facilitated extinction of the avoidance behavior. We also replicated our recent finding of an association between female sex and longer avoidance duration and further showed that females continued to demonstrate more avoidance behavior even on extinction trials when the aversive events no longer occurred. This study is the first to show sex differences on the acquisition and extinction of human avoidance behavior and to demonstrate the role of safety signals in such behavior, highlighting the potential relevance of safety signals for cognitive therapies that focus on extinction learning to treat anxiety symptoms.

  17. The Effect of Signal Quality and Contiguous Word of Mouth on Customer Acquisition for a Video-on-Demand Service

    OpenAIRE

    Sungjoon Nam; Puneet Manchanda; Pradeep K. Chintagunta

    2010-01-01

    This paper documents the existence and magnitude of contiguous word-of-mouth effects of signal quality of a video-on-demand (VOD) service on customer acquisition. We operationalize contiguous word-of-mouth effect based on geographic proximity and use behavioral data to quantify the effect. The signal quality for this VOD service is exogenously determined, objectively measured, and spatially uncorrelated. Furthermore, it is unobserved to the potential subscriber and is revealed postadoption. F...

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

    Science.gov (United States)

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

    2017-10-01

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

  19. Field acquisition system of signals produced by 60 Hz electric power distribution networks; Sistema de aquisicao em campo de sinais produzidos por redes eletricas de 60 Hz

    Energy Technology Data Exchange (ETDEWEB)

    Arasaki, Arnaldo Takashi

    1996-12-31

    Signal acquisition and analysis problems generally require expensive instruments whose utilization may be optimized through the use of portable signal acquisition modules with signal storage capability. Signals could be registered where they are generated and then analyzed properly in a laboratory far away. A portable acquisition module is developed after studying promising solutions such as direct analog storage, analog-to-digital binary conversion and delta modulation of the collected signals. Since some years ago, a solid state non-volatile analog memory has been commercially available and it was chosen in this portable module design. It also provides low power consumption, turning out to be interesting to signal acquisition and storage applications. This work also presents the portable acquisition module results. (author) 100 refs., 48 figs., 7 tabs.

  20. Field acquisition system of signals produced by 60 Hz electric power distribution networks; Sistema de aquisicao em campo de sinais produzidos por redes eletricas de 60 Hz

    Energy Technology Data Exchange (ETDEWEB)

    Arasaki, Arnaldo Takashi

    1997-12-31

    Signal acquisition and analysis problems generally require expensive instruments whose utilization may be optimized through the use of portable signal acquisition modules with signal storage capability. Signals could be registered where they are generated and then analyzed properly in a laboratory far away. A portable acquisition module is developed after studying promising solutions such as direct analog storage, analog-to-digital binary conversion and delta modulation of the collected signals. Since some years ago, a solid state non-volatile analog memory has been commercially available and it was chosen in this portable module design. It also provides low power consumption, turning out to be interesting to signal acquisition and storage applications. This work also presents the portable acquisition module results. (author) 100 refs., 48 figs., 7 tabs.

  1. Development of Optical Molecular Imaging System for the Acquisition of Bioluminescence Signals from Small Animals

    International Nuclear Information System (INIS)

    Lee, Byeong Il; Kim, Hyeon Sik; Jeong, Hye Jin; Lee, Hyung Jae; Moon, Seung Min; Kwon, Seung Young; Jeong, Shin Young; Bom, Hee Seung; Min, Jung Joon; Choi, Eun Seo

    2009-01-01

    Optical imaging is providing great advance and improvement in genetic and molecular imaging of animals and humans. Optical imaging system consists of optical imaging devices, which carry out major function for monitoring, tracing, and imaging in most of molecular in-vivo researches. In bio-luminescent imaging, small animals containing luciferase gene locally irradiate light, and emitted photons transmitted through skin of the small animals are imaged by using a high sensitive charged coupled device (CCD) camera. In this paper, we introduced optical imaging system for the image acquisition of bio-luminescent signals emitted from small animals. In the system, Nikon lens and four LED light sources were mounted at the inside of a dark box. A cooled CCD camera equipped with a control module was used. We tested the performance of the optical imaging system using effendorf tube and light emitting bacteria which injected intravenously into CT26 tumor bearing nude mouse. The performance of implemented optical imaging system for bio-luminescence imaging was demonstrated and the feasibility of the system in small animal imaging application was proved. We anticipate this system could be a useful tool for the molecular imaging of small animals adaptable for various experimental conditions in future

  2. The Traffic Signal Acquisition System Based on GPS and SD Card Storage

    Directory of Open Access Journals (Sweden)

    LIU Chang-yuan

    2017-06-01

    Full Text Available In terms of the issues where traffic lights’ positions and traffic status information cannot be managed automatically,in this system,STC12C5A60S2 microcontroller can be used as the master chip in conjunction with the GPS position module,Neo-5Q. The wireless transceiver module,PT2262 /2272 and the portable installing SD card are used to design a new type of real-time information acquisition solution for positions of traffic lights and signal status. And the system can determine the traffic lights’ positions and the process of lighting in a real time. Then the data will be stored in SD card by the SD card module. Furthermore,the equipment can be implemented on existing facilities with a simple circuit. According to the result of experiments,the system contains a convenient storage,works in a real time and it is also advisable to help with the data reading and analysis. Thus, implementation of the system is of great significance to acquire and analyze the traffic status information in recent times.

  3. Acquisition of electrical signals using commercial electronic components for detection system of Lead ion in distilled water

    Science.gov (United States)

    Pujiyanto; Yasin, M.; Rusydi, F.

    2018-03-01

    Development of lead ion detection systems is expected to have an advantage in terms of simplicity of the device and easy for concentration analysis of a lead ion with very high performance. One important part of lead ion detection systems are electrical signal acquisition parts. The electrical signal acquisition part uses the main electronic components: non inverting op-amplifier, instrumentation amplifier, multiplier circuit and logarithmic amplifier. Here will be shown the performance of lead ion detection systems when the existing electrical signal processors use commercial electronic components. The results that can be drawn from this experimental were the lead ion sensor that has been developed can be used to detect lead ions with a sensitivity of 10.48 mV/ppm with the linearity 97.11% and had a measurement range of 0.1 ppm to 80 ppm.

  4. Selecting the optimal anti-aliasing filter for multichannel biosignal acquisition intended for inter-signal phase shift analysis

    International Nuclear Information System (INIS)

    Keresnyei, Róbert; Hejjel, László; Megyeri, Péter; Zidarics, Zoltán

    2015-01-01

    The availability of microcomputer-based portable devices facilitates the high-volume multichannel biosignal acquisition and the analysis of their instantaneous oscillations and inter-signal temporal correlations. These new, non-invasively obtained parameters can have considerable prognostic or diagnostic roles. The present study investigates the inherent signal delay of the obligatory anti-aliasing filters. One cycle of each of the 8 electrocardiogram (ECG) and 4 photoplethysmogram signals from healthy volunteers or artificially synthesised series were passed through 100–80–60–40–20 Hz 2–4–6–8th order Bessel and Butterworth filters digitally synthesized by bilinear transformation, that resulted in a negligible error in signal delay compared to the mathematical model of the impulse- and step responses of the filters. The investigated filters have as diverse a signal delay as 2–46 ms depending on the filter parameters and the signal slew rate, which is difficult to predict in biological systems and thus difficult to compensate for. Its magnitude can be comparable to the examined phase shifts, deteriorating the accuracy of the measurement. As a conclusion, identical or very similar anti-aliasing filters with lower orders and higher corner frequencies, oversampling, and digital low pass filtering are recommended for biosignal acquisition intended for inter-signal phase shift analysis. (note)

  5. A user configurable data acquisition and signal processing system for high-rate, high channel count applications

    International Nuclear Information System (INIS)

    Salim, Arwa; Crockett, Louise; McLean, John; Milne, Peter

    2012-01-01

    Highlights: ► The development of a new digital signal processing platform is described. ► The system will allow users to configure the real-time signal processing through software routines. ► The architecture of the DRUID system and signal processing elements is described. ► A prototype of the DRUID system has been developed for the digital chopper-integrator. ► The results of acquisition on 96 channels at 500 kSamples/s per channel are presented. - Abstract: Real-time signal processing in plasma fusion experiments is required for control and for data reduction as plasma pulse times grow longer. The development time and cost for these high-rate, multichannel signal processing systems can be significant. This paper proposes a new digital signal processing (DSP) platform for the data acquisition system that will allow users to easily customize real-time signal processing systems to meet their individual requirements. The D-TACQ reconfigurable user in-line DSP (DRUID) system carries out the signal processing tasks in hardware co-processors (CPs) implemented in an FPGA, with an embedded microprocessor (μP) for control. In the fully developed platform, users will be able to choose co-processors from a library and configure programmable parameters through the μP to meet their requirements. The DRUID system is implemented on a Spartan 6 FPGA, on the new rear transition module (RTM-T), a field upgrade to existing D-TACQ digitizers. As proof of concept, a multiply-accumulate (MAC) co-processor has been developed, which can be configured as a digital chopper-integrator for long pulse magnetic fusion devices. The DRUID platform allows users to set options for the integrator, such as the number of masking samples. Results from the digital integrator are presented for a data acquisition system with 96 channels simultaneously acquiring data at 500 kSamples/s per channel.

  6. Source localization of rhythmic ictal EEG activity

    DEFF Research Database (Denmark)

    Beniczky, Sándor; Lantz, Göran; Rosenzweig, Ivana

    2013-01-01

    Although precise identification of the seizure-onset zone is an essential element of presurgical evaluation, source localization of ictal electroencephalography (EEG) signals has received little attention. The aim of our study was to estimate the accuracy of source localization of rhythmic ictal...... EEG activity using a distributed source model....

  7. Multiphoton minimal inertia scanning for fast acquisition of neural activity signals

    Science.gov (United States)

    Schuck, Renaud; Go, Mary Ann; Garasto, Stefania; Reynolds, Stephanie; Dragotti, Pier Luigi; Schultz, Simon R.

    2018-04-01

    Objective. Multi-photon laser scanning microscopy provides a powerful tool for monitoring the spatiotemporal dynamics of neural circuit activity. It is, however, intrinsically a point scanning technique. Standard raster scanning enables imaging at subcellular resolution; however, acquisition rates are limited by the size of the field of view to be scanned. Recently developed scanning strategies such as travelling salesman scanning (TSS) have been developed to maximize cellular sampling rate by scanning only select regions in the field of view corresponding to locations of interest such as somata. However, such strategies are not optimized for the mechanical properties of galvanometric scanners. We thus aimed to develop a new scanning algorithm which produces minimal inertia trajectories, and compare its performance with existing scanning algorithms. Approach. We describe here the adaptive spiral scanning (SSA) algorithm, which fits a set of near-circular trajectories to the cellular distribution to avoid inertial drifts of galvanometer position. We compare its performance to raster scanning and TSS in terms of cellular sampling frequency and signal-to-noise ratio (SNR). Main Results. Using surrogate neuron spatial position data, we show that SSA acquisition rates are an order of magnitude higher than those for raster scanning and generally exceed those achieved by TSS for neural densities comparable with those found in the cortex. We show that this result also holds true for in vitro hippocampal mouse brain slices bath loaded with the synthetic calcium dye Cal-520 AM. The ability of TSS to ‘park’ the laser on each neuron along the scanning trajectory, however, enables higher SNR than SSA when all targets are precisely scanned. Raster scanning has the highest SNR but at a substantial cost in number of cells scanned. To understand the impact of sampling rate and SNR on functional calcium imaging, we used the Cramér-Rao Bound on evoked calcium traces recorded

  8. Modelling Cardiac Signal as a Confound in EEG-fMRI and its Application in Focal Epilepsy

    DEFF Research Database (Denmark)

    Liston, Adam David; Salek-Haddadi, Afraim; Hamandi, Khalid

    2005-01-01

    Cardiac noise has been shown to reduce the sensitivity of functional Magnetic Resonance Imaging (fMRI) to an experimental effect due to its confounding presence in the blood oxygenation level-dependent (BOLD) signal. Its effect is most severe in particular regions of the brain and a method is yet...

  9. An embedded EEG analyzing system based on muC/os-II.

    Science.gov (United States)

    Liu, Boqiang; Zhang, Yanyan; Liu, Zhongguo; Yin, Cong

    2007-01-01

    An EEG analyzing system based on Advanced RISC Machines (ARM) and muC/os-II real time operating system is discussed in this paper. The detailed system design including the producing of event signals and the synchronization between event signals and EEG signals is described. The details of data acquisition, data preprocessing, data transmitting through USB and system configurations are also contained in the system design. In this paper the design of high capability amplifier and the software of embedded subsystem are discussed. Also the design of realizing multi-task system in muC/os-II, the definition of communicating protocols between PC and the equipment and the detail configurations of USB are given out. The final test shows that the filter behaviors of this equipment are feasible.

  10. Self-Adhesive and Capacitive Carbon Nanotube-Based Electrode to Record Electroencephalograph Signals From the Hairy Scalp.

    Science.gov (United States)

    Lee, Seung Min; Kim, Jeong Hun; Park, Cheolsoo; Hwang, Ji-Young; Hong, Joung Sook; Lee, Kwang Ho; Lee, Sang Hoon

    2016-01-01

    We fabricated a carbon nanotube (CNT)/adhesive polydimethylsiloxane (aPDMS) composite-based dry electroencephalograph (EEG) electrode for capacitive measuring of EEG signals. As research related to brain-computer interface applications has advanced, the presence of hairs on a patient's scalp has continued to present an obstacle to recorder EEG signals using dry electrodes. The CNT/aPDMS electrode developed here is elastic, highly conductive, self-adhesive, and capable of making conformal contact with and attaching to a hairy scalp. Onto the conductive disk, hundreds of conductive pillars coated with Parylene C insulation layer were fabricated. A CNT/aPDMS layer was attached on the disk to transmit biosignals to the pillar. The top of disk was designed to be solderable, which enables the electrode to connect with a variety of commercial EEG acquisition systems. The mechanical and electrical characteristics of the electrode were tested, and the performances of the electrodes were evaluated by recording EEGs, including alpha rhythms, auditory-evoked potentials, and steady-state visually-evoked potentials. The results revealed that the electrode provided a high signal-to-noise ratio with good tolerance for motion. Almost no leakage current was observed. Although preamplifiers with ultrahigh input impedance have been essential for previous capacitive electrodes, the EEGs were recorded here by directly connecting a commercially available EEG acquisition system to the electrode to yield high-quality signals comparable to those obtained using conventional wet electrodes.

  11. INTELLIGENT EEG ANALYSIS

    Directory of Open Access Journals (Sweden)

    M. Murugesan

    2011-04-01

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

  12. A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG

    OpenAIRE

    Chen, Xun; Liu, Aiping; Peng, Hu; Ward, Rabab K.

    2014-01-01

    Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis. For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts. In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems. For pra...

  13. A Machine Learning Approach to the Detection of Pilot’s Reaction to Unexpected Events Based on EEG Signals

    Directory of Open Access Journals (Sweden)

    Bartosz Binias

    2018-01-01

    Full Text Available This work considers the problem of utilizing electroencephalographic signals for use in systems designed for monitoring and enhancing the performance of aircraft pilots. Systems with such capabilities are generally referred to as cognitive cockpits. This article provides a description of the potential that is carried by such systems, especially in terms of increasing flight safety. Additionally, a neuropsychological background of the problem is presented. Conducted research was focused mainly on the problem of discrimination between states of brain activity related to idle but focused anticipation of visual cue and reaction to it. Especially, a problem of selecting a proper classification algorithm for such problems is being examined. For that purpose an experiment involving 10 subjects was planned and conducted. Experimental electroencephalographic data was acquired using an Emotiv EPOC+ headset. Proposed methodology involved use of a popular method in biomedical signal processing, the Common Spatial Pattern, extraction of bandpower features, and an extensive test of different classification algorithms, such as Linear Discriminant Analysis, k-nearest neighbors, and Support Vector Machines with linear and radial basis function kernels, Random Forests, and Artificial Neural Networks.

  14. Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

    Directory of Open Access Journals (Sweden)

    François-B. Vialatte

    2011-01-01

    Full Text Available Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD. However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ (3.5–7.5 Hz, α1 (7.5–9.5 Hz, α2 (9.5–12.5 Hz, and β (12.5–25 Hz. The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the θ range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.

  15. An evanescent wave biosensor--Part I: Fluorescent signal acquisition from step-etched fiber optic probes.

    Science.gov (United States)

    Anderson, G P; Golden, J P; Ligler, F S

    1994-06-01

    A fiber-optic biosensor capable of remote continuous monitoring has recently been designed. To permit sensing at locations separate from the optoelectronic instrumentation, long optical fibers are utilized. An evanescent wave immuno-probe is prepared by removing the cladding near the distal end of the fiber and covalently attaching antibodies to the core. Probes with a radius unaltered from that of the original core inefficiently returned the signal produced upon binding the fluorescent-labelled antigen. To elucidate the limiting factors in signal acquisition, a series of fibers with increasingly reduced probe core radius was examined. The results were consistent with the V-number mismatch, the difference in mode carrying capacity between the clad and unclad fiber, being a critical factor in limiting signal coupling from the fiber probe. However, it was also delineated that conditions which conserve excitation power, such that power in the evanescent wave is optimized, must also be met to obtain a maximal signal. The threshold sensitivity for the optimal step-etched fiber probe was improved by over 20-fold in an immunoassay, although, it was demonstrated that signal acquisition decreased along the probe length, suggesting that a sensor region of uniform radius is not ideal.

  16. Dealing with noise and physiological artifacts in human EEG recordings: empirical mode methods

    Science.gov (United States)

    Runnova, Anastasiya E.; Grubov, Vadim V.; Khramova, Marina V.; Hramov, Alexander E.

    2017-04-01

    In the paper we propose the new method for removing noise and physiological artifacts in human EEG recordings based on empirical mode decomposition (Hilbert-Huang transform). As physiological artifacts we consider specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the proposed method with steps including empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing these empirical modes and reconstructing of initial EEG signal. We show the efficiency of the method on the example of filtration of human EEG signal from eye-moving artifacts.

  17. EEG simulation by 2D interconnected chaotic oscillators

    International Nuclear Information System (INIS)

    Kubany, Adam; Mhabary, Ziv; Gontar, Vladimir

    2011-01-01

    Research highlights: → ANN of 2D interconnected chaotic oscillators is explored for EEG simulation. → An inverse problem solution (PRCGA) is proposed. → Good matching between the simulated and experimental EEG signals has been achieved. - Abstract: An artificial neuronal network composed by 2D interconnected chaotic oscillators is explored for brain waves (EEG) simulation. For the inverse problem solution a parallel real-coded genetic algorithm (PRCGA) is proposed. In order to conduct thorough comparison between the simulated and target signal characteristics, a spectrum analysis of the signals is undertaken. A good matching between the theoretical and experimental EEG signals has been achieved. Numerical results of calculations are presented and discussed.

  18. EEG simulation by 2D interconnected chaotic oscillators

    Energy Technology Data Exchange (ETDEWEB)

    Kubany, Adam, E-mail: adamku@bgu.ac.i [Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 84105 (Israel); Mhabary, Ziv; Gontar, Vladimir [Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 84105 (Israel)

    2011-01-15

    Research highlights: ANN of 2D interconnected chaotic oscillators is explored for EEG simulation. An inverse problem solution (PRCGA) is proposed. Good matching between the simulated and experimental EEG signals has been achieved. - Abstract: An artificial neuronal network composed by 2D interconnected chaotic oscillators is explored for brain waves (EEG) simulation. For the inverse problem solution a parallel real-coded genetic algorithm (PRCGA) is proposed. In order to conduct thorough comparison between the simulated and target signal characteristics, a spectrum analysis of the signals is undertaken. A good matching between the theoretical and experimental EEG signals has been achieved. Numerical results of calculations are presented and discussed.

  19. EEG analysis in a telemedical virtual world

    NARCIS (Netherlands)

    Jovanov, E.; Starcevic, D.; Samardzic, A.; Marsh, A.; Obrenovic, Z.

    1999-01-01

    Telemedicine creates virtual medical collaborative environments. We propose here a novel concept of virtual medical devices (VMD) for telemedical applications. VMDs provide different views on biomedical recordings and efficient signal analysis. In this paper we present a telemedical EEG analysis

  20. Induction and separation of motion artifacts in EEG data using a mobile phantom head device.

    Science.gov (United States)

    Oliveira, Anderson S; Schlink, Bryan R; Hairston, W David; König, Peter; Ferris, Daniel P

    2016-06-01

    Electroencephalography (EEG) can assess brain activity during whole-body motion in humans but head motion can induce artifacts that obfuscate electrocortical signals. Definitive solutions for removing motion artifact from EEG have yet to be found, so creating methods to assess signal processing routines for removing motion artifact are needed. We present a novel method for investigating the influence of head motion on EEG recordings as well as for assessing the efficacy of signal processing approaches intended to remove motion artifact. We used a phantom head device to mimic electrical properties of the human head with three controlled dipolar sources of electrical activity embedded in the phantom. We induced sinusoidal vertical motions on the phantom head using a custom-built platform and recorded EEG signals with three different acquisition systems while the head was both stationary and in varied motion conditions. Recordings showed up to 80% reductions in signal-to-noise ratio (SNR) and up to 3600% increases in the power spectrum as a function of motion amplitude and frequency. Independent component analysis (ICA) successfully isolated the three dipolar sources across all conditions and systems. There was a high correlation (r > 0.85) and marginal increase in the independent components' (ICs) power spectrum (∼15%) when comparing stationary and motion parameters. The SNR of the IC activation was 400%-700% higher in comparison to the channel data SNR, attenuating the effects of motion on SNR. Our results suggest that the phantom head and motion platform can be used to assess motion artifact removal algorithms and compare different EEG systems for motion artifact sensitivity. In addition, ICA is effective in isolating target electrocortical events and marginally improving SNR in relation to stationary recordings.

  1. Physiological artifacts in scalp EEG and ear-EEG.

    Science.gov (United States)

    Kappel, Simon L; Looney, David; Mandic, Danilo P; Kidmose, Preben

    2017-08-11

    A problem inherent to recording EEG is the interference arising from noise and artifacts. While in a laboratory environment, artifacts and interference can, to a large extent, be avoided or controlled, in real-life scenarios this is a challenge. Ear-EEG is a concept where EEG is acquired from electrodes in the ear. We present a characterization of physiological artifacts generated in a controlled environment for nine subjects. The influence of the artifacts was quantified in terms of the signal-to-noise ratio (SNR) deterioration of the auditory steady-state response. Alpha band modulation was also studied in an open/closed eyes paradigm. Artifacts related to jaw muscle contractions were present all over the scalp and in the ear, with the highest SNR deteriorations in the gamma band. The SNR deterioration for jaw artifacts were in general higher in the ear compared to the scalp. Whereas eye-blinking did not influence the SNR in the ear, it was significant for all groups of scalps electrodes in the delta and theta bands. Eye movements resulted in statistical significant SNR deterioration in both frontal, temporal and ear electrodes. Recordings of alpha band modulation showed increased power and coherence of the EEG for ear and scalp electrodes in the closed-eyes periods. Ear-EEG is a method developed for unobtrusive and discreet recording over long periods of time and in real-life environments. This study investigated the influence of the most important types of physiological artifacts, and demonstrated that spontaneous activity, in terms of alpha band oscillations, could be recorded from the ear-EEG platform. In its present form ear-EEG was more prone to jaw related artifacts and less prone to eye-blinking artifacts compared to state-of-the-art scalp based systems.

  2. The added value of simultaneous EEG and amplitude-integrated EEG recordings in three newborn infants

    NARCIS (Netherlands)

    de Vries, Nathalie K. S.; ter Horst, Hendrik J.; Bos, Arend F.

    2007-01-01

    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.

  3. A Pervasive Approach to EEG-Based Depression Detection

    Directory of Open Access Journals (Sweden)

    Hanshu Cai

    2018-01-01

    Full Text Available Nowadays, depression is the world’s major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 (92 depressed patients and 121 normal controls subjects, was constructed. The electroencephalogram (EEG signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe three-electrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods (Support Vector Machine, K-Nearest Neighbor, Classification Trees, and Artificial Neural Network distinguished the depressed participants from normal controls. The classifiers’ performances were evaluated using 10-fold cross-validation. The results showed that K-Nearest Neighbor (KNN had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis.

  4. A Low-Power CMOS Front-End for Photoplethysmographic Signal Acquisition With Robust DC Photocurrent Rejection.

    Science.gov (United States)

    Wong, A K Y; Kong-Pang Pun; Yuan-Ting Zhang; Ka Nang Leung

    2008-12-01

    A micro-power CMOS front-end, consisting of a transimpedance amplifier (TIA) and an ultralow cutoff frequency lowpass filter for the acquisition of photoplethysmographic signal (PPG) is presented. Robust DC photocurrent rejection for the pulsed signal source is achieved through a sample-and-hold stage in the feed-forward signal path and an error amplifier in the feedback path. Ultra-low cutoff frequency of the filter is achieved with a proposed technique that incorporates a pair of current-steering transistors that increases the effective filter capacitance. The design was realized in a 0.35-mum CMOS technology. It consumes 600 muW at 2.5 V, rejects DC photocurrent ranged from 100 nA to 53.6 muA, and achieves lower-band and upper-band - 3-dB cutoff frequencies of 0.46 and 2.8 Hz, respectively.

  5. Oxytocin Signaling in Basolateral and Central Amygdala Nuclei Differentially Regulates the Acquisition, Expression, and Extinction of Context-Conditioned Fear in Rats

    Science.gov (United States)

    Campbell-Smith, Emma J.; Holmes, Nathan M.; Lingawi, Nura W.; Panayi, Marios C.; Westbrook, R. Frederick

    2015-01-01

    The present study investigated how oxytocin (OT) signaling in the central (CeA) and basolateral (BLA) amygdala affects acquisition, expression, and extinction of context-conditioned fear (freezing) in rats. In the first set of experiments, acquisition of fear to a shocked context was impaired by a preconditioning infusion of synthetic OT into the…

  6. In-Containment Signal Acquisition and Data Transmission via Power Lines within High Dose Areas of Nuclear Power Plants

    International Nuclear Information System (INIS)

    Mueller Steffen; Wibbing Sascha; Weigel Robert; Koelpin Alexander; Dennerlein, Juergen; Janke, Iryna; Weber, Johannes

    2015-01-01

    Signal acquisition and data transmission for innovative sensor systems and networks inside the containment of nuclear power plants (NPPs) is still a challenge with respect to safety, performance, reliability, availability, and costs. This especially applies to equipment upgrades for existing plants, special measurements, but also for new builds. This paper presents a novel method for efficient and cost-effective sensor signal acquisition and data transmission via power lines, in order to cope with the disadvantages of common system architectures that often suffer from poor signal integrity due to raw data transmissions via long cables, huge efforts and costs for installation, and low flexibility with respect to maintenance and upgrades. A transmitter-receiver architecture is proposed that allows multiplexing of multiple sensor inputs for unidirectional point-to-point transmission by superimposing information signals on existing AC or DC supply lines, but also on active and inactive sensor wires, or spare cables, using power line communication (PLC) technology. Based on commercial off-the-shelf (COTS) electronic parts, a radiation hard transmitter hardware is designed to operate in harsh environment within the containment during full plant operation. The system's modular approach allows application specific trade-offs between redundancy and throughput regarding data transmission, as well as various sensor input front-ends which are compatible with state of the art systems. PLC technology eliminates the need for costly installation of additional cables and wall penetrations, while providing a complementary and diverse communication technology for upgrades of existing systems. At the receiver side in low dose areas, signals are extracted from the power line, demodulated, and de-multiplexed, in order to regain the original sensor signal information and provide it either in analog or digital output format. Successful laboratory qualification tests, field trails

  7. In-Containment Signal Acquisition and Data Transmission via Power Lines within High Dose Areas of Nuclear Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    Mueller Steffen; Wibbing Sascha; Weigel Robert; Koelpin Alexander [Institute for Electronics Engineering, University of Erlangen-Nuremberg (Germany); Dennerlein, Juergen; Janke, Iryna; Weber, Johannes [AREVA GmbH, Paul-Gossen-Str. 100, 91052 Erlangen (Germany)

    2015-07-01

    Signal acquisition and data transmission for innovative sensor systems and networks inside the containment of nuclear power plants (NPPs) is still a challenge with respect to safety, performance, reliability, availability, and costs. This especially applies to equipment upgrades for existing plants, special measurements, but also for new builds. This paper presents a novel method for efficient and cost-effective sensor signal acquisition and data transmission via power lines, in order to cope with the disadvantages of common system architectures that often suffer from poor signal integrity due to raw data transmissions via long cables, huge efforts and costs for installation, and low flexibility with respect to maintenance and upgrades. A transmitter-receiver architecture is proposed that allows multiplexing of multiple sensor inputs for unidirectional point-to-point transmission by superimposing information signals on existing AC or DC supply lines, but also on active and inactive sensor wires, or spare cables, using power line communication (PLC) technology. Based on commercial off-the-shelf (COTS) electronic parts, a radiation hard transmitter hardware is designed to operate in harsh environment within the containment during full plant operation. The system's modular approach allows application specific trade-offs between redundancy and throughput regarding data transmission, as well as various sensor input front-ends which are compatible with state of the art systems. PLC technology eliminates the need for costly installation of additional cables and wall penetrations, while providing a complementary and diverse communication technology for upgrades of existing systems. At the receiver side in low dose areas, signals are extracted from the power line, demodulated, and de-multiplexed, in order to regain the original sensor signal information and provide it either in analog or digital output format. Successful laboratory qualification tests, field trails

  8. Hemodynamic Response to Interictal Epileptiform Discharges Addressed by Personalized EEG-fNIRS Recordings

    Science.gov (United States)

    Pellegrino, Giovanni; Machado, Alexis; von Ellenrieder, Nicolas; Watanabe, Satsuki; Hall, Jeffery A.; Lina, Jean-Marc; Kobayashi, Eliane; Grova, Christophe

    2016-01-01

    Objective: We aimed at studying the hemodynamic response (HR) to Interictal Epileptic Discharges (IEDs) using patient-specific and prolonged simultaneous ElectroEncephaloGraphy (EEG) and functional Near InfraRed Spectroscopy (fNIRS) recordings. Methods: The epileptic generator was localized using Magnetoencephalography source imaging. fNIRS montage was tailored for each patient, using an algorithm to optimize the sensitivity to the epileptic generator. Optodes were glued using collodion to achieve prolonged acquisition with high quality signal. fNIRS data analysis was handled with no a priori constraint on HR time course, averaging fNIRS signals to similar IEDs. Cluster-permutation analysis was performed on 3D reconstructed fNIRS data to identify significant spatio-temporal HR clusters. Standard (GLM with fixed HRF) and cluster-permutation EEG-fMRI analyses were performed for comparison purposes. Results: fNIRS detected HR to IEDs for 8/9 patients. It mainly consisted oxy-hemoglobin increases (seven patients), followed by oxy-hemoglobin decreases (six patients). HR was lateralized in six patients and lasted from 8.5 to 30 s. Standard EEG-fMRI analysis detected an HR in 4/9 patients (4/9 without enough IEDs, 1/9 unreliable result). The cluster-permutation EEG-fMRI analysis restricted to the region investigated by fNIRS showed additional strong and non-canonical BOLD responses starting earlier than the IEDs and lasting up to 30 s. Conclusions: (i) EEG-fNIRS is suitable to detect the HR to IEDs and can outperform EEG-fMRI because of prolonged recordings and greater chance to detect IEDs; (ii) cluster-permutation analysis unveils additional HR features underestimated when imposing a canonical HR function (iii) the HR is often bilateral and lasts up to 30 s. PMID:27047325

  9. Hemodynamic response to Interictal Epileptiform Discharges addressed by personalized EEG-fNIRS recordings

    Directory of Open Access Journals (Sweden)

    Giovanni ePellegrino

    2016-03-01

    Full Text Available Objective: We aimed at studying the hemodynamic response (HR to Interictal Epileptic Discharges (IEDs using patient-specific and prolonged simultaneous ElectroEncephaloGraphy (EEG and functional Near InfraRed Spectroscopy (fNIRS recordings. Methods: The epileptic generator was localized using Magnetoencephalography source imaging. fNIRS montage was tailored for each patient, using an algorithm to optimize the sensitivity to the epileptic generator. Optodes were glued using collodion to achieve prolonged acquisition with high quality signal. fNIRS data analysis was handled with no a priori constraint on HR time course, averaging fNIRS signals to similar IEDs. Cluster-permutation analysis was performed on 3D reconstructed fNIRS data to identify significant spatio-temporal HR clusters. Standard (GLM with fixed HRF and cluster-permutation EEG-fMRI analyses were performed for comparison purposes. Results: fNIRS detected HR to IEDs for 8/9 patients. It mainly consisted oxy-hemoglobin increases (7 patients, followed by oxy-hemoglobin decreases (6 patients. HR was lateralized in 6 patients and lasted from 8.5 to 30s. Standard EEG-fMRI analysis detected an HR in 4/9 patients (4/9 without enough IEDs, 1/9 unreliable result. The cluster-permutation EEG-fMRI analysis restricted to the region investigated by fNIRS showed additional strong and non-canonical BOLD responses starting earlier than the IEDs and lasting up to 30s. Conclusions: i EEG-fNIRS is suitable to detect the HR to IEDs and can outperform EEG-fMRI because of prolonged recordings and greater chance to detect IEDs; ii cluster-permutation analysis unveils additional HR features underestimated when imposing a canonical HR function iii the HR is often bilateral and lasts up to 30s.

  10. Human-machine interfaces based on EMG and EEG applied to robotic systems

    Directory of Open Access Journals (Sweden)

    Sarcinelli-Filho Mario

    2008-03-01

    Full Text Available Abstract Background Two different Human-Machine Interfaces (HMIs were developed, both based on electro-biological signals. One is based on the EMG signal and the other is based on the EEG signal. Two major features of such interfaces are their relatively simple data acquisition and processing systems, which need just a few hardware and software resources, so that they are, computationally and financially speaking, low cost solutions. Both interfaces were applied to robotic systems, and their performances are analyzed here. The EMG-based HMI was tested in a mobile robot, while the EEG-based HMI was tested in a mobile robot and a robotic manipulator as well. Results Experiments using the EMG-based HMI were carried out by eight individuals, who were asked to accomplish ten eye blinks with each eye, in order to test the eye blink detection algorithm. An average rightness rate of about 95% reached by individuals with the ability to blink both eyes allowed to conclude that the system could be used to command devices. Experiments with EEG consisted of inviting 25 people (some of them had suffered cases of meningitis and epilepsy to test the system. All of them managed to deal with the HMI in only one training session. Most of them learnt how to use such HMI in less than 15 minutes. The minimum and maximum training times observed were 3 and 50 minutes, respectively. Conclusion Such works are the initial parts of a system to help people with neuromotor diseases, including those with severe dysfunctions. The next steps are to convert a commercial wheelchair in an autonomous mobile vehicle; to implement the HMI onboard the autonomous wheelchair thus obtained to assist people with motor diseases, and to explore the potentiality of EEG signals, making the EEG-based HMI more robust and faster, aiming at using it to help individuals with severe motor dysfunctions.

  11. High-Speed Data Acquisition and Digital Signal Processing System for PET Imaging Techniques Applied to Mammography

    Science.gov (United States)

    Martinez, J. D.; Benlloch, J. M.; Cerda, J.; Lerche, Ch. W.; Pavon, N.; Sebastia, A.

    2004-06-01

    This paper is framed into the Positron Emission Mammography (PEM) project, whose aim is to develop an innovative gamma ray sensor for early breast cancer diagnosis. Currently, breast cancer is detected using low-energy X-ray screening. However, functional imaging techniques such as PET/FDG could be employed to detect breast cancer and track disease changes with greater sensitivity. Furthermore, a small and less expensive PET camera can be utilized minimizing main problems of whole body PET. To accomplish these objectives, we are developing a new gamma ray sensor based on a newly released photodetector. However, a dedicated PEM detector requires an adequate data acquisition (DAQ) and processing system. The characterization of gamma events needs a free-running analog-to-digital converter (ADC) with sampling rates of more than 50 Ms/s and must achieve event count rates up to 10 MHz. Moreover, comprehensive data processing must be carried out to obtain event parameters necessary for performing the image reconstruction. A new generation digital signal processor (DSP) has been used to comply with these requirements. This device enables us to manage the DAQ system at up to 80 Ms/s and to execute intensive calculi over the detector signals. This paper describes our designed DAQ and processing architecture whose main features are: very high-speed data conversion, multichannel synchronized acquisition with zero dead time, a digital triggering scheme, and high throughput of data with an extensive optimization of the signal processing algorithms.

  12. Preterm EEG: a multimodal neurophysiological protocol.

    Science.gov (United States)

    Stjerna, Susanna; Voipio, Juha; Metsäranta, Marjo; Kaila, Kai; Vanhatalo, Sampsa

    2012-02-18

    Since its introduction in early 1950s, electroencephalography (EEG) has been widely used in the neonatal intensive care units (NICU) for assessment and monitoring of brain function in preterm and term babies. Most common indications are the diagnosis of epileptic seizures, assessment of brain maturity, and recovery from hypoxic-ischemic events. EEG recording techniques and the understanding of neonatal EEG signals have dramatically improved, but these advances have been slow to penetrate through the clinical traditions. The aim of this presentation is to bring theory and practice of advanced EEG recording available for neonatal units. In the theoretical part, we will present animations to illustrate how a preterm brain gives rise to spontaneous and evoked EEG activities, both of which are unique to this developmental phase, as well as crucial for a proper brain maturation. Recent animal work has shown that the structural brain development is clearly reflected in early EEG activity. Most important structures in this regard are the growing long range connections and the transient cortical structure, subplate. Sensory stimuli in a preterm baby will generate responses that are seen at a single trial level, and they have underpinnings in the subplate-cortex interaction. This brings neonatal EEG readily into a multimodal study, where EEG is not only recording cortical function, but it also tests subplate function via different sensory modalities. Finally, introduction of clinically suitable dense array EEG caps, as well as amplifiers capable of recording low frequencies, have disclosed multitude of brain activities that have as yet been overlooked. In the practical part of this video, we show how a multimodal, dense array EEG study is performed in neonatal intensive care unit from a preterm baby in the incubator. The video demonstrates preparation of the baby and incubator, application of the EEG cap, and performance of the sensory stimulations.

  13. Filtration of human EEG recordings from physiological artifacts with empirical mode method

    Science.gov (United States)

    Grubov, Vadim V.; Runnova, Anastasiya E.; Khramova, Marina V.

    2017-03-01

    In the paper we propose the new method for dealing with noise and physiological artifacts in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We consider noises and physiological artifacts on EEG as specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from eye-moving artifacts and show high efficiency of the method.

  14. Quantitative topographic differentiation of the neonatal EEG.

    Science.gov (United States)

    Paul, Karel; Krajca, Vladimír; Roth, Zdenek; Melichar, Jan; Petránek, Svojmil

    2006-09-01

    To test the discriminatory topographic potential of a new method of the automatic EEG analysis in neonates. A quantitative description of the neonatal EEG can contribute to the objective assessment of the functional state of the brain, and may improve the precision of diagnosing cerebral dysfunctions manifested by 'disorganization', 'dysrhythmia' or 'dysmaturity'. 21 healthy, full-term newborns were examined polygraphically during sleep (EEG-8 referential derivations, respiration, ECG, EOG, EMG). From each EEG record, two 5-min samples (one from the middle of quiet sleep, the other from the middle of active sleep) were subject to subsequent automatic analysis and were described by 13 variables: spectral features and features describing shape and variability of the signal. The data from individual infants were averaged and the number of variables was reduced by factor analysis. All factors identified by factor analysis were statistically significantly influenced by the location of derivation. A large number of statistically significant differences were also established when comparing the effects of individual derivations on each of the 13 measured variables. Both spectral features and features describing shape and variability of the signal are largely accountable for the topographic differentiation of the neonatal EEG. The presented method of the automatic EEG analysis is capable to assess the topographic characteristics of the neonatal EEG, and it is adequately sensitive and describes the neonatal electroencephalogram with sufficient precision. The discriminatory capability of the used method represents a promise for their application in the clinical practice.

  15. Spatio-temporal dynamics of multimodal EEG-fNIRS signals in the loss and recovery of consciousness under sedation using midazolam and propofol.

    Directory of Open Access Journals (Sweden)

    Seul-Ki Yeom

    Full Text Available On sedation motivated by the clinical needs for safety and reliability, recent studies have attempted to identify brain-specific signatures for tracking patient transition into and out of consciousness, but the differences in neurophysiological effects between 1 the sedative types and 2 the presence/absence of surgical stimulations still remain unclear. Here we used multimodal electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS measurements to observe electrical and hemodynamic responses during sedation simultaneously. Forty healthy volunteers were instructed to push the button to administer sedatives in response to auditory stimuli every 9-11 s. To generally illustrate brain activity at repetitive transition points at the loss of consciousness (LOC and the recovery of consciousness (ROC, patient-controlled sedation was performed using two different sedatives (midazolam (MDZ and propofol (PPF under two surgical conditions. Once consciousness was lost via sedatives, we observed gradually increasing EEG power at lower frequencies (15 Hz, as well as spatially increased EEG powers in the delta and lower alpha bands, and particularly also in the upper alpha rhythm, at the frontal and parieto-occipital areas over time. During ROC from unconsciousness, these spatio-temporal changes were reversed. Interestingly, the level of consciousness was switched on/off at significantly higher effect-site concentrations of sedatives in the brain according to the use of surgical stimuli, but the spatio-temporal EEG patterns were similar, regardless of the sedative used. We also observed sudden phase shifts in fronto-parietal connectivity at the LOC and the ROC as critical points. fNIRS measurement also revealed mild hemodynamic fluctuations. Compared with general anesthesia, our results provide insights into critical hallmarks of sedative-induced (unconsciousness, which have similar spatio-temporal EEG-fNIRS patterns regardless of the stage and

  16. Correlation between intra- and extracranial background EEG

    DEFF Research Database (Denmark)

    Duun-Henriksen, Jonas; Kjaer, Troels W.; Madsen, Rasmus E.

    2012-01-01

    Scalp EEG is the most widely used modality to record the electrical signals of the brain. It is well known that the volume conduction of these brain waves through the brain, cerebrospinal fluid, skull and scalp reduces the spatial resolution and the signal amplitude. So far the volume conduction...... has primarily been investigated by realistic head models or interictal spike analysis. We have set up a novel and more realistic experiment that made it possible to compare the information in the intra- and extracranial EEG. We found that intracranial EEG channels contained correlated patterns when...... placed less than 30 mm apart, that intra- and extracranial channels were partly correlated when placed less than 40 mm apart, and that extracranial channels probably were correlated over larger distances. The underlying cortical area that influences the extracranial EEG is found to be up to 45 cm2...

  17. β-Adrenergic Receptors Regulate the Acquisition and Consolidation Phases of Aversive Memory Formation Through Distinct, Temporally Regulated Signaling Pathways.

    Science.gov (United States)

    Schiff, Hillary C; Johansen, Joshua P; Hou, Mian; Bush, David E A; Smith, Emily K; Klein, JoAnna E; LeDoux, Joseph E; Sears, Robert M

    2017-03-01

    Memory formation requires the temporal coordination of molecular events and cellular processes following a learned event. During Pavlovian threat (fear) conditioning (PTC), sensory and neuromodulatory inputs converge on post-synaptic neurons within the lateral nucleus of the amygdala (LA). By activating an intracellular cascade of signaling molecules, these G-protein-coupled neuromodulatory receptors are capable of recruiting a diverse profile of plasticity-related proteins. Here we report that norepinephrine, through its actions on β-adrenergic receptors (βARs), modulates aversive memory formation following PTC through two molecularly and temporally distinct signaling mechanisms. Specifically, using behavioral pharmacology and biochemistry in adult rats, we determined that βAR activity during, but not after PTC training initiates the activation of two plasticity-related targets: AMPA receptors (AMPARs) for memory acquisition and short-term memory and extracellular regulated kinase (ERK) for consolidating the learned association into a long-term memory. These findings reveal that βAR activity during, but not following PTC sets in motion cascading molecular events for the acquisition (AMPARs) and subsequent consolidation (ERK) of learned associations.

  18. A Hygroscopic Sensor Electrode for Fast Stabilized Non-Contact ECG Signal Acquisition.

    Science.gov (United States)

    Fong, Ee-May; Chung, Wan-Young

    2015-08-05

    A capacitive electrocardiography (cECG) technique using a non-invasive ECG measuring technology that does not require direct contact between the sensor and the skin has attracted much interest. The system encounters several challenges when the sensor electrode and subject's skin are weakly coupled. Because there is no direct physical contact between the subject and any grounding point, there is no discharge path for the built-up electrostatic charge. Subsequently, the electrostatic charge build-up can temporarily contaminate the ECG signal from being clearly visible; a stabilization period (3-15 min) is required for the measurement of a clean, stable ECG signal at low humidity levels (below 55% relative humidity). Therefore, to obtain a clear ECG signal without noise and to reduce the ECG signal stabilization time to within 2 min in a dry ambient environment, we have developed a fabric electrode with embedded polymer (FEEP). The designed hygroscopic FEEP has an embedded superabsorbent polymer layer. The principle of FEEP as a conductive electrode is to provide humidity to the capacitive coupling to ensure strong coupling and to allow for the measurement of a stable, clear biomedical signal. The evaluation results show that hygroscopic FEEP is capable of rapidly measuring high-accuracy ECG signals with a higher SNR ratio.

  19. Meditation and the EEG

    OpenAIRE

    West, Michael

    1980-01-01

    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...

  20. Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

    NARCIS (Netherlands)

    Caat, Michael ten; Maurits, Natasha M.; Roerdink, Jos B.T.M.

    2008-01-01

    A typical data- driven visualization of electroencephalography ( EEG) coherence is a graph layout, with vertices representing electrodes and edges representing significant coherences between electrode signals. A drawback of this layout is its visual clutter for multichannel EEG. To reduce clutter,

  1. Mobile EEG in epilepsy

    NARCIS (Netherlands)

    Askamp, Jessica; van Putten, Michel Johannes Antonius Maria

    2014-01-01

    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

  2. BioDAQ--a simple biosignal acquisition system for didactic use.

    Science.gov (United States)

    Csaky, Z; Mihalas, G I; Focsa, M

    2002-01-01

    A simple non expensive device for biosignal acquisition is presented. It mainly meets the requirements for didactic purposes specific in medical informatics laboratory classes. The system has two main types of devices: 'student unit'--the simplest one, used during lessons on real signals and 'demo unit', which can be also used in medical practice or for collecting biological signals. It is able to record: optical pulse, sphygmogram, ECG (1-4 leads) EEG or EMG (1-4 channels). For didactical purposes it has a large scale of recording options: variable sampling rate, gain and filtering. It can also be used in tele-acquisition via Internet.

  3. Improved data acquisition methods for uninterrupted signal monitoring and ultra-fast plasma diagnostics in LHD

    International Nuclear Information System (INIS)

    Nakanishi, Hideya; Imazu, Setsuo; Ohsuna, Masaki

    2012-01-01

    To deal with endless data streams acquired in LHD steady-state experiments, the LHD data acquisition system was designed with a simple concept that divides a long pulse into a consecutive series of 10-s “subshots”. Latest digitizers applying high-speed PCI-Express technology, however, output nonstop gigabyte per second data streams whose subshot intervals would be extremely long if 10-s rule was applied. These digitizers need shorter subshot intervals, less than 10-s long. In contrast, steady-state fusion plants need uninterrupted monitoring of the environment and device soundness. They adopt longer subshot lengths of either 10 min or 1 day. To cope with both uninterrupted monitoring and ultra-fast diagnostics, the ability to vary the subshot length according to the type of operation is required. In this study, a design modification that enables variable subshot lengths was implemented and its practical effectiveness in LHD was verified. (author)

  4. FPGA-based GEM detector signal acquisition for SXR spectroscopy system

    Science.gov (United States)

    Wojenski, A.; Pozniak, K. T.; Kasprowicz, G.; Kolasinski, P.; Krawczyk, R.; Zabolotny, W.; Chernyshova, M.; Czarski, T.; Malinowski, K.

    2016-11-01

    The presented work is related to the Gas Electron Multiplier (GEM) detector soft X-ray spectroscopy system for tokamak applications. The used GEM detector has one-dimensional, 128 channel readout structure. The channels are connected to the radiation-hard electronics with configurable analog stage and fast ADCs, supporting speeds of 125 MSPS for each channel. The digitalized data is sent directly to the FPGAs using fast serial links. The preprocessing algorithms are implemented in the FPGAs, with the data buffering made in the on-board 2Gb DDR3 memory chips. After the algorithmic stage, the data is sent to the Intel Xeon-based PC for further postprocessing using PCI-Express link Gen 2. For connection of multiple FPGAs, PCI-Express switch 8-to-1 was designed. The whole system can support up to 2048 analog channels. The scope of the work is an FPGA-based implementation of the recorder of the raw signal from GEM detector. Since the system will work in a very challenging environment (neutron radiation, intense electro-magnetic fields), the registered signals from the GEM detector can be corrupted. In the case of the very intense hot plasma radiation (e.g. laser generated plasma), the registered signals can overlap. Therefore, it is valuable to register the raw signals from the GEM detector with high number of events during soft X-ray radiation. The signal analysis will have the direct impact on the implementation of photon energy computation algorithms. As the result, the system will produce energy spectra and topological distribution of soft X-ray radiation. The advanced software was developed in order to perform complex system startup and monitoring of hardware units. Using the array of two one-dimensional GEM detectors it will be possible to perform tomographic reconstruction of plasma impurities radiation in the SXR region.

  5. SignalPlant: an open signal processing software platform.

    Science.gov (United States)

    Plesinger, F; Jurco, J; Halamek, J; Jurak, P

    2016-07-01

    The growing technical standard of acquisition systems allows the acquisition of large records, often reaching gigabytes or more in size as is the case with whole-day electroencephalograph (EEG) recordings, for example. Although current 64-bit software for signal processing is able to process (e.g. filter, analyze, etc) such data, visual inspection and labeling will probably suffer from rather long latency during the rendering of large portions of recorded signals. For this reason, we have developed SignalPlant-a stand-alone application for signal inspection, labeling and processing. The main motivation was to supply investigators with a tool allowing fast and interactive work with large multichannel records produced by EEG, electrocardiograph and similar devices. The rendering latency was compared with EEGLAB and proves significantly faster when displaying an image from a large number of samples (e.g. 163-times faster for 75  ×  10(6) samples). The presented SignalPlant software is available free and does not depend on any other computation software. Furthermore, it can be extended with plugins by third parties ensuring its adaptability to future research tasks and new data formats.

  6. Passive in-vehicle driver breath alcohol detection using advanced sensor signal acquisition and fusion.

    Science.gov (United States)

    Ljungblad, Jonas; Hök, Bertil; Allalou, Amin; Pettersson, Håkan

    2017-05-29

    The research objective of the present investigation is to demonstrate the present status of passive in-vehicle driver breath alcohol detection and highlight the necessary conditions for large-scale implementation of such a system. Completely passive detection has remained a challenge mainly because of the requirements on signal resolution combined with the constraints of vehicle integration. The work is part of the Driver Alcohol Detection System for Safety (DADSS) program aiming at massive deployment of alcohol sensing systems that could potentially save thousands of American lives annually. The work reported here builds on earlier investigations, in which it has been shown that detection of alcohol vapor in the proximity of a human subject may be traced to that subject by means of simultaneous recording of carbon dioxide (CO 2 ) at the same location. Sensors based on infrared spectroscopy were developed to detect and quantify low concentrations of alcohol and CO 2 . In the present investigation, alcohol and CO 2 were recorded at various locations in a vehicle cabin while human subjects were performing normal in-step procedures and driving preparations. A video camera directed to the driver position was recording images of the driver's upper body parts, including the face, and the images were analyzed with respect to features of significance to the breathing behavior and breath detection, such as mouth opening and head direction. Improvement of the sensor system with respect to signal resolution including algorithm and software development, and fusion of the sensor and camera signals was successfully implemented and tested before starting the human study. In addition, experimental tests and simulations were performed with the purpose of connecting human subject data with repeatable experimental conditions. The results include occurrence statistics of detected breaths by signal peaks of CO 2 and alcohol. From the statistical data, the accuracy of breath alcohol

  7. Two channel EEG thought pattern classifier.

    Science.gov (United States)

    Craig, D A; Nguyen, H T; Burchey, H A

    2006-01-01

    This paper presents a real-time electro-encephalogram (EEG) identification system with the goal of achieving hands free control. With two EEG electrodes placed on the scalp of the user, EEG signals are amplified and digitised directly using a ProComp+ encoder and transferred to the host computer through the RS232 interface. Using a real-time multilayer neural network, the actual classification for the control of a powered wheelchair has a very fast response. It can detect changes in the user's thought pattern in 1 second. Using only two EEG electrodes at positions O(1) and C(4) the system can classify three mental commands (forward, left and right) with an accuracy of more than 79 %

  8. Novel artefact removal algorithms for co-registered EEG/fMRI based on selective averaging and subtraction.

    Science.gov (United States)

    de Munck, Jan C; van Houdt, Petra J; Gonçalves, Sónia I; van Wegen, Erwin; Ossenblok, Pauly P W

    2013-01-01

    Co-registered EEG and functional MRI (EEG/fMRI) is a potential clinical tool for planning invasive EEG in patients with epilepsy. In addition, the analysis of EEG/fMRI data provides a fundamental insight into the precise physiological meaning of both fMRI and EEG data. Routine application of EEG/fMRI for localization of epileptic sources is hampered by large artefacts in the EEG, caused by switching of scanner gradients and heartbeat effects. Residuals of the ballistocardiogram (BCG) artefacts are similarly shaped as epileptic spikes, and may therefore cause false identification of spikes. In this study, new ideas and methods are presented to remove gradient artefacts and to reduce BCG artefacts of different shapes that mutually overlap in time. Gradient artefacts can be removed efficiently by subtracting an average artefact template when the EEG sampling frequency and EEG low-pass filtering are sufficient in relation to MR gradient switching (Gonçalves et al., 2007). When this is not the case, the gradient artefacts repeat themselves at time intervals that depend on the remainder between the fMRI repetition time and the closest multiple of the EEG acquisition time. These repetitions are deterministic, but difficult to predict due to the limited precision by which these timings are known. Therefore, we propose to estimate gradient artefact repetitions using a clustering algorithm, combined with selective averaging. Clustering of the gradient artefacts yields cleaner EEG for data recorded during scanning of a 3T scanner when using a sampling frequency of 2048 Hz. It even gives clean EEG when the EEG is sampled with only 256 Hz. Current BCG artefacts-reduction algorithms based on average template subtraction have the intrinsic limitation that they fail to deal properly with artefacts that overlap in time. To eliminate this constraint, the precise timings of artefact overlaps were modelled and represented in a sparse matrix. Next, the artefacts were disentangled with

  9. Low power laser generated ultrasound: Signal processing for time domain data acquisition

    International Nuclear Information System (INIS)

    Cleary, A; Thursby, G; McKee, C; Armstrong, I; Culshaw, B; Veres, I; Pierce, S G

    2011-01-01

    The use of low power modulated laser diode systems has previously been established as a suitable method for non-destructive laser generation of ultrasound. Using a quasi-continuous optical excitation amplified by an erbium-doped fibre amplifier (EDFA) allows flexible generation of ultrasonic waves, offering control of further parameters such as the frequency content or signal shape. In addition, pseudo-random binary sequences (PRBS) can be used to improve the detected impulse response. Here we compare two sequences, the m-sequence and the Golay code, and discuss the advantages and practical limits of their application with laser diode based optical excitation of ultrasound.

  10. Effects of an acute therapeutic or rewarding dose of amphetamine on acquisition of Pavlovian autoshaping and ventral striatal dopamine signaling.

    Science.gov (United States)

    Schuweiler, D R; Athens, J M; Thompson, J M; Vazhayil, S T; Garris, P A

    2018-01-15

    Rewarding doses of amphetamine increase the amplitude, duration, and frequency of dopamine transients in the ventral striatum. Debate continues at the behavioral level about which component of reward, learning or incentive salience, is signaled by these dopamine transients and thus altered in addiction. The learning hypothesis proposes that rewarding drugs result in pathological overlearning of drug-predictive cues, while the incentive sensitization hypothesis suggests that rewarding drugs result in sensitized attribution of incentive salience to drug-predictive cues. Therapeutic doses of amphetamine, such as those used to treat attention-deficit hyperactivity disorder, are hypothesized to enhance the ventral striatal dopamine transients that are critical for reward-related learning and to enhance Pavlovian learning. However, the effects of therapeutic doses of amphetamine on Pavlovian learning are poorly understood, and the effects on dopamine transients are completely unknown. We determined the effects of an acute pre-training therapeutic or rewarding amphetamine injection on the acquisition of Pavlovian autoshaping in the intact rat. We also determined the effects of these doses on electrically evoked transient-like dopamine signals using fast-scan cyclic voltammetry in the anesthetized rat. The rewarding dose enhanced the amplitude and duration of DA signals, caused acute task disengagement, impaired learning for several days, and triggered incentive sensitization. The therapeutic dose produced smaller enhancements in DA signals but did not have similar behavioral effects. These results underscore the necessity of more studies using therapeutic doses, and suggest a hybrid learning/incentive sensitization model may be required to explain the development of addiction. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Methods of measurement signal acquisition from the rotational flow meter for frequency analysis

    Directory of Open Access Journals (Sweden)

    Świsulski Dariusz

    2017-01-01

    Full Text Available One of the simplest and commonly used instruments for measuring the flow of homogeneous substances is the rotational flow meter. The main part of such a device is a rotor (vane or screw rotating at a speed which is the function of the fluid or gas flow rate. A pulse signal with a frequency proportional to the speed of the rotor is obtained at the sensor output. For measurements in dynamic conditions, a variable interval between pulses prohibits the analysis of the measuring signal. Therefore, the authors of the article developed a method involving the determination of measured values on the basis of the last inter-pulse interval preceding the moment designated by the timing generator. For larger changes of the measured value at a predetermined time, the value can be determined by means of extrapolation of the two adjacent interpulse ranges, assuming a linear change in the flow. The proposed methods allow analysis which requires constant spacing between measurements, allowing for an analysis of the dynamics of changes in the test flow, eg. using a Fourier transform. To present the advantages of these methods simulations of flow measurement were carried out with a DRH-1140 rotor flow meter from the company Kobold.

  12. Development of Hand Grip Assistive Device Control System for Old People through Electromyography (EMG Signal Acquisitions

    Directory of Open Access Journals (Sweden)

    Khamis Herman

    2017-01-01

    Full Text Available The hand grip assistive device is a glove to assist old people who suffer from hand weakness in their daily life activities. The device earlier control system only use simple on and off switch. This required old people to use both hand to activate the device. The new control system of the hand grip assistive device was developed to allow single hand operation for old people. New control system take advantages of electromyography (EMG and flex sensor which was implemented to the device. It was programmed into active and semi-active mode operation. EMG sensors were placed on the forearm to capture EMG signal of Flexor Digitorum Profundus muscle to activate the device. Flex sensor was used to indicate the finger position and placed on top of the finger. The signal from both sensors then used to control the device. The new control system allowed single hand operation and designed to prevent user from over depended on the device by activating it through moving their fingers.

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

    Science.gov (United States)

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

    2012-04-01

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

  14. Prevalence and etiology of false normal aEEG recordings in neonatal hypoxic-ischaemic encephalopathy

    OpenAIRE

    Marics, Gábor; Csekő, Anna; Vásárhelyi, Barna; Zakariás, Dávid; Schuster, György; Szabó, Miklós

    2013-01-01

    Background Amplitude-integrated electroencephalography (aEEG) is a useful tool to determine the severity of neonatal hypoxic-ischemic encephalopathy (HIE). Our aim was to assess the prevalence and study the origin of false normal aEEG recordings based on 85 aEEG recordings registered before six hours of age. Methods Raw EEG recordings were reevaluated retrospectively with Fourier analysis to identify and describe the frequency patterns of the raw EEG signal, in cases with inconsistent aEEG re...

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

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Hansen, Lars Kai

    2016-01-01

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

  16. Convolutive ICA for Spatio-Temporal Analysis of EEG

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai

    2007-01-01

    in the convolutive model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving an EEG ICA subspace. Initial results suggest that in some cases convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model....

  17. EOG Artifacts Removal in EEG Measurements for Affective Interaction

    NARCIS (Netherlands)

    Qi, Wen

    2014-01-01

    A brain-computer interface (BCI) is a direct link between the brain and a computer. Multi-modal input with BCI forms a promising solution for creating rich gaming experience. Electroencephalography (EEG) measurement is the sole necessary component for a BCI system. EEG signals have the

  18. EEG and Coma.

    Science.gov (United States)

    Ardeshna, Nikesh I

    2016-03-01

    Coma is defined as a state of extreme unresponsiveness, in which a person exhibits no voluntary movement or behavior even to painful stimuli. The utilization of EEG for patients in coma has increased dramatically over the last few years. In fact, many institutions have set protocols for continuous EEG (cEEG) monitoring for patients in coma due to potential causes such as subarachnoid hemorrhage or cardiac arrest. Consequently, EEG plays an important role in diagnosis, managenent, and in some cases even prognosis of coma patients.

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  20. Data acquisition and real time signal processing of plasma diagnostics on ASDEX Upgrade using LabVIEW RT

    Energy Technology Data Exchange (ETDEWEB)

    Giannone, L.; Scarabosio, A.; Eich, T.; Fuchs, C.; Haas, G.; Kallenbach, A.; McCarthy, P.; Mlynek, A.; Neu, G.; Reich, M.; Schneider, W.; Schuhbeck, K.; Treutterer, W.; Zehetbauer, T.; Asdex, Upgrade Team [Max-Planck-Institut fur Plasmaphysik, Garching (Germany); Cerna, M.; Wenzel, L.; Concezzi, S.; Debelle, T.; Marker, B.; Munroe, M.; Petersen, N.; Vrancic, A. [National Instruments Corporation, Austin (United States); Marquardt, M.; Sachtleben, J. [Max-Planck-Institute for Plasmaphysics, Teilinstitut Greifswald (Germany)

    2009-07-01

    There are 5 plasma diagnostics using LabVIEW RT for data acquisition and control on ASDEX Upgrade. These diagnostics are integrated into the VxWorks control system by the exchange of XML files. Real time communication to the control system is possible by Ethernet using UDP or by reflective memory using a dedicated fiber optic cable. The bolometer and manometer data acquisition systems are described, they use FPGA cards to process raw data in real time. The absorbed power of the bolometer foil is calculated in real time on the FPGA. The radiation peaking factor is also calculated in real time and is used for feedback control of the discharge. The manometer uses 8 analog inputs and 4 analog outputs of a FPGA card to provide PID control of the electron current emission of a filament. The electron and ion currents are acquired at 750 kHz and the neutral gas pressures of 4 manometers are calculated in real time on a FPGA card at up to 10 kHz. The magnetic equilibrium diagnostic acquires 80 magnetic probe and flux loop signals at 10 kHz. The 95 plasma position and shape parameters and magnetic flux surfaces are calculated in real time. The function parameterization algorithm used to calculate the magnetic flux surfaces in real time requires the multiplication of a matrix of dimension 2691*231 with a vector of length 231. This matrix and vector multiplication is solved through parallel computing on a dual quad-core computer and the execution time of this operation is reduced by a factor of four compared to calculation on a single core. This document is composed of an abstract followed by a poster. (authors)

  1. Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF).

    Science.gov (United States)

    Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R

    2018-01-01

    Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3-45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25-63% improvement), and VEPs variability (16-44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.

  2. EEG in the classroom: Synchronised neural recordings during video presentation

    DEFF Research Database (Denmark)

    Poulsen, Andreas Trier; Kamronn, Simon Due; Dmochowski, Jacek

    2017-01-01

    We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a common video stimulus. The neural reliability, as quantified by ISC, has been linked to engagement and attentional......-evoked neural responses, known to be modulated by attention, can be tracked for groups of students with synchronized EEG acquisition. This is a step towards real-time inference of engagement in the classroom....

  3. Connectivity Measures in EEG Microstructural Sleep Elements.

    Science.gov (United States)

    Sakellariou, Dimitris; Koupparis, Andreas M; Kokkinos, Vasileios; Koutroumanidis, Michalis; Kostopoulos, George K

    2016-01-01

    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 characterize 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 occurrence

  4. EEG: Origin and measurement

    NARCIS (Netherlands)

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

    2010-01-01

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

  5. Independent EEG sources are dipolar.

    Directory of Open Access Journals (Sweden)

    Arnaud Delorme

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

  6. Detection of EEG electrodes in brain volumes.

    Science.gov (United States)

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

    2010-01-01

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

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Anna Elisabetta eVaudano

    2013-11-01

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

  9. Random matrix analysis of human EEG data

    Czech Academy of Sciences Publication Activity Database

    Šeba, Petr

    2003-01-01

    Roč. 91, - (2003), s. 198104-1 - 198104-4 ISSN 0031-9007 R&D Projects: GA ČR GA202/02/0088 Institutional research plan: CEZ:AV0Z1010914 Keywords : random matrix theory * EEG signal Subject RIV: BE - Theoretical Physics Impact factor: 7.035, year: 2003

  10. A statistically robust EEG re-referencing procedure to mitigate reference effect

    OpenAIRE

    Lepage, Kyle Q.; Kramer, Mark A.; Chu, Catherine J.

    2014-01-01

    Background: The electroencephalogram (EEG) remains the primary tool for diagnosis of abnormal brain activity in clinical neurology and for in vivo recordings of human neurophysiology in neuroscience research. In EEG data acquisition, voltage is measured at positions on the scalp with respect to a reference electrode. When this reference electrode responds to electrical activity or artifact all electrodes are affected. Successful analysis of EEG data often involves re-referencing procedures th...

  11. Age-related Multiscale Changes in Brain Signal Variability in Pre-task versus Post-task Resting-state EEG.

    Science.gov (United States)

    Wang, Hongye; McIntosh, Anthony R; Kovacevic, Natasa; Karachalios, Maria; Protzner, Andrea B

    2016-07-01

    Recent empirical work suggests that, during healthy aging, the variability of network dynamics changes during task performance. Such variability appears to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into resting-state dynamics. We recorded EEG in young, middle-aged, and older adults during a "rest-task-rest" design and investigated if aging modifies the interaction between resting-state activity and external stimulus-induced activity. Using multiscale entropy as our measure of variability, we found that, with increasing age, resting-state dynamics shifts from distributed to more local neural processing, especially at posterior sources. In the young group, resting-state dynamics also changed from pre- to post-task, where fine-scale entropy increased in task-positive regions and coarse-scale entropy increased in the posterior cingulate, a key region associated with the default mode network. Lastly, pre- and post-task resting-state dynamics were linked to performance on the intervening task for all age groups, but this relationship became weaker with increasing age. Our results suggest that age-related changes in resting-state dynamics occur across different spatial and temporal scales and have consequences for information processing capacity.

  12. Investigating social cognition in infants and adults using dense array electroencephalography ((d)EEG).

    Science.gov (United States)

    Akano, Adekemi J; Haley, David W; Dudek, Joanna

    2011-06-27

    pedestal, which, when filled with electrolyte solution, creates a sealed microenvironment and an electrode-scalp interface. In all Geodesic (d;)EEG systems, EEG sensors detect changes in voltage originating from the participant's scalp, along with a small amount of electrical noise originating from the room environment. Electrical signals from all sensors of the Geodesic sensor net are received simultaneously by the amplifier, where they are automatically processed, packaged, and sent to the data-acquisition computer (DAC). Once received by the DAC, scalp electrical activity can be isolated from artifacts for analysis using the filtering and artifact detection tools included in the EGI software. Typically, the HCGSN can be used continuously for only up to two hours because the electrolyte solution dries out over time, gradually decreasing the quality of the scalp-electrode interface. In the Parent-Infant Research Lab at the University of Toronto, we are using (d)EEG to study social cognitive processes including memory, emotion, goals, intentionality, anticipation, and executive functioning in both adult and infant participants.

  13. Modulation of 7 T fMRI Signal in the Cerebellar Cortex and Nuclei During Acquisition, Extinction, and Reacquisition of Conditioned Eyeblink Responses.

    Science.gov (United States)

    Ernst, Thomas M; Thürling, Markus; Müller, Sarah; Kahl, Fabian; Maderwald, Stefan; Schlamann, Marc; Boele, Henk-Jan; Koekkoek, Sebastiaan K E; Diedrichsen, Jörn; De Zeeuw, Chris I; Ladd, Mark E; Timmann, Dagmar

    2017-08-01

    Classical delay eyeblink conditioning is likely the most commonly used paradigm to study cerebellar learning. As yet, few studies have focused on extinction and savings of conditioned eyeblink responses (CRs). Saving effects, which are reflected in a reacquisition after extinction that is faster than the initial acquisition, suggest that learned associations are at least partly preserved during extinction. In this study, we tested the hypothesis that acquisition-related plasticity is nihilated during extinction in the cerebellar cortex, but retained in the cerebellar nuclei, allowing for faster reacquisition. Changes of 7 T functional magnetic resonance imaging (fMRI) signals were investigated in the cerebellar cortex and nuclei of young and healthy human subjects. Main effects of acquisition, extinction, and reacquisition against rest were calculated in conditioned stimulus-only trials. First-level β values were determined for a spherical region of interest (ROI) around the acquisition peak voxel in lobule VI, and dentate and interposed nuclei ipsilateral to the unconditioned stimulus. In the cerebellar cortex and nuclei, fMRI signals were significantly lower in extinction compared to acquisition and reacquisition, but not significantly different between acquisition and reacquisition. These findings are consistent with the theory of bidirectional learning in both the cerebellar cortex and nuclei. It cannot explain, however, why conditioned responses reappear almost immediately in reacquisition following extinction. Although the present data do not exclude that part of the initial memory remains in the cerebellum in extinction, future studies should also explore changes in extracerebellar regions as a potential substrate of saving effects. Hum Brain Mapp 38:3957-3974, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  14. Hypoxia and bicarbonate could limit the expression of iron acquisition genes in Strategy I plants by affecting ethylene synthesis and signaling in different ways.

    Science.gov (United States)

    García, María J; García-Mateo, María J; Lucena, Carlos; Romera, Francisco J; Rojas, Carmen L; Alcántara, Esteban; Pérez-Vicente, Rafael

    2014-01-01

    In a previous work, it was shown that bicarbonate (one of the most important factors causing Fe chlorosis in Strategy I plants) can limit the expression of several genes involved in Fe acquisition. Hypoxia is considered another important factor causing Fe chlorosis, mainly on calcareous soils. However, to date it is not known whether hypoxia aggravates Fe chlorosis by affecting bicarbonate concentration or by specific negative effects on Fe acquisition. Results found in this work show that hypoxia, generated by eliminating the aeration of the nutrient solution, can limit the expression of several Fe acquisition genes in Fe-deficient Arabidopsis, cucumber and pea plants, like the genes for ferric reductases AtFRO2, PsFRO1 and CsFRO1; iron transporters AtIRT1, PsRIT1 and CsIRT1; H(+) -ATPase CsHA1; and transcription factors AtFIT, AtbHLH38, and AtbHLH39. Interestingly, the limitation of the expression of Fe-acquisition genes by hypoxia did not occur in the Arabidopsis ethylene constitutive mutant ctr1, which suggests that the negative effect of hypoxia is related to ethylene, an hormone involved in the upregulation of Fe acquisition genes. As for hypoxia, results obtained by applying bicarbonate to the nutrient solution suggests that ethylene is also involved in its negative effect, since ACC (1-aminocyclopropane-1-carboxylic acid; ethylene precursor) partially reversed the negative effect of bicarbonate on the expression of Fe acquisition genes. Taken together, the results obtained show that hypoxia and bicarbonate could induce Fe chlorosis by limiting the expression of Fe acquisition genes, probably because each factor negatively affects different steps of ethylene synthesis and/or signaling. © 2013 Scandinavian Plant Physiology Society.

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

    Science.gov (United States)

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

    2012-05-01

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

  16. Highly Efficient Compression Algorithms for Multichannel EEG.

    Science.gov (United States)

    Shaw, Laxmi; Rahman, Daleef; Routray, Aurobinda

    2018-05-01

    The difficulty associated with processing and understanding the high dimensionality of electroencephalogram (EEG) data requires developing efficient and robust compression algorithms. In this paper, different lossless compression techniques of single and multichannel EEG data, including Huffman coding, arithmetic coding, Markov predictor, linear predictor, context-based error modeling, multivariate autoregression (MVAR), and a low complexity bivariate model have been examined and their performances have been compared. Furthermore, a high compression algorithm named general MVAR and a modified context-based error modeling for multichannel EEG have been proposed. The resulting compression algorithm produces a higher relative compression ratio of 70.64% on average compared with the existing methods, and in some cases, it goes up to 83.06%. The proposed methods are designed to compress a large amount of multichannel EEG data efficiently so that the data storage and transmission bandwidth can be effectively used. These methods have been validated using several experimental multichannel EEG recordings of different subjects and publicly available standard databases. The satisfactory parametric measures of these methods, namely percent-root-mean square distortion, peak signal-to-noise ratio, root-mean-square error, and cross correlation, show their superiority over the state-of-the-art compression methods.

  17. EEG feature selection method based on decision tree.

    Science.gov (United States)

    Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

    2015-01-01

    This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

  18. Automated approach to detecting behavioral states using EEG-DABS

    Directory of Open Access Journals (Sweden)

    Zachary B. Loris

    2017-07-01

    Full Text Available Electrocorticographic (ECoG signals represent cortical electrical dipoles generated by synchronous local field potentials that result from simultaneous firing of neurons at distinct frequencies (brain waves. Since different brain waves correlate to different behavioral states, ECoG signals presents a novel strategy to detect complex behaviors. We developed a program, EEG Detection Analysis for Behavioral States (EEG-DABS that advances Fast Fourier Transforms through ECoG signals time series, separating it into (user defined frequency bands and normalizes them to reduce variability. EEG-DABS determines events if segments of an experimental ECoG record have significantly different power bands than a selected control pattern of EEG. Events are identified at every epoch and frequency band and then are displayed as output graphs by the program. Certain patterns of events correspond to specific behaviors. Once a predetermined pattern was selected for a behavioral state, EEG-DABS correctly identified the desired behavioral event. The selection of frequency band combinations for detection of the behavior affects accuracy of the method. All instances of certain behaviors, such as freezing, were correctly identified from the event patterns generated with EEG-DABS. Detecting behaviors is typically achieved by visually discerning unique animal phenotypes, a process that is time consuming, unreliable, and subjective. EEG-DABS removes variability by using defined parameters of EEG/ECoG for a desired behavior over chronic recordings. EEG-DABS presents a simple and automated approach to quantify different behavioral states from ECoG signals.

  19. EEG as an Indicator of Cerebral Functioning in Postanoxic Coma.

    Science.gov (United States)

    Juan, Elsa; Kaplan, Peter W; Oddo, Mauro; Rossetti, Andrea O

    2015-12-01

    Postanoxic coma after cardiac arrest is one of the most serious acute cerebral conditions and a frequent cause of admission to critical care units. Given substantial improvement of outcome over the recent years, a reliable and timely assessment of clinical evolution and prognosis is essential in this context, but may be challenging. In addition to the classic neurologic examination, EEG is increasingly emerging as an important tool to assess cerebral functions noninvasively. Although targeted temperature management and related sedation may delay clinical assessment, EEG provides accurate prognostic information in the early phase of coma. Here, the most frequently encountered EEG patterns in postanoxic coma are summarized and their relations with outcome prediction are discussed. This article also addresses the influence of targeted temperature management on brain signals and the implication of the evolution of EEG patterns over time. Finally, the article ends with a view of the future prospects for EEG in postanoxic management and prognostication.

  20. On the Keyhole Hypothesis: High Mutual Information between Ear and Scalp EEG

    Directory of Open Access Journals (Sweden)

    Kaare B. Mikkelsen

    2017-06-01

    Full Text Available We propose and test the keyhole hypothesis—that measurements from low dimensional EEG, such as ear-EEG reflect a broadly distributed set of neural processes. We formulate the keyhole hypothesis in information theoretical terms. The experimental investigation is based on legacy data consisting of 10 subjects exposed to a battery of stimuli, including alpha-attenuation, auditory onset, and mismatch-negativity responses and a new medium-long EEG experiment involving data acquisition during 13 h. Linear models were estimated to lower bound the scalp-to-ear capacity, i.e., predicting ear-EEG data from simultaneously recorded scalp EEG. A cross-validation procedure was employed to ensure unbiased estimates. We present several pieces of evidence in support of the keyhole hypothesis: There is a high mutual information between data acquired at scalp electrodes and through the ear-EEG “keyhole,” furthermore we show that the view—represented as a linear mapping—is stable across both time and mental states. Specifically, we find that ear-EEG data can be predicted reliably from scalp EEG. We also address the reverse view, and demonstrate that large portions of the scalp EEG can be predicted from ear-EEG, with the highest predictability achieved in the temporal regions and when using ear-EEG electrodes with a common reference electrode.

  1. Standardized Computer-based Organized Reporting of EEG: SCORE

    Science.gov (United States)

    Beniczky, Sándor; Aurlien, Harald; Brøgger, Jan C; Fuglsang-Frederiksen, Anders; Martins-da-Silva, António; Trinka, Eugen; Visser, Gerhard; Rubboli, Guido; Hjalgrim, Helle; Stefan, Hermann; Rosén, Ingmar; Zarubova, Jana; Dobesberger, Judith; Alving, Jørgen; Andersen, Kjeld V; Fabricius, Martin; Atkins, Mary D; Neufeld, Miri; Plouin, Perrine; Marusic, Petr; Pressler, Ronit; Mameniskiene, Ruta; Hopfengärtner, Rüdiger; Emde Boas, Walter; Wolf, Peter

    2013-01-01

    The electroencephalography (EEG) signal has a high complexity, and the process of extracting clinically relevant features is achieved by visual analysis of the recordings. The interobserver agreement in EEG interpretation is only moderate. This is partly due to the method of reporting the findings in free-text format. The purpose of our endeavor was to create a computer-based system for EEG assessment and reporting, where the physicians would construct the reports by choosing from predefined elements for each relevant EEG feature, as well as the clinical phenomena (for video-EEG recordings). A working group of EEG experts took part in consensus workshops in Dianalund, Denmark, in 2010 and 2011. The faculty was approved by the Commission on European Affairs of the International League Against Epilepsy (ILAE). The working group produced a consensus proposal that went through a pan-European review process, organized by the European Chapter of the International Federation of Clinical Neurophysiology. The Standardised Computer-based Organised Reporting of EEG (SCORE) software was constructed based on the terms and features of the consensus statement and it was tested in the clinical practice. The main elements of SCORE are the following: personal data of the patient, referral data, recording conditions, modulators, background activity, drowsiness and sleep, interictal findings, “episodes” (clinical or subclinical events), physiologic patterns, patterns of uncertain significance, artifacts, polygraphic channels, and diagnostic significance. The following specific aspects of the neonatal EEGs are scored: alertness, temporal organization, and spatial organization. For each EEG finding, relevant features are scored using predefined terms. Definitions are provided for all EEG terms and features. SCORE can potentially improve the quality of EEG assessment and reporting; it will help incorporate the results of computer-assisted analysis into the report, it will make

  2. EEG entropy measures in anesthesia

    Directory of Open Access Journals (Sweden)

    Zhenhu eLiang

    2015-02-01

    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.

  3. On some problems encountered in calculating the correlation dimension of EEG

    International Nuclear Information System (INIS)

    Dvorak, I.; Siska, J.

    1986-06-01

    Results of calculations of correlation dimension of the human EEG are presented. Effects of proband's mental activity, of the length of scrutinized signal and of the locus of registration on the computed values are studied. Evidence is given for a deterministic component in the EEG signal. (author)

  4. FFT transformed quantitative EEG analysis of short term memory load.

    Science.gov (United States)

    Singh, Yogesh; Singh, Jayvardhan; Sharma, Ratna; Talwar, Anjana

    2015-07-01

    The EEG is considered as building block of functional signaling in the brain. The role of EEG oscillations in human information processing has been intensively investigated. To study the quantitative EEG correlates of short term memory load as assessed through Sternberg memory test. The study was conducted on 34 healthy male student volunteers. The intervention consisted of Sternberg memory test, which runs on a version of the Sternberg memory scanning paradigm software on a computer. Electroencephalography (EEG) was recorded from 19 scalp locations according to 10-20 international system of electrode placement. EEG signals were analyzed offline. To overcome the problems of fixed band system, individual alpha frequency (IAF) based frequency band selection method was adopted. The outcome measures were FFT transformed absolute powers in the six bands at 19 electrode positions. Sternberg memory test served as model of short term memory load. Correlation analysis of EEG during memory task was reflected as decreased absolute power in Upper alpha band in nearly all the electrode positions; increased power in Theta band at Fronto-Temporal region and Lower 1 alpha band at Fronto-Central region. Lower 2 alpha, Beta and Gamma band power remained unchanged. Short term memory load has distinct electroencephalographic correlates resembling the mentally stressed state. This is evident from decreased power in Upper alpha band (corresponding to Alpha band of traditional EEG system) which is representative band of relaxed mental state. Fronto-temporal Theta power changes may reflect the encoding and execution of memory task.

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

    Science.gov (United States)

    2015-10-01

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

  6. Neural activity based biofeedback therapy for Autism spectrum disorder through wearable wireless textile EEG monitoring system

    Science.gov (United States)

    Sahi, Ahna; Rai, Pratyush; Oh, Sechang; Ramasamy, Mouli; Harbaugh, Robert E.; Varadan, Vijay K.

    2014-04-01

    Mu waves, also known as mu rhythms, comb or wicket rhythms are synchronized patterns of electrical activity involving large numbers of neurons, in the part of the brain that controls voluntary functions. Controlling, manipulating, or gaining greater awareness of these functions can be done through the process of Biofeedback. Biofeedback is a process that enables an individual to learn how to change voluntary movements for purposes of improving health and performance through the means of instruments such as EEG which rapidly and accurately 'feedback' information to the user. Biofeedback is used for therapeutic purpose for Autism Spectrum Disorder (ASD) by focusing on Mu waves for detecting anomalies in brain wave patterns of mirror neurons. Conventional EEG measurement systems use gel based gold cup electrodes, attached to the scalp with adhesive. It is obtrusive and wires sticking out of the electrodes to signal acquisition system make them impractical for use in sensitive subjects like infants and children with ASD. To remedy this, sensors can be incorporated with skull cap and baseball cap that are commonly used for infants and children. Feasibility of Textile based Sensor system has been investigated here. Textile based multi-electrode EEG, EOG and EMG monitoring system with embedded electronics for data acquisition and wireless transmission has been seamlessly integrated into fabric of these items for continuous detection of Mu waves. Textile electrodes were placed on positions C3, CZ, C4 according to 10-20 international system and their capability to detect Mu waves was tested. The system is ergonomic and can potentially be used for early diagnosis in infants and planning therapy for ASD patients.

  7. EEG entropy measures in anesthesia

    Science.gov (United States)

    Liang, Zhenhu; Wang, Yinghua; Sun, Xue; Li, Duan; Voss, Logan J.; Sleigh, Jamie W.; Hagihira, Satoshi; Li, Xiaoli

    2015-01-01

    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

  8. Processing of Natural Signals like EMG for Person Identification using NUFB-GMM

    OpenAIRE

    Suresh M; P G Krishnamohan; Mallikarjun S Holi

    2014-01-01

    Physiological signals like Electrocardiogram(ECG) and Electroencephalogram(EEG), including deoxyribonucleic acid(DNA) are person specific and distinct for different persons. The motor unit firing pattern, motor unit recruitment order and characteristics of muscle changing from person to person, and therefore Electromyogram (EMG) can be used for person identification. EMG records obtained from a single channel data acquisition system are used to develop person identification system. Non-unifor...

  9. A method to synchronize signals from multiple patient monitoring devices through a single input channel for inclusion in list-mode acquisitions

    International Nuclear Information System (INIS)

    O’Connor, J. Michael; Pretorius, P. Hendrik; Johnson, Karen; King, Michael A.

    2013-01-01

    Purpose: This technical note documents a method that the authors developed for combining a signal to synchronize a patient-monitoring device with a second physiological signal for inclusion into list-mode acquisition. Our specific application requires synchronizing an external patient motion-tracking system with a medical imaging system by multiplexing the tracking input with the ECG input. The authors believe that their methodology can be adapted for use in a variety of medical imaging modalities including single photon emission computed tomography (SPECT) and positron emission tomography (PET). Methods: The authors insert a unique pulse sequence into a single physiological input channel. This sequence is then recorded in the list-mode acquisition along with the R-wave pulse used for ECG gating. The specific form of our pulse sequence allows for recognition of the time point being synchronized even when portions of the pulse sequence are lost due to collisions with R-wave pulses. This was achieved by altering our software used in binning the list-mode data to recognize even a portion of our pulse sequence. Limitations on heart rates at which our pulse sequence could be reliably detected were investigated by simulating the mixing of the two signals as a function of heart rate and time point during the cardiac cycle at which our pulse sequence is mixed with the cardiac signal. Results: The authors have successfully achieved accurate temporal synchronization of our motion-tracking system with acquisition of SPECT projections used in 17 recent clinical research cases. In our simulation analysis the authors determined that synchronization to enable compensation for body and respiratory motion could be achieved for heart rates up to 125 beats-per-minute (bpm). Conclusions: Synchronization of list-mode acquisition with external patient monitoring devices such as those employed in motion-tracking can reliably be achieved using a simple method that can be implemented using

  10. A method to synchronize signals from multiple patient monitoring devices through a single input channel for inclusion in list-mode acquisitions

    Energy Technology Data Exchange (ETDEWEB)

    O’Connor, J. Michael; Pretorius, P. Hendrik; Johnson, Karen; King, Michael A., E-mail: Michael.King@umassmed.edu [Department of Radiology, University of Massachusetts Medical School, Worcester, Massachusetts 01655 (United States)

    2013-12-15

    Purpose: This technical note documents a method that the authors developed for combining a signal to synchronize a patient-monitoring device with a second physiological signal for inclusion into list-mode acquisition. Our specific application requires synchronizing an external patient motion-tracking system with a medical imaging system by multiplexing the tracking input with the ECG input. The authors believe that their methodology can be adapted for use in a variety of medical imaging modalities including single photon emission computed tomography (SPECT) and positron emission tomography (PET). Methods: The authors insert a unique pulse sequence into a single physiological input channel. This sequence is then recorded in the list-mode acquisition along with the R-wave pulse used for ECG gating. The specific form of our pulse sequence allows for recognition of the time point being synchronized even when portions of the pulse sequence are lost due to collisions with R-wave pulses. This was achieved by altering our software used in binning the list-mode data to recognize even a portion of our pulse sequence. Limitations on heart rates at which our pulse sequence could be reliably detected were investigated by simulating the mixing of the two signals as a function of heart rate and time point during the cardiac cycle at which our pulse sequence is mixed with the cardiac signal. Results: The authors have successfully achieved accurate temporal synchronization of our motion-tracking system with acquisition of SPECT projections used in 17 recent clinical research cases. In our simulation analysis the authors determined that synchronization to enable compensation for body and respiratory motion could be achieved for heart rates up to 125 beats-per-minute (bpm). Conclusions: Synchronization of list-mode acquisition with external patient monitoring devices such as those employed in motion-tracking can reliably be achieved using a simple method that can be implemented using

  11. Hypnagogic imagery and EEG activity.

    Science.gov (United States)

    Hayashi, M; Katoh, K; Hori, T

    1999-04-01

    The relationships between hypnagogic imagery and EEG activity were studied. 7 subjects (4 women and 3 men) reported the content of hypnagogic imagery every minute and the hypnagogic EEGs were classified into 5 stages according to Hori's modified criteria. The content of the hypnagogic imagery changed as a function of the hypnagogic EEG stages.

  12. Acquisition, extinction, and recall of opiate reward memory are signaled by dynamic neuronal activity patterns in the prefrontal cortex.

    Science.gov (United States)

    Sun, Ninglei; Chi, Ning; Lauzon, Nicole; Bishop, Stephanie; Tan, Huibing; Laviolette, Steven R

    2011-12-01

    The medial prefrontal cortex (mPFC) comprises an important component in the neural circuitry underlying drug-related associative learning and memory processing. Neuronal activation within mPFC circuits is correlated with the recall of opiate-related drug-taking experiences in both humans and other animals. Using an unbiased associative place conditioning procedure, we recorded mPFC neuronal populations during the acquisition, recall, and extinction phases of morphine-related associative learning and memory. Our analyses revealed that mPFC neurons show increased activity both in terms of tonic and phasic activity patterns during the acquisition phase of opiate reward-related memory and demonstrate stimulus-locked associative activity changes in real time, during the recall of opiate reward memories. Interestingly, mPFC neuronal populations demonstrated divergent patterns of bursting activity during the acquisition versus recall phases of newly acquired opiate reward memory, versus the extinction of these memories, with strongly increased bursting during the recall of an extinction memory and no associative bursting during the recall of a newly acquired opiate reward memory. Our results demonstrate that neurons within the mPFC are involved in both the acquisition, recall, and extinction of opiate-related reward memories, showing unique patterns of tonic and phasic activity patterns during these separate components of the opiate-related reward learning and memory recall.

  13. Generalized Information Equilibrium Approaches to EEG Sleep Stage Discrimination

    Directory of Open Access Journals (Sweden)

    Todd Zorick

    2016-01-01

    Full Text Available Recent advances in neuroscience have raised the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG signals is via power-law distributed neuronal avalanches, while EEG signals are nonstationary. Therefore, spectral analysis of EEG may miss many properties inherent in such signals. A complete understanding of such dynamical systems requires knowledge of the underlying nonequilibrium thermodynamics. In recent work by Fielitz and Borchardt (2011, 2014, the concept of information equilibrium (IE in information transfer processes has successfully characterized many different systems far from thermodynamic equilibrium. We utilized a publicly available database of polysomnogram EEG data from fourteen subjects with eight different one-minute tracings of sleep stage 2 and waking and an overlapping set of eleven subjects with eight different one-minute tracings of sleep stage 3. We applied principles of IE to model EEG as a system that transfers (equilibrates information from the time domain to scalp-recorded voltages. We find that waking consciousness is readily distinguished from sleep stages 2 and 3 by several differences in mean information transfer constants. Principles of IE applied to EEG may therefore prove to be useful in the study of changes in brain function more generally.

  14. Anti-deception: reliable EEG-based biometrics with real-time capability from the neural response of face rapid serial visual presentation.

    Science.gov (United States)

    Wu, Qunjian; Yan, Bin; Zeng, Ying; Zhang, Chi; Tong, Li

    2018-05-03

    The electroencephalogram (EEG) signal represents a subject's specific brain activity patterns and is considered as an ideal biometric given its superior invisibility, non-clonality, and non-coercion. In order to enhance its applicability in identity authentication, a novel EEG-based identity authentication method is proposed based on self- or non-self-face rapid serial visual presentation. In contrast to previous studies that extracted EEG features from rest state or motor imagery, the designed paradigm could obtain a distinct and stable biometric trait with a lower time cost. Channel selection was applied to select specific channels for each user to enhance system portability and improve discriminability between users and imposters. Two different imposter scenarios were designed to test system security, which demonstrate the capability of anti-deception. Fifteen users and thirty imposters participated in the experiment. The mean authentication accuracy values for the two scenarios were 91.31 and 91.61%, with 6 s time cost, which illustrated the precision and real-time capability of the system. Furthermore, in order to estimate the repeatability and stability of our paradigm, another data acquisition session is conducted for each user. Using the classification models generated from the previous sessions, a mean false rejected rate of 7.27% has been achieved, which demonstrates the robustness of our paradigm. Experimental results reveal that the proposed paradigm and methods are effective for EEG-based identity authentication.

  15. Improvement of the XANAM System and Acquisition of a Peak Signal with a High S/N ratio

    International Nuclear Information System (INIS)

    Suzuki, S; Nakamura, M; Kinoshita, K; Koike, Y; Fujikawa, K; Matsudaira, N; Chun, W-J; Nomura, M; Asakura, K

    2007-01-01

    We have made remarkable progress in detecting X-ray-induced frequency shift signals, which will promote development of a chemically sensitive NC-AFM. A highperformance controller provides a tenfold higher signal to noise ratio than that previously reported. We confirmed that the frequency shift or complementary Z-feedback signal dependence on X-ray energy has a peak. An important feature of the signal is that it does not follow the absorption spectrum of a surface element. These new findings are important to elucidate this novel X-ray-induced phenomenon

  16. Safety and EEG data quality of concurrent high-density EEG and high-speed fMRI at 3 Tesla

    DEFF Research Database (Denmark)

    Foged, Mette Thrane; Lindberg, Ulrich; Vakamudi, Kishore

    2017-01-01

    ) 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...

  17. Wireless recording systems: from noninvasive EEG-NIRS to invasive EEG devices.

    Science.gov (United States)

    Sawan, Mohamad; Salam, Muhammad T; Le Lan, Jérôme; Kassab, Amal; Gelinas, Sébastien; Vannasing, Phetsamone; Lesage, Frédéric; Lassonde, Maryse; Nguyen, Dang K

    2013-04-01

    In this paper, we present the design and implementation of a wireless wearable electronic system dedicated to remote data recording for brain monitoring. The reported wireless recording system is used for a) simultaneous near-infrared spectrometry (NIRS) and scalp electro-encephalography (EEG) for noninvasive monitoring and b) intracerebral EEG (icEEG) for invasive monitoring. Bluetooth and dual radio links were introduced for these recordings. The Bluetooth-based device was embedded in a noninvasive multichannel EEG-NIRS system for easy portability and long-term monitoring. On the other hand, the 32-channel implantable recording device offers 24-bit resolution, tunable features, and a sampling frequency up to 2 kHz per channel. The analog front-end preamplifier presents low input-referred noise of 5 μ VRMS and a signal-to-noise ratio of 112 dB. The communication link is implemented using a dual-band radio frequency transceiver offering a half-duplex 800 kb/s data rate, 16.5 mW power consumption and less than 10(-10) post-correction Bit-Error Rate (BER). The designed system can be accessed and controlled by a computer with a user-friendly graphical interface. The proposed wireless implantable recording device was tested in vitro using real icEEG signals from two patients with refractory epilepsy. The wirelessly recorded signals were compared to the original signals recorded using wired-connection, and measured normalized root-mean square deviation was under 2%.

  18. Wearable ear EEG for brain interfacing

    Science.gov (United States)

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

    2017-02-01

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

  19. Wireless and wearable EEG system for evaluating driver vigilance.

    Science.gov (United States)

    Lin, Chin-Teng; Chuang, Chun-Hsiang; Huang, Chih-Sheng; Tsai, Shu-Fang; Lu, Shao-Wei; Chen, Yen-Hsuan; Ko, Li-Wei

    2014-04-01

    Brain activity associated with attention sustained on the task of safe driving has received considerable attention recently in many neurophysiological studies. Those investigations have also accurately estimated shifts in drivers' levels of arousal, fatigue, and vigilance, as evidenced by variations in their task performance, by evaluating electroencephalographic (EEG) changes. However, monitoring the neurophysiological activities of automobile drivers poses a major measurement challenge when using a laboratory-oriented biosensor technology. This work presents a novel dry EEG sensor based mobile wireless EEG system (referred to herein as Mindo) to monitor in real time a driver's vigilance status in order to link the fluctuation of driving performance with changes in brain activities. The proposed Mindo system incorporates the use of a wireless and wearable EEG device to record EEG signals from hairy regions of the driver conveniently. Additionally, the proposed system can process EEG recordings and translate them into the vigilance level. The study compares the system performance between different regression models. Moreover, the proposed system is implemented using JAVA programming language as a mobile application for online analysis. A case study involving 15 study participants assigned a 90 min sustained-attention driving task in an immersive virtual driving environment demonstrates the reliability of the proposed system. Consistent with previous studies, power spectral analysis results confirm that the EEG activities correlate well with the variations in vigilance. Furthermore, the proposed system demonstrated the feasibility of predicting the driver's vigilance in real time.

  20. Higher-Order Spectrum in Understanding Nonlinearity in EEG Rhythms

    Directory of Open Access Journals (Sweden)

    Cauchy Pradhan

    2012-01-01

    Full Text Available The fundamental nature of the brain's electrical activities recorded as electroencephalogram (EEG remains unknown. Linear stochastic models and spectral estimates are the most common methods for the analysis of EEG because of their robustness, simplicity of interpretation, and apparent association with rhythmic behavioral patterns in nature. In this paper, we extend the use of higher-order spectrum in order to indicate the hidden characteristics of EEG signals that simply do not arise from random processes. The higher-order spectrum is an extension Fourier spectrum that uses higher moments for spectral estimates. This essentially nullifies all Gaussian random effects, therefore, can reveal non-Gaussian and nonlinear characteristics in the complex patterns of EEG time series. The paper demonstrates the distinguishing features of bispectral analysis for chaotic systems, filtered noises, and normal background EEG activity. The bispectrum analysis detects nonlinear interactions; however, it does not quantify the coupling strength. The squared bicoherence in the nonredundant region has been estimated to demonstrate nonlinear coupling. The bicoherence values are minimal for white Gaussian noises (WGNs and filtered noises. Higher bicoherence values in chaotic time series and normal background EEG activities are indicative of nonlinear coupling in these systems. The paper shows utility of bispectral methods as an analytical tool in understanding neural process underlying human EEG patterns.

  1. Research on control law accelerator of digital signal process chip TMS320F28035 for real-time data acquisition and processing

    Science.gov (United States)

    Zhao, Shuangle; Zhang, Xueyi; Sun, Shengli; Wang, Xudong

    2017-08-01

    TI C2000 series digital signal process (DSP) chip has been widely used in electrical engineering, measurement and control, communications and other professional fields, DSP TMS320F28035 is one of the most representative of a kind. When using the DSP program, need data acquisition and data processing, and if the use of common mode C or assembly language programming, the program sequence, analogue-to-digital (AD) converter cannot be real-time acquisition, often missing a lot of data. The control low accelerator (CLA) processor can run in parallel with the main central processing unit (CPU), and the frequency is consistent with the main CPU, and has the function of floating point operations. Therefore, the CLA coprocessor is used in the program, and the CLA kernel is responsible for data processing. The main CPU is responsible for the AD conversion. The advantage of this method is to reduce the time of data processing and realize the real-time performance of data acquisition.

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

    OpenAIRE

    Gang Wang; Doutian Ren

    2013-01-01

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

  3. Assessment of the depth of anesthesia based on symbolic dynamics of the EEG

    OpenAIRE

    Tupaika, Nadine; Vallverdú Ferrer, Montserrat; Jospin, Mathieu; Jensen, Erik Weber; Struys, Michel M. R. F.; Vereecke, Hugo E. M.; Voss, Andreas; Caminal Magrans, Pere

    2010-01-01

    Methodologies based on symbolic dynamics have successfully demonstrated to reflect the nonlinear behavior of biological signals. In the present study, symbolic dynamics was applied to the electroencephalogram (EEG) in order to describe the level of depth of anesthesia. The EEG was transformed to symbol sequences. Words of three symbols were built from this symbolic series. The results obtained from the EEGs of 36 patients undergoing anesthesia showed that the probabilities of the ...

  4. Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

    Science.gov (United States)

    Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

    2018-05-01

    Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pretrained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy, and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multichannel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

  5. A phasemeter concept for space applications that integrates an autonomous signal acquisition stage based on the discrete wavelet transform

    Science.gov (United States)

    Ales, Filippo; Mandel, Oliver; Gath, Peter; Johann, Ulrich; Braxmaier, Claus

    2015-08-01

    We describe a phasemeter designed to autonomously acquire and track a heterodyne signal with low signal-to-noise ratio in a frequency band that spans from 1 MHz to 25 MHz. The background driving some of the design criterions of the phasemeter comes from studies on future space mission concepts such as orbiting gravitational wave observatories and next generation geodesy missions which all rely on tracking phasemeters in order to meet their mission goal. The phasemeter has been implemented within a field programmable gate array trying to minimize the requirement of computational resources and its performance has been tested using signal generators. Laboratory test has shown that the phasemeter is capable of locking to an input signal in less than half a millisecond, while its phase measurement accuracy is in the micro-radian range for measurement frequencies that span from mHz to Hz.

  6. Smart acquisition EELS

    International Nuclear Information System (INIS)

    Sader, Kasim; Schaffer, Bernhard; Vaughan, Gareth; Brydson, Rik; Brown, Andy; Bleloch, Andrew

    2010-01-01

    We have developed a novel acquisition methodology for the recording of electron energy loss spectra (EELS) using a scanning transmission electron microscope (STEM): 'Smart Acquisition'. Smart Acquisition allows the independent control of probe scanning procedures and the simultaneous acquisition of analytical signals such as EELS. The original motivation for this work arose from the need to control the electron dose experienced by beam-sensitive specimens whilst maintaining a sufficiently high signal-to-noise ratio in the EEL signal for the extraction of useful analytical information (such as energy loss near edge spectral features) from relatively undamaged areas. We have developed a flexible acquisition framework which separates beam position data input, beam positioning, and EELS acquisition. In this paper we demonstrate the effectiveness of this technique on beam-sensitive thin films of amorphous aluminium trifluoride. Smart Acquisition has been used to expose lines to the electron beam, followed by analysis of the structures created by line-integrating EELS acquisitions, and the results are compared to those derived from a standard EELS linescan. High angle annular dark-field images show clear reductions in damage for the Smart Acquisition areas compared to the conventional linescan, and the Smart Acquisition low loss EEL spectra are more representative of the undamaged material than those derived using a conventional linescan. Atomically resolved EELS of all four elements of CaNdTiO show the high resolution capabilities of Smart Acquisition.

  7. Smart acquisition EELS

    Energy Technology Data Exchange (ETDEWEB)

    Sader, Kasim, E-mail: k.sader@leeds.ac.uk [SuperSTEM, J block, Daresbury Laboratory, Warrington, Cheshire, WA4 4AD (United Kingdom); Institute for Materials Research, University of Leeds, LS2 9JT (United Kingdom); Schaffer, Bernhard [SuperSTEM, J block, Daresbury Laboratory, Warrington, Cheshire, WA4 4AD (United Kingdom); Department of Physics and Astronomy, University of Glasgow (United Kingdom); Vaughan, Gareth [Institute for Materials Research, University of Leeds, LS2 9JT (United Kingdom); Brydson, Rik [SuperSTEM, J block, Daresbury Laboratory, Warrington, Cheshire, WA4 4AD (United Kingdom); Institute for Materials Research, University of Leeds, LS2 9JT (United Kingdom); Brown, Andy [Institute for Materials Research, University of Leeds, LS2 9JT (United Kingdom); Bleloch, Andrew [SuperSTEM, J block, Daresbury Laboratory, Warrington, Cheshire, WA4 4AD (United Kingdom); Department of Engineering, University of Liverpool, Liverpool (United Kingdom)

    2010-07-15

    We have developed a novel acquisition methodology for the recording of electron energy loss spectra (EELS) using a scanning transmission electron microscope (STEM): 'Smart Acquisition'. Smart Acquisition allows the independent control of probe scanning procedures and the simultaneous acquisition of analytical signals such as EELS. The original motivation for this work arose from the need to control the electron dose experienced by beam-sensitive specimens whilst maintaining a sufficiently high signal-to-noise ratio in the EEL signal for the extraction of useful analytical information (such as energy loss near edge spectral features) from relatively undamaged areas. We have developed a flexible acquisition framework which separates beam position data input, beam positioning, and EELS acquisition. In this paper we demonstrate the effectiveness of this technique on beam-sensitive thin films of amorphous aluminium trifluoride. Smart Acquisition has been used to expose lines to the electron beam, followed by analysis of the structures created by line-integrating EELS acquisitions, and the results are compared to those derived from a standard EELS linescan. High angle annular dark-field images show clear reductions in damage for the Smart Acquisition areas compared to the conventional linescan, and the Smart Acquisition low loss EEL spectra are more representative of the undamaged material than those derived using a conventional linescan. Atomically resolved EELS of all four elements of CaNdTiO show the high resolution capabilities of Smart Acquisition.

  8. Measurement and modification of the EEG and related behavior

    Science.gov (United States)

    Sterman, M. B.

    1991-01-01

    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

  9. MR imaging characteristics of intracranial hemorrhage using gradient-echo signal acquisition at 1.5 T: Comparison with spin-echo imaging and clinical applications

    International Nuclear Information System (INIS)

    Atlas, S.W.; Grossman, R.I.; Gomori, J.M.; Hackney, D.B.; Goldberg, H.I.; Bilaniuk, L.T.; Zimmerman, R.A.

    1987-01-01

    Evolving paramagnetic blood-breakdown products create static local magnetic susceptibility gradients, which induce rapid phase dispersion on the basis of T2/sup */ shortening. The authors evaluated 30 patients with 50 separate hemorrhagic intracranial lesions with both spin-echo (SE) and gradient echo signal acquisition (GESA) MR imaging at 1.5 T. GESA sequences used repetition time (TR) of 200-750 msec, echo time (TE) of 50-80 msec, and flip angles of 10 0 to 15 0 to emphasize T2/sup */-based contributions to contrast. SE sequences in all cases utilized both short and long TR (600 and 2,500-3,000 msec), with TE of 20-120 msec. Advantages of GESA imaging with Long TE and short flip angles in the evaluation of intracranial hemorrhage include (1) increased sensitivity to susceptibility-induced phase loss from T2/sup */ shortening, resulting in detection of hemorrhagic lesions not seen on conventional long TR/long TE SE images, and (2) very rapid acquisition of images with T2/sup */-based contrast. Limitations of this sequence include (1) severe diamagnetic susceptibility-induced artifacts, especially near air-brain interfaces, which often obscure large portions of the brain and occasionally simulate serious pathology, (2) characteristic internal signal intensity patterns demonstrated by SE imaging, such as in evolving hematomas, occult vascular malformations, and hemorrhagic malignancies, are often obscured by marked hypointensity on GESA images, and (3) reduced signal-noise ratio. The authors conclude that, although images with marked sensitivity to T2/sup */ effects can be rapidly generated by GESA, there is only a limited role for this sequence when evaluating intracranial hemorrhage at 1.5 T, and, in fact, significant information is lost when compared to SE images

  10. Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification

    Directory of Open Access Journals (Sweden)

    Shengkun Xie

    2014-01-01

    Full Text Available Classification of electroencephalography (EEG is the most useful diagnostic and monitoring procedure for epilepsy study. A reliable algorithm that can be easily implemented is the key to this procedure. In this paper a novel signal feature extraction method based on dynamic principal component analysis and nonoverlapping moving window is proposed. Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. The obtained results demonstrated that our proposed methodologies are able to differentiate EEGs from controls and interictal for epilepsy diagnosis and to separate EEGs from interictal and ictal for seizure detection. Our approach yields high classification accuracy for both single-channel short-term EEGs and multichannel long-term EEGs. The classification performance of the method is also compared with other state-of-the-art techniques on the same datasets and the effect of signal variability on the presented methods is also studied.

  11. Design and implementation of an electrocardiographical signal acquisition and digital processing system orientated to the detection of paroxysmal arrhythmias

    International Nuclear Information System (INIS)

    Braceli, Agustín Iriart; Morani, Jorge Exequiel

    2011-01-01

    This article describes the design, technical aspects and implementation of a device capable of acquiring electrocardiograph signals; visualize them in real time over a graphic liquid crystal display (GLCD), and the storage of these ECG registers on a SD memory card. It also details a noise suppression algorithm using the Wavelet Transform. This system was specially developed to cover some bankruptcy that presents actual Holters or ECG regarding the detection of paroxysmal arrhythmias. The contribution of this work is settled on its portability and low production cost. The filtering method used provides an ECG signal without any significant noise and appropriate to the diagnosis of cardiac pathologies.

  12. Design and implementation of an electrocardiographical signal acquisition and digital processing system orientated to the detection of paroxysmal arrhythmias

    Science.gov (United States)

    Iriart Braceli, Agustín; Exequiel Morani, Jorge

    2011-12-01

    This article describes the design, technical aspects and implementation of a device capable of acquiring electrocardiograph signals; visualize them in real time over a graphic liquid crystal display (GLCD), and the storage of these ECG registers on a SD memory card. It also details a noise suppression algorithm using the Wavelet Transform. This system was specially developed to cover some bankruptcy that presents actual Holters or ECG regarding the detection of paroxysmal arrhythmias. The contribution of this work is settled on its portability and low production cost. The filtering method used provides an ECG signal without any significant noise and appropriate to the diagnosis of cardiac pathologies.

  13. Rett syndrome: EEG presentation.

    Science.gov (United States)

    Robertson, R; Langill, L; Wong, P K; Ho, H H

    1988-11-01

    Rett syndrome, a degenerative neurological disorder of girls, has a classical presentation and typical EEG findings. The electroencephalograms (EEGs) of 7 girls whose records have been followed from the onset of symptoms to the age of 5 or more are presented. These findings are tabulated with the Clinical Staging System of Hagberg and Witt-Engerström (1986). The records show a progressive deterioration in background rhythms in waking and sleep. The abnormalities of the background activity may only become evident at 4-5 years of age or during stage 2--the Rapid Destructive Stage. The marked contrast between waking and sleep background may not occur until stage 3--the Pseudostationary Stage. In essence EEG changes appear to lag behind clinical symptomatology by 1-3 years. An unexpected, but frequent, abnormality was central spikes seen in 5 of 7 girls. They appeared to be age related and could be evoked by tactile stimulation in 2 patients. We hypothesize that the prominent 'hand washing' mannerism may be self-stimulating and related to the appearance of central spike discharges.

  14. Feasibility of Seizure Prediction from intracranial EEG Recordings

    DEFF Research Database (Denmark)

    Henriksen, Jonas; Kjær, Troels; Thomsen, Carsten E.

    2009-01-01

    Purpose: The current project evaluated the feasibility of providing an algorithm that could warn a patient of a forthcoming seizure based on iEEG recordings. Method: The mean phase coherence (MPC) feature (Mormann F et al. Phys Nonlinear Phenom 2000;3-4:358-369.) was implemented and tested...... in a rigorously, out-of-sample manner. The MPC-feature is based on the synchronization measure, explained through the analytic signal approach where the Hilbert transform is used to find the instantaneous phase of an arbitrary signal. By a relative comparison between two different iEEG channels the phase...

  15. Proepileptic patterns in EEG of WAG/Rij rats

    Science.gov (United States)

    Grubov, Vadim V.; Sitnikova, Evgenia Yu.; Nedaivozov, Vladimir O.; Koronovskii, Alexey A.

    2018-04-01

    In this paper we study specific oscillatory patterns on EEG signals of WAG/Rij rats. These patterns are known as proepileptic because they occur in time period during the development of absence-epilepsy before fully-formed epileptic seizures. In the paper we analyze EEG signals of WAG/Rij rats with continuous wavelet transform and empirical mode decomposition in order to find particular features of epileptic spike-wave discharges and nonepileptic sleep spindles. Then we introduce proepileptic activity as patterns that combine features of epileptic and non-epileptic activity. We analyze proepileptic activity in order to specify its features and time-frequency structure.

  16. High-density EEG coherence analysis using functional units applied to mental fatigue

    NARCIS (Netherlands)

    Caat, Michael ten; Lorist, Monicque M.; Bezdan, Eniko; Roerdink, Jos B.T.M.; Maurits, Natasha M.

    2008-01-01

    Electroencephalography (EEG) coherence provides a quantitative measure of functional brain connectivity which is calculated between pairs of signals as a function of frequency. Without hypotheses, traditional coherence analysis would be cumbersome for high-density EEG which employs a large number of

  17. The hemodynamic response of the alpha rhythm: an EEG/fMRI study.

    NARCIS (Netherlands)

    de Munck, J.C.; Goncalves, S.I.; Huijboom, L.; Kuijer, J.P.; Pouwels, P.J.; Heethaar, R.M.; Lopes da Silva, F.H.

    2007-01-01

    EEG was recorded during fMRI scanning of 16 normal controls in resting condition with eyes closed. Time variations of the occipital alpha band amplitudes were correlated to the fMRI signal variations to obtain insight into the hemodynamic correlates of the EEG alpha activity. Contrary to earlier

  18. Changes in decibel scale wavelength properties of EEG with alertness levels while performing sustained attention tasks.

    Science.gov (United States)

    Arjunan, Sridhar P; Kumar, Dinesh K; Jung, Tzyy-Ping

    2009-01-01

    Loss of alertness can have dire consequences for people controlling motorized equipment or for people in professions such as defense. Electroencephalogram (EEG) is known to be related to alertness of the person, but due to high level of noise and low signal strength, the use of EEG for such applications has been considered to be unreliable. This study reports the fractal analysis of EEG and identifies the use of maximum fractal length (MFL) as a feature that is inversely correlated with the alertness of the subject. The results show that MFL (of only single channel of EEG) indicates the loss of alertness of the individual with mean (inverse) correlation coefficient = 0.82.

  19. Wide-range bipolar pulse conductance instrument employing current and voltage modes with sampled or integrated signal acquisition

    Energy Technology Data Exchange (ETDEWEB)

    Calhoun, R K; Holler, F J [Kentucky Univ., Lexington, KY (United States). Dept. of Chemistry; Geiger, jr, R F; Nieman, T A [Illinois Univ., Urbana, IL (United States). Dept. of Chemistry; Caserta, K J [Procter and Gamble Co., Cincinnati, OH (United States)

    1991-11-05

    An instrument for measuring solution conductance using the bipolar pulse technique is described. The instrument is capable of measuring conductances in the range of 5x10{sup -9}-10{Omega}{sup -1} with 1% accuracy or better in as little as 32 {mu}s. Accuracy of 0.001-0.01% is achievable over the range 1x10{sup -6}-1{Omega}{sup -1}. Circuitry and software are described that allow the instrument to adjust automatically the pulse height, pulse duration, excitation mode (current or voltage pulse) and data acquisition mode (sampled or integrated) to acquire data of optimum accuracy and precision. The urease-catalyzed decomposition of urea is used to illustrate the versality of the instrument, and other applications are cited. (author). 60 refs.; 7 figs.; 2 tabs.

  20. Stimulus-dependent spiking relationships with the EEG

    Science.gov (United States)

    Snyder, Adam C.

    2015-01-01

    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

  1. Decoding English Alphabet Letters Using EEG Phase Information

    Directory of Open Access Journals (Sweden)

    YiYan Wang

    2018-02-01

    Full Text Available Increasing evidence indicates that the phase pattern and power of the low frequency oscillations of brain electroencephalograms (EEG contain significant information during the human cognition of sensory signals such as auditory and visual stimuli. Here, we investigate whether and how the letters of the alphabet can be directly decoded from EEG phase and power data. In addition, we investigate how different band oscillations contribute to the classification and determine the critical time periods. An English letter recognition task was assigned, and statistical analyses were conducted to decode the EEG signal corresponding to each letter visualized on a computer screen. We applied support vector machine (SVM with gradient descent method to learn the potential features for classification. It was observed that the EEG phase signals have a higher decoding accuracy than the oscillation power information. Low-frequency theta and alpha oscillations have phase information with higher accuracy than do other bands. The decoding performance was best when the analysis period began from 180 to 380 ms after stimulus presentation, especially in the lateral occipital and posterior temporal scalp regions (PO7 and PO8. These results may provide a new approach for brain-computer interface techniques (BCI and may deepen our understanding of EEG oscillations in cognition.

  2. Local and Widely Distributed EEG Activity in Schizophrenia With Prevalence of Negative Symptoms.

    Science.gov (United States)

    Grin-Yatsenko, Vera A; Ponomarev, Valery A; Pronina, Marina V; Poliakov, Yury I; Plotnikova, Irina V; Kropotov, Juri D

    2017-09-01

    We evaluated EEG frequency abnormalities in resting state (eyes closed and eyes open) EEG in a group of chronic schizophrenia patients as compared with healthy subjects. The study included 3 methods of analysis of deviation of EEG characteristics: genuine EEG, current source density (CSD), and group independent component (gIC). All 3 methods have shown that the EEG in schizophrenia patients is characterized by enhanced low-frequency (delta and theta) and high-frequency (beta) activity in comparison with the control group. However, the spatial pattern of differences was dependent on the type of method used. Comparative analysis has shown that increased EEG power in schizophrenia patients apparently concerns both widely spatially distributed components and local components of signal. Furthermore, the observed differences in the delta and theta range can be described mainly by the local components, and those in the beta range mostly by spatially widely distributed ones. The possible nature of the widely distributed activity is discussed.

  3. A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

    Directory of Open Access Journals (Sweden)

    Jiuqi Han

    2018-04-01

    Full Text Available Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high computational complexity, non-convergent procedure and narrow expansibility. In this paper, to remedy these drawbacks, we propose a fast, open EEG classification framework centralized by EEG feature compression, low-dimensional representation, and convergent iterative channel ranking. First, to reduce the complexity, we use data clustering to compress the EEG features channel-wise, packing the high-dimensional EEG signal, and endowing them with numerical signatures. Second, to provide easy access to alternative superior methods, we structurally represent each EEG trial in a feature vector with its corresponding numerical signature. Thus, the recorded signals of many trials shrink to a low-dimensional structural matrix compatible with most pattern recognition methods. Third, a series of effective iterative feature selection approaches with theoretical convergence is introduced to rank the EEG channels and remove redundant ones, further accelerating the EEG classification process and ensuring its stability. Finally, a classical linear discriminant analysis (LDA model is employed to classify a single EEG trial with selected channels. Experimental results on two real world brain-computer interface (BCI competition datasets demonstrate the promising performance of the proposed framework over state-of-the-art methods.

  4. A preliminary study of muscular artifact cancellation in single-channel EEG.

    Science.gov (United States)

    Chen, Xun; Liu, Aiping; Peng, Hu; Ward, Rabab K

    2014-10-01

    Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis. For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts. In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems. For practical reasons, a single EEG channel system must be used in these situations. Unfortunately, there exist few studies for muscular artifact cancellation in single-channel EEG recordings. To address this issue, in this preliminary study, we propose a simple, yet effective, method to achieve the muscular artifact cancellation for the single-channel EEG case. This method is a combination of the ensemble empirical mode decomposition (EEMD) and the joint blind source separation (JBSS) techniques. We also conduct a study that compares and investigates all possible single-channel solutions and demonstrate the performance of these methods using numerical simulations and real-life applications. The proposed method is shown to significantly outperform all other methods. It can successfully remove muscular artifacts without altering the underlying EEG activity. It is thus a promising tool for use in ambulatory healthcare systems.

  5. [A novel biologic electricity signal measurement based on neuron chip].

    Science.gov (United States)

    Lei, Yinsheng; Wang, Mingshi; Sun, Tongjing; Zhu, Qiang; Qin, Ran

    2006-06-01

    Neuron chip is a multiprocessor with three pipeline CPU; its communication protocol and control processor are integrated in effect to carry out the function of communication, control, attemper, I/O, etc. A novel biologic electronic signal measurement network system is composed of intelligent measurement nodes with neuron chip at the core. In this study, the electronic signals such as ECG, EEG, EMG and BOS can be synthetically measured by those intelligent nodes, and some valuable diagnostic messages are found. Wavelet transform is employed in this system to analyze various biologic electronic signals due to its strong time-frequency ability of decomposing signal local character. Better effect is gained. This paper introduces the hardware structure of network and intelligent measurement node, the measurement theory and the signal figure of data acquisition and processing.

  6. Post-Correlation Semi-Coherent Integration for High-Dynamic and Weak GPS Signal Acquisition (Preprint)

    Science.gov (United States)

    2008-06-01

    average signal model in (8) is repeated below with simplified notations as: )2( 0φπγδ += kjkk Bez (14) where δk = bkbk+1, B = MA2 , γ = 2αTs2M2, φ0...1995. [2] E.D. Kaplan and C.J. Hegarty (eds.), Understanding GPS: Principles and Applications (2nd Ed.) Artech House Publishers, Norwood, MA, 2006

  7. Predicting EEG complexity from sleep macro and microstructure

    International Nuclear Information System (INIS)

    Chouvarda, I; Maglaveras, N; Mendez, M O; Rosso, V; Parrino, L; Grassi, A; Terzano, M; Bianchi, A M; Cerutti, S

    2011-01-01

    This work investigates the relation between the complexity of electroencephalography (EEG) signal, as measured by fractal dimension (FD), and normal sleep structure in terms of its macrostructure and microstructure. Sleep features are defined, encoding sleep stage and cyclic alternating pattern (CAP) related information, both in short and long term. The relevance of each sleep feature to the EEG FD is investigated, and the most informative ones are depicted. In order to quantitatively assess the relation between sleep characteristics and EEG dynamics, a modeling approach is proposed which employs subsets of the sleep macrostructure and microstructure features as input variables and predicts EEG FD based on these features of sleep micro/macrostructure. Different sleep feature sets are investigated along with linear and nonlinear models. Findings suggest that the EEG FD time series is best predicted by a nonlinear support vector machine (SVM) model, employing both sleep stage/transitions and CAP features at different time scales depending on the EEG activation subtype. This combination of features suggests that short-term and long-term history of macro and micro sleep events interact in a complex manner toward generating the dynamics of sleep

  8. EEG dynamical correlates of focal and diffuse causes of coma.

    Science.gov (United States)

    Kafashan, MohammadMehdi; Ryu, Shoko; Hargis, Mitchell J; Laurido-Soto, Osvaldo; Roberts, Debra E; Thontakudi, Akshay; Eisenman, Lawrence; Kummer, Terrance T; Ching, ShiNung

    2017-11-15

    Rapidly determining the causes of a depressed level of consciousness (DLOC) including coma is a common clinical challenge. Quantitative analysis of the electroencephalogram (EEG) has the potential to improve DLOC assessment by providing readily deployable, temporally detailed characterization of brain activity in such patients. While used commonly for seizure detection, EEG-based assessment of DLOC etiology is less well-established. As a first step towards etiological diagnosis, we sought to distinguish focal and diffuse causes of DLOC through assessment of temporal dynamics within EEG signals. We retrospectively analyzed EEG recordings from 40 patients with DLOC with consensus focal or diffuse culprit pathology. For each recording, we performed a suite of time-series analyses, then used a statistical framework to identify which analyses (features) could be used to distinguish between focal and diffuse cases. Using cross-validation approaches, we identified several spectral and non-spectral EEG features that were significantly different between DLOC patients with focal vs. diffuse etiologies, enabling EEG-based classification with an accuracy of 76%. Our findings suggest that DLOC due to focal vs. diffuse injuries differ along several electrophysiological parameters. These results may form the basis of future classification strategies for DLOC and coma that are more etiologically-specific and therefore therapeutically-relevant.

  9. Rapidly Learned Identification of Epileptic Seizures from Sonified EEG

    Directory of Open Access Journals (Sweden)

    Psyche eLoui

    2014-10-01

    Full Text Available Sonification refers to a process by which data are converted into sound, providing an auditory alternative to visual display. Currently, the prevalent method for diagnosing seizures in epilepsy is by visually reading a patient’s electroencephalogram (EEG. However, sonification of the EEG data provides certain advantages due to the nature of human auditory perception. We hypothesized that human listeners will be able to identify seizures from EEGs using the auditory modality alone, and that accuracy of seizure identification will increase after a short training session. Here we describe an algorithm we have used to sonify EEGs of both seizure and non-seizure activity, followed by a training study in which subjects listened to short clips of sonified EEGs and determine whether each clip was of seizure or normal activity, both before and after a short training session. Results show that before training subjects performed at chance level in differentiating seizures vs. non-seizures, but there was a significant improvement of accuracy after the training session. After training, subjects successfully distinguished seizures from non-seizures using the auditory modality alone. Further analyses using signal detection theory demonstrated improvement in sensitivity and reduction in response bias as a result of training. This study demonstrates the potential of sonified EEGs to be used for the detection of seizures. Future studies will attempt to increase accuracy using novel training and sonification modifications, with the goals of managing, predicting, and ultimately controlling seizures using sonification as a possible biofeedback-based intervention for epilepsy.

  10. A three domain covariance framework for EEG/MEG data.

    Science.gov (United States)

    Roś, Beata P; Bijma, Fetsje; de Gunst, Mathisca C M; de Munck, Jan C

    2015-10-01

    In this paper we introduce a covariance framework for the analysis of single subject EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-trial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three components that correspond to space, time and epochs/trials, and consider maximum likelihood estimation of the unknown parameter values. An iterative algorithm that finds approximations of the maximum likelihood estimates is proposed. Our covariance model is applicable in a variety of cases where spontaneous EEG or MEG acts as source of noise and realistic noise covariance estimates are needed, such as in evoked activity studies, or where the properties of spontaneous EEG or MEG are themselves the topic of interest, like in combined EEG-fMRI experiments in which the correlation between EEG and fMRI signals is investigated. We use a simulation study to assess the performance of the estimator and investigate the influence of different assumptions about the covariance factors on the estimated covariance matrix and on its components. We apply our method to real EEG and MEG data sets. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. Acquisition of 5-fluorouracil resistance induces epithelial-mesenchymal transitions through the Hedgehog signaling pathway in HCT-8 colon cancer cells.

    Science.gov (United States)

    Liu, Yanjun; DU, Fangfang; Zhao, Qiannan; Jin, Jian; Ma, Xin; Li, Huazhong

    2015-06-01

    Colon cancer has a high incidence in individuals >60-years-old. The commonly used chemotherapeutic agent, 5-fluorouracil (5-FU), has gradually lost its potency in treating colorectal cancer following the acquisition of resistance. Drug resistance is usually associated with epithelial-mesenchymal transitions (EMTs) in cancer cells. In the present study, the EMT phenotypes of two colon cancer cell lines, wild-type (HCT-8/WT) and 5-FU-resistant (HCT-8/5-FU), were characterized following the analysis of cellular migration, proliferation, morphology and molecular changes. In order to further clarify the mechanism of EMT in HCT-8/5-FU cells, the effect of EMT pathway inhibitors upon drug sensitivity was investigated. The results revealed that the Hedgehog signaling pathway inhibitor, GDC0449, reversed drug resistance. Therefore, inhibition of the Hedgehog pathway may provide a novel chemotherapeutic strategy for the treatment of patients with 5-FU-resistant colon cancer.

  12. Negligible motion artifacts in scalp electroencephalography (EEG during treadmill walking

    Directory of Open Access Journals (Sweden)

    Kevin eNathan

    2016-01-01

    Full Text Available Recent Mobile Brain/Body Imaging (MoBI techniques based on active electrode scalp electroencephalogram (EEG allow the acquisition and real-time analysis of brain dynamics during active unrestrained motor behavior involving whole body movements such as treadmill walking, over-ground walking and other locomotive and non-locomotive tasks. Unfortunately, MoBI protocols are prone to physiological and non-physiological artifacts, including motion artifacts that may contaminate the EEG recordings. A few attempts have been made to quantify these artifacts during locomotion tasks but with inconclusive results due in part to methodological pitfalls. In this paper, we investigate the potential contributions of motion artifacts in scalp EEG during treadmill walking at three different speeds (1.5, 3.0, and 4.5 km/h using a wireless 64 channel active EEG system and a wireless inertial sensor attached to the subject’s head. The experimental setup was designed according to good measurement practices using state-of-the-art commercially-available instruments, and the measurements were analyzed using Fourier analysis and wavelet coherence approaches. Contrary to prior claims, the subjects’ motion did not significantly affect their EEG during treadmill walking although precaution should be taken when gait speeds approach 4.5 km/h. Overall, these findings suggest how MoBI methods may be safely deployed in neural, cognitive, and rehabilitation engineering applications.

  13. Data acquisition and signal processing system for IPR R1 TRIGA-Mark I nuclear research reactor of CDTN

    International Nuclear Information System (INIS)

    Mesquita, A.Z.; Maretti, F. Jr.; Rezende, H.C.; Tambourgi, E.B.

    2004-01-01

    The TRIGA IPR-R1 Nuclear Research Reactor, located at the Nuclear Technology Development Center (CDTN/CNEN) in Belo Horizonte, Brazil, is being operated since 44 years ago. The main operational parameters were monitored by analog recorders and counters located in the reactor control console. The reactor operators registered the most important operational parameters and data in the reactor logbook. This process is quite useful, but it can involve some human errors. It is also impossible for the operators to take notes of all variables involving the process mainly during fast power transients in some operations. A PC-based data acquisition was developed for the reactor that allows online monitoring, through graphic interfaces, and shows operational parameters evolution to the operators. Some parameters that were not measured, like the power and the coolant flow rate at the primary loop, are monitored now in the computer video monitor. The developed system allows measuring out all parameters in a frequency up to 1 kHz. These data is also recorded in text files available for consults and analysis. (author)

  14. Safety of Simultaneous Scalp or Intracranial EEG during MRI: A Review

    Directory of Open Access Journals (Sweden)

    Hassan B. Hawsawi

    2017-10-01

    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.

  15. A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

    Directory of Open Access Journals (Sweden)

    Balbir Singh

    2017-01-01

    Full Text Available EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA, which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies.

  16. A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

    Science.gov (United States)

    Wagatsuma, Hiroaki

    2017-01-01

    EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies. PMID:28194221

  17. Robust power spectral estimation for EEG data.

    Science.gov (United States)

    Melman, Tamar; Victor, Jonathan D

    2016-08-01

    Typical electroencephalogram (EEG) recordings often contain substantial artifact. These artifacts, often large and intermittent, can interfere with quantification of the EEG via its power spectrum. To reduce the impact of artifact, EEG records are typically cleaned by a preprocessing stage that removes individual segments or components of the recording. However, such preprocessing can introduce bias, discard available signal, and be labor-intensive. With this motivation, we present a method that uses robust statistics to reduce dependence on preprocessing by minimizing the effect of large intermittent outliers on the spectral estimates. Using the multitaper method (Thomson, 1982) as a starting point, we replaced the final step of the standard power spectrum calculation with a quantile-based estimator, and the Jackknife approach to confidence intervals with a Bayesian approach. The method is implemented in provided MATLAB modules, which extend the widely used Chronux toolbox. Using both simulated and human data, we show that in the presence of large intermittent outliers, the robust method produces improved estimates of the power spectrum, and that the Bayesian confidence intervals yield close-to-veridical coverage factors. The robust method, as compared to the standard method, is less affected by artifact: inclusion of outliers produces fewer changes in the shape of the power spectrum as well as in the coverage factor. In the presence of large intermittent outliers, the robust method can reduce dependence on data preprocessing as compared to standard methods of spectral estimation. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. Monitoring alert and drowsy states by modeling EEG source nonstationarity

    Science.gov (United States)

    Hsu, Sheng-Hsiou; Jung, Tzyy-Ping

    2017-10-01

    Objective. As a human brain performs various cognitive functions within ever-changing environments, states of the brain characterized by recorded brain activities such as electroencephalogram (EEG) are inevitably nonstationary. The challenges of analyzing the nonstationary EEG signals include finding neurocognitive sources that underlie different brain states and using EEG data to quantitatively assess the state changes. Approach. This study hypothesizes that brain activities under different states, e.g. levels of alertness, can be modeled as distinct compositions of statistically independent sources using independent component analysis (ICA). This study presents a framework to quantitatively assess the EEG source nonstationarity and estimate levels of alertness. The framework was tested against EEG data collected from 10 subjects performing a sustained-attention task in a driving simulator. Main results. Empirical results illustrate that EEG signals under alert versus drowsy states, indexed by reaction speeds to driving challenges, can be characterized by distinct ICA models. By quantifying the goodness-of-fit of each ICA model to the EEG data using the model deviation index (MDI), we found that MDIs were significantly correlated with the reaction speeds (r  =  -0.390 with alertness models and r  =  0.449 with drowsiness models) and the opposite correlations indicated that the two models accounted for sources in the alert and drowsy states, respectively. Based on the observed source nonstationarity, this study also proposes an online framework using a subject-specific ICA model trained with an initial (alert) state to track the level of alertness. For classification of alert against drowsy states, the proposed online framework achieved an averaged area-under-curve of 0.745 and compared favorably with a classic power-based approach. Significance. This ICA-based framework provides a new way to study changes of brain states and can be applied to

  19. Textile Electrodes for EEG Recording — A Pilot Study

    Directory of Open Access Journals (Sweden)

    Johan Löfhede

    2012-12-01

    Full Text Available The overall aim of our research is to develop a monitoring system for neonatal intensive care units. Long-term EEG monitoring in newborns require that the electrodes don’t harm the sensitive skin of the baby, an especially relevant feature for premature babies. Our approach to EEG monitoring is based on several electrodes distributed over the head of the baby, and since the weight of the head always will be on some of them, any type of hard electrode will inevitably cause a pressure-point that can irritate the skin. Therefore, we propose the use of soft conductive textiles as EEG electrodes, primarily for neonates, but also for other kinds of unobtrusive long-term monitoring. In this paper we have tested two types of textile electrodes on five healthy adults and compared them to standard high quality electrodes. The acquired signals were compared with respect to morphology, frequency distribution, spectral coherence, correlation and power line interference sensitivity, and the signals were found to be similar in most respects. The good measurement performance exhibited by the textile electrodes indicates that they are feasible candidates for EEG recording, opening the door for long-term EEG monitoring applications.

  20. Unsupervised EEG analysis for automated epileptic seizure detection

    Science.gov (United States)

    Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad

    2016-07-01

    Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

  1. Autonomic nervous system dynamics for mood and emotional-state recognition significant advances in data acquisition, signal processing and classification

    CERN Document Server

    Valenza, Gaetano

    2014-01-01

    This monograph reports on advances in the measurement and study of autonomic nervous system (ANS) dynamics as a source of reliable and effective markers for mood state recognition and assessment of emotional responses. Its primary impact will be in affective computing and the application of emotion-recognition systems. Applicative studies of biosignals such as: electrocardiograms; electrodermal responses; respiration activity; gaze points; and pupil-size variation are covered in detail, and experimental results explain how to characterize the elicited affective levels and mood states pragmatically and accurately using the information thus extracted from the ANS. Nonlinear signal processing techniques play a crucial role in understanding the ANS physiology underlying superficially noticeable changes and provide important quantifiers of cardiovascular control dynamics. These have prognostic value in both healthy subjects and patients with mood disorders. Moreover, Autonomic Nervous System Dynamics for Mood and ...

  2. A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Xiong, Tao; Richardson, Andrew G; Lucas, Timothy H; Chin, Peter S; Etienne-Cummings, Ralph; Tran, Trac D; Van der Spiegel, Jan

    2016-07-18

    Reliable, multi-channel neural recording is critical to the neuroscience research and clinical treatment. However, most hardware development of fully integrated, multi-channel wireless neural recorders to-date, is still in the proof-of-concept stage. To be ready for practical use, the trade-offs between performance, power consumption, device size, robustness, and compatibility need to be carefully taken into account. This paper presents an optimized wireless compressed sensing neural signal recording system. The system takes advantages of both custom integrated circuits and universal compatible wireless solutions. The proposed system includes an implantable wireless system-on-chip (SoC) and an external wireless relay. The SoC integrates 16-channel low-noise neural amplifiers, programmable filters and gain stages, a SAR ADC, a real-time compressed sensing module, and a near field wireless power and data transmission link. The external relay integrates a 32 bit low-power microcontroller with Bluetooth 4.0 wireless module, a programming interface, and an inductive charging unit. The SoC achieves high signal recording quality with minimized power consumption, while reducing the risk of infection from through-skin connectors. The external relay maximizes the compatibility and programmability. The proposed compressed sensing module is highly configurable, featuring a SNDR of 9.78 dB with a compression ratio of 8×. The SoC has been fabricated in a 180 nm standard CMOS technology, occupying 2.1 mm × 0.6 mm silicon area. A pre-implantable system has been assembled to demonstrate the proposed paradigm. The developed system has been successfully used for long-term wireless neural recording in freely behaving rhesus monkey.

  3. Acquisition and analysis of cardiovascular signals on smartphones: potential, pitfalls and perspectives: by the Task Force of the e-Cardiology Working Group of European Society of Cardiology.

    Science.gov (United States)

    Bruining, Nico; Caiani, Enrico; Chronaki, Catherine; Guzik, Przemyslaw; van der Velde, Enno

    2014-11-01

    Smartphones, mobile applications ('apps'), social media, analytics, and the cloud are profoundly changing the practice of medicine and the way health decisions are made. With the constant progress of technology, the measurement of vital signals becomes easier, cheaper, and practically a standard approach in clinical practice. The interest in measuring vital signals goes beyond medical professionals to the general public, patients, informal caregivers, and healthy individuals, who frequently lack any formal medical training. On smartphone platforms such as iOS and Android, a proliferation of health or medical 'apps' acquire and analyse a variety of vital signs through embedded sensors, interconnected devices or peripherals utilising on occasion analytics and social media. Smartphone vendors compete with traditional medical device manufacturers in the grey area between health care, wellness, and fitness, as US and EU regulatory bodies are setting and revising rules for these new technologies. On the other hand, in the absence of robust validation results, clinicians are hesitant to trust measurements by apps or recommend specific apps to their patients, partly also due to lack of a cost reimbursement policy. This review focuses on the acquisition and analysis on smartphones of three important vital signs in the cardiovascular and respiratory field as well as in rehabilitation i.e. heart or pulse rate, blood pressure, and blood oxygenation. The potential, pitfalls, and perspectives on mobile devices and smartphone apps for health management by patients and healthy individuals are discussed. © Authors 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

  4. Real-time dynamic range and signal to noise enhancement in beam-scanning microscopy by integration of sensor characteristics, data acquisition hardware, and statistical methods

    Science.gov (United States)

    Kissick, David J.; Muir, Ryan D.; Sullivan, Shane Z.; Oglesbee, Robert A.; Simpson, Garth J.

    2013-02-01

    Despite the ubiquitous use of multi-photon and confocal microscopy measurements in biology, the core techniques typically suffer from fundamental compromises between signal to noise (S/N) and linear dynamic range (LDR). In this study, direct synchronous digitization of voltage transients coupled with statistical analysis is shown to allow S/N approaching the theoretical maximum throughout an LDR spanning more than 8 decades, limited only by the dark counts of the detector on the low end and by the intrinsic nonlinearities of the photomultiplier tube (PMT) detector on the high end. Synchronous digitization of each voltage transient represents a fundamental departure from established methods in confocal/multi-photon imaging, which are currently based on either photon counting or signal averaging. High information-density data acquisition (up to 3.2 GB/s of raw data) enables the smooth transition between the two modalities on a pixel-by-pixel basis and the ultimate writing of much smaller files (few kB/s). Modeling of the PMT response allows extraction of key sensor parameters from the histogram of voltage peak-heights. Applications in second harmonic generation (SHG) microscopy are described demonstrating S/N approaching the shot-noise limit of the detector over large dynamic ranges.

  5. A technique to consider mismatches between fMRI and EEG/MEG sources for fMRI-constrained EEG/MEG source imaging: a preliminary simulation study

    International Nuclear Information System (INIS)

    Im, Chang-Hwan; Lee, Soo Yeol

    2006-01-01

    fMRI-constrained EEG/MEG source imaging can be a powerful tool in studying human brain functions with enhanced spatial and temporal resolutions. Recent studies on the combination of fMRI and EEG/MEG have suggested that fMRI prior information could be readily implemented by simply imposing different weighting factors to cortical sources overlapping with the fMRI activations. It has been also reported, however, that such a hard constraint may cause severe distortions or elimination of meaningful EEG/MEG sources when there are distinct mismatches between the fMRI activations and the EEG/MEG sources. If one wants to obtain the actual EEG/MEG source locations and uses the fMRI prior information as just an auxiliary tool to enhance focality of the distributed EEG/MEG sources, it is reasonable to weaken the strength of fMRI constraint when severe mismatches between fMRI and EEG/MEG sources are observed. The present study suggests an efficient technique to automatically adjust the strength of fMRI constraint according to the mismatch level. The use of the proposed technique rarely affects the results of conventional fMRI-constrained EEG/MEG source imaging if no major mismatch between the two modalities is detected; while the new results become similar to those of typical EEG/MEG source imaging without fMRI constraint if the mismatch level is significant. A preliminary simulation study using realistic EEG signals demonstrated that the proposed technique can be a promising tool to selectively apply fMRI prior information to EEG/MEG source imaging

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

    Directory of Open Access Journals (Sweden)

    Syam Syahrull Hi-Fi

    2017-01-01

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

  7. EEG/MEG Source Reconstruction with Spatial-Temporal Two-Way Regularized Regression

    KAUST Repository

    Tian, Tian Siva; Huang, Jianhua Z.; Shen, Haipeng; Li, Zhimin

    2013-01-01

    In this work, we propose a spatial-temporal two-way regularized regression method for reconstructing neural source signals from EEG/MEG time course measurements. The proposed method estimates the dipole locations and amplitudes simultaneously

  8. Quantitative EEG analysis using error reduction ratio-causality test; validation on simulated and real EEG data.

    Science.gov (United States)

    Sarrigiannis, Ptolemaios G; Zhao, Yifan; Wei, Hua-Liang; Billings, Stephen A; Fotheringham, Jayne; Hadjivassiliou, Marios

    2014-01-01

    To introduce a new method of quantitative EEG analysis in the time domain, the error reduction ratio (ERR)-causality test. To compare performance against cross-correlation and coherence with phase measures. A simulation example was used as a gold standard to assess the performance of ERR-causality, against cross-correlation and coherence. The methods were then applied to real EEG data. Analysis of both simulated and real EEG data demonstrates that ERR-causality successfully detects dynamically evolving changes between two signals, with very high time resolution, dependent on the sampling rate of the data. Our method can properly detect both linear and non-linear effects, encountered during analysis of focal and generalised seizures. We introduce a new quantitative EEG method of analysis. It detects real time levels of synchronisation in the linear and non-linear domains. It computes directionality of information flow with corresponding time lags. This novel dynamic real time EEG signal analysis unveils hidden neural network interactions with a very high time resolution. These interactions cannot be adequately resolved by the traditional methods of coherence and cross-correlation, which provide limited results in the presence of non-linear effects and lack fidelity for changes appearing over small periods of time. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  9. Resting-state EEG, impulsiveness, and personality in daily and nondaily smokers.

    Science.gov (United States)

    Rass, Olga; Ahn, Woo-Young; O'Donnell, Brian F

    2016-01-01

    Resting EEG is sensitive to transient, acute effects of nicotine administration and abstinence, but the chronic effects of smoking on EEG are poorly characterized. This study measures the resting EEG profile of chronic smokers in a non-deprived, non-peak state to test whether differences in smoking behavior and personality traits affect pharmaco-EEG response. Resting EEG, impulsiveness, and personality measures were collected from daily smokers (n=22), nondaily smokers (n=31), and non-smokers (n=30). Daily smokers had reduced resting delta and alpha EEG power and higher impulsiveness (Barratt Impulsiveness Scale) compared to nondaily smokers and non-smokers. Both daily and nondaily smokers discounted delayed rewards more steeply, reported lower conscientiousness (NEO-FFI), and reported greater disinhibition and experience seeking (Sensation Seeking Scale) than non-smokers. Nondaily smokers reported greater sensory hedonia than nonsmokers. Altered resting EEG power in daily smokers demonstrates differences in neural signaling that correlated with greater smoking behavior and dependence. Although nondaily smokers share some characteristics with daily smokers that may predict smoking initiation and maintenance, they differ on measures of impulsiveness and resting EEG power. Resting EEG in non-deprived chronic smokers provides a standard for comparison to peak and trough nicotine states and may serve as a biomarker for nicotine dependence, relapse risk, and recovery. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  10. Resting-state EEG, Impulsiveness, and Personality in Daily and Nondaily Smokers†

    Science.gov (United States)

    Rass, Olga; Ahn, Woo-Young; O’Donnell, Brian F.

    2015-01-01

    Objectives Resting EEG is sensitive to transient, acute effects of nicotine administration and abstinence, but the chronic effects smoking on EEG are poorly characterized. This study measures the resting EEG profile of chronic smokers in a non-deprived, non-peak state to test whether differences in smoking behavior and personality traits affect pharmaco-EEG response. Methods Resting EEG, impulsiveness, and personality measures were collected from daily smokers (n=22), nondaily smokers (n=31), and non-smokers (n=30). Results Daily smokers had reduced resting delta and alpha EEG power and higher impulsiveness (Barratt Impulsiveness Scale) compared to nondaily smokers and non-smokers. Both daily and nondaily smokers discounted delayed rewards more steeply, reported lower conscientiousness (NEO-FFI) and reported greater disinhibition and experience seeking (Sensation Seeking Scale) than non-smokers. Nondaily smokers reported greater sensory hedonia than nonsmokers. Conclusions Altered resting EEG power in daily smokers demonstrates differences in neural signaling that correlated with greater smoking behavior and dependence. Although nondaily smokers share some characteristics with daily smokers that may predict smoking initiation and maintenance, they differ on measures of impulsiveness and resting EEG power. Significance Resting EEG in non-deprived chronic smokers provides a standard for comparison to peak and trough nicotine states and may serve as a biomarker for nicotine dependence, relapse risk, and recovery. PMID:26051750

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

    Science.gov (United States)

    Fingelkurts, Alexander A.; Fingelkurts, Andrew A.

    2014-01-01

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

  12. Data acquisition and real-time signal processing of plasma diagnostics on ASDEX Upgrade using LabVIEW RT

    International Nuclear Information System (INIS)

    Giannone, L.; Cerna, M.; Cole, R.; Fitzek, M.; Kallenbach, A.; Lueddecke, K.; McCarthy, P.J.; Scarabosio, A.; Schneider, W.; Sips, A.C.C.; Treutterer, W.; Vrancic, A.; Wenzel, L.; Yi, H.; Behler, K.; Eich, T.; Eixenberger, H.; Fuchs, J.C.; Haas, G.; Lexa, G.

    2010-01-01

    The existing VxWorks real-time system for the position and shape control in ASDEX Upgrade has been extended to calculate magnetic flux surfaces in real-time using a multi-core PCI Express system running LabVIEW RT 8.6. real-time signal processing of bolometers and manometers is performed with the on-board FPGA to calculate the measured radiated power flux and particle flux respectively from the raw data. Radiation feedback experiments use halo current measurements from the outer divertor with real-time median filter pre-processing to remove the excursions produced by ELMs. Integration of these plasma diagnostics into the control system by the exchange of XML sheets for communicating the real-time variables to be produced and consumed is in operation. Reflective memory and UDP are employed by the LabVIEW RT plasma diagnostics to communicate with the control system and other plasma diagnostics in a multi-platform real-time network.

  13. CAMS prototype extension: Integration of data acquisition, signal validation, tracking simulator, predictive simulator, state identification, and probabilistic safety assessment

    International Nuclear Information System (INIS)

    Fantoni, Paolo; Iguchi, Yukihiro; Meyer, Geir; Soerensen, Aimar; Van Dyck, Claude

    1996-04-01

    CAMS (Computerized Accident Management Support) is a system that will provide assistance to the staff in the control room, in the technical support centre, and in a national safety centre. These three groups of users do not need the same type of support. Support is offered in identification of the plant state, in assessment of the future development of the accident, and in planning of accident mitigation strategies. Last year the predictive part of the system was tested at a safety exercise arranged by the Swedish Nuclear Inspectorate, and found to be a useful tool, with potential for further development. Now, new methods are added in signal validation, state identification, tracking simulation, predictive simulation, risk monitoring, and man-machine interface design. A prototype will be demonstrated at Loen in May 1996. This prototype is still under development. The purpose of this prototype is to test those methods in a simulated environment to verify that the developed functions, using different techniques, can work together producing the desired result in an efficient way. The plan is to test these techniques at power plants. During the CAMS design, a considerable effort has been given to maintain the generality of the CAMS concept; although the referenced process has been so far a BWR nuclear plant, the use of this structure and design can be applied to other processes, including non-nuclear processes. The research programme is carried out in close cooperation with member organizations (author)

  14. Data acquisition and real-time signal processing of plasma diagnostics on ASDEX Upgrade using LabVIEW RT

    Energy Technology Data Exchange (ETDEWEB)

    Giannone, L., E-mail: Louis.Giannone@ipp.mpg.d [Max-Planck-Institut fuer Plasmaphysik, EURATOM-IPP Association, D-85748 Garching (Germany); Cerna, M. [National Instruments, Austin, TX 78759-3504 (United States); Cole, R.; Fitzek, M. [Unlimited Computer Systems GmbH, 82393 Iffeldorf (Germany); Kallenbach, A. [Max-Planck-Institut fuer Plasmaphysik, EURATOM-IPP Association, D-85748 Garching (Germany); Lueddecke, K. [Unlimited Computer Systems GmbH, 82393 Iffeldorf (Germany); McCarthy, P.J. [Department of Physics, University College Cork, Association EURATOM-DCU, Cork (Ireland); Scarabosio, A.; Schneider, W.; Sips, A.C.C.; Treutterer, W. [Max-Planck-Institut fuer Plasmaphysik, EURATOM-IPP Association, D-85748 Garching (Germany); Vrancic, A.; Wenzel, L.; Yi, H. [National Instruments, Austin, TX 78759-3504 (United States); Behler, K.; Eich, T.; Eixenberger, H.; Fuchs, J.C.; Haas, G.; Lexa, G. [Max-Planck-Institut fuer Plasmaphysik, EURATOM-IPP Association, D-85748 Garching (Germany)

    2010-07-15

    The existing VxWorks real-time system for the position and shape control in ASDEX Upgrade has been extended to calculate magnetic flux surfaces in real-time using a multi-core PCI Express system running LabVIEW RT 8.6. real-time signal processing of bolometers and manometers is performed with the on-board FPGA to calculate the measured radiated power flux and particle flux respectively from the raw data. Radiation feedback experiments use halo current measurements from the outer divertor with real-time median filter pre-processing to remove the excursions produced by ELMs. Integration of these plasma diagnostics into the control system by the exchange of XML sheets for communicating the real-time variables to be produced and consumed is in operation. Reflective memory and UDP are employed by the LabVIEW RT plasma diagnostics to communicate with the control system and other plasma diagnostics in a multi-platform real-time network.

  15. EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures.

    Science.gov (United States)

    Wang, Lei; Long, Xi; Arends, Johan B A M; Aarts, Ronald M

    2017-10-01

    The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. A single-channel EEG signal can be mapped into visibility graphs (VGS), including basic visibility graph (VG), horizontal VG (HVG), and difference VG (DVG). These graphs were used to characterize different EEG seizure patterns. To demonstrate its effectiveness in identifying EEG seizure patterns and detecting generalized seizures, EEG recordings of 615h on one EEG channel from 29 epileptic patients with ID were analyzed. A novel feature set with discriminative power for seizure detection was obtained by using the VGS method. The degree distributions (DDs) of DVG can clearly distinguish EEG of each seizure pattern. The degree entropy and power-law degree power in DVG were proposed here for the first time, and they show significant difference between seizure and non-seizure EEG. The connecting structure measured by HVG can better distinguish seizure EEG from background than those by VG and DVG. A traditional EEG feature set based on frequency analysis was used here as a benchmark feature set. With a support vector machine (SVM) classifier, the seizure detection performance of the benchmark feature set (sensitivity of 24%, FD t /h of 1.8s) can be improved by combining our proposed VGS features extracted from one EEG channel (sensitivity of 38%, FD t /h of 1.4s). The proposed VGS-based features can help improve seizure detection for ID patients. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Educational simulation of the electroencephalogram (EEG)

    NARCIS (Netherlands)

    Beer, de N.A.M.; Meurs, van W.L.; Grit, M.B.M.; Good, M.L.; Gravenstein, D.

    2001-01-01

    We describe a model for simulating a spontaneous electroencephalogram (EEG) and for simulating the effects of anesthesia on the EEG, to allow anesthesiologists and EEG technicians to learn and practice intraoperative EEG monitoring. For this purpose, we developed a linear model to manipulate the

  17. Driver-response relationships between frontal EEG and respiration during affective audiovisual stimuli.

    Science.gov (United States)

    Kroupi, Eleni; Vesin, Jean-Marc; Ebrahimi, Touradj

    2013-01-01

    The complementary nature and the coordinative tendencies of brain and body are essential to the way humans function. Although static features from brain and body signals have been shown to reflect emotions, the dynamic interrelation of the two systems during emotional processes is still in its infancy. This study aims at investigating the way brain signals captured by Electroencephalography (EEG) and bodily responses reflected in respiration interact when watching music clips. A non-linear measure is applied to frontal EEG and respiration to determine the driver/driven relationship between these two modalities. The results reveal a unidirectional dependence from respiration to EEG which adds evidence to the bodily-feedback theory.

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

    Directory of Open Access Journals (Sweden)

    Thorsten Oliver Zander

    2011-05-01

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

  19. Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis.

    Science.gov (United States)

    Somers, Ben; Bertrand, Alexander

    2016-12-01

    Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. We apply a distributed canonical correlation analysis (CCA-)based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.

  20. EEG in connection with coma.

    Science.gov (United States)

    Wilson, John A; Nordal, Helge J

    2013-01-08

    Coma is a dynamic condition that may have various causes. Important changes may take place rapidly, often with consequences for treatment. The purpose of this article is to provide a brief overview of EEG patterns in comas with various causes, and indicate how EEG contributes in an assessment of the prognosis for coma patients. The article is based on many years of clinical and research-based experience of EEG used for patients in coma. A self-built reference database was supplemented by searches for relevant articles in PubMed. EEG reveals immediate changes in coma, and can provide early information on cause and prognosis. It is the only diagnostic tool for detecting a non-convulsive epileptic status. Locked-in- syndrome may be overseen without EEG. Repeated EEG scans increase diagnostic certainty and make it possible to monitor the development of coma. EEG reflects brain function continuously and therefore holds a key place in the assessment and treatment of coma.

  1. Seismic data acquisition systems

    International Nuclear Information System (INIS)

    Kolvankar, V.G.; Nadre, V.N.; Rao, D.S.

    1989-01-01

    Details of seismic data acquisition systems developed at the Bhabha Atomic Research Centre, Bombay are reported. The seismic signals acquired belong to different signal bandwidths in the band from 0.02 Hz to 250 Hz. All these acquisition systems are built around a unique technique of recording multichannel data on to a single track of an audio tape and in digital form. Techniques of how these signals in different bands of frequencies were acquired and recorded are described. Method of detecting seismic signals and its performance is also discussed. Seismic signals acquired in different set-ups are illustrated. Time indexing systems for different set-ups and multichannel waveform display systems which form essential part of the data acquisition systems are also discussed. (author). 13 refs., 6 figs., 1 tab

  2. Electroencephalogy (EEG) Feedback in Decision-Making

    Science.gov (United States)

    2015-08-26

    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

  3. Low Cost Electrode Assembly for EEG Recordings in Mice

    Directory of Open Access Journals (Sweden)

    Emily C. Vogler

    2017-11-01

    Full Text Available Wireless electroencephalography (EEG of small animal subjects typically utilizes miniaturized EEG devices which require a robust recording and electrode assembly that remains in place while also being well-tolerated by the animal so as not to impair the ability of the animal to perform normal living activities or experimental tasks. We developed simple and fast electrode assembly and method of electrode implantation using electrode wires and wire-wrap technology that provides both higher survival and success rates in obtaining recordings from the electrodes than methods using screws as electrodes. The new wire method results in a 51% improvement in the number of electrodes that successfully record EEG signal. Also, the electrode assembly remains affixed and provides EEG signal for at least a month after implantation. Screws often serve as recording electrodes, which require either drilling holes into the skull to insert screws or affixing screws to the surface of the skull with adhesive. Drilling holes large enough to insert screws can be invasive and damaging to brain tissue, using adhesives may interfere with conductance and result in a poor signal, and soldering screws to wire leads results in fragile connections. The methods presented in this article provide a robust implant that is minimally invasive and has a significantly higher success rate of electrode implantation. In addition, the implant remains affixed and produces good recordings for over a month, while using economical, easily obtained materials and skills readily available in most animal research laboratories.

  4. Multireference adaptive noise canceling applied to the EEG.

    Science.gov (United States)

    James, C J; Hagan, M T; Jones, R D; Bones, P J; Carroll, G J

    1997-08-01

    The technique of multireference adaptive noise canceling (MRANC) is applied to enhance transient nonstationarities in the electroeancephalogram (EEG), with the adaptation implemented by means of a multilayer-perception artificial neural network (ANN). The method was applied to recorded EEG segments and the performance on documented nonstationarities recorded. The results show that the neural network (nonlinear) gives an improvement in performance (i.e., signal-to-noise ratio (SNR) of the nonstationarities) compared to a linear implementation of MRANC. In both cases an improvement in the SNR was obtained. The advantage of the spatial filtering aspect of MRANC is highlighted when the performance of MRANC is compared to that of the inverse auto-regressive filtering of the EEG, a purely temporal filter.

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

    Directory of Open Access Journals (Sweden)

    Guy eCheron

    2016-02-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  8. Multivariate spectral-analysis of movement-related EEG data

    International Nuclear Information System (INIS)

    Andrew, C. M.

    1997-01-01

    The univariate method of event-related desynchronization (ERD) analysis, which quantifies the temporal evolution of power within specific frequency bands from electroencephalographic (EEG) data recorded during a task or event, is extended to an event related multivariate spectral analysis method. With this method, time courses of cross-spectra, phase spectra, coherence spectra, band-averaged coherence values (event-related coherence, ERCoh), partial power spectra and partial coherence spectra are estimated from an ensemble of multivariate event-related EEG trials. This provides a means of investigating relationships between EEG signals recorded over different scalp areas during the performance of a task or the occurrence of an event. The multivariate spectral analysis method is applied to EEG data recorded during three different movement-related studies involving discrete right index finger movements. The first study investigates the impact of the EEG derivation type on the temporal evolution of interhemispheric coherence between activity recorded at electrodes overlying the left and right sensorimotor hand areas during cued finger movement. The question results whether changes in coherence necessarily reflect changes in functional coupling of the cortical structures underlying the recording electrodes. The method is applied to data recorded during voluntary finger movement and a hypothesis, based on an existing global/local model of neocortical dynamics, is formulated to explain the coherence results. The third study applies partial spectral analysis too, and investigates phase relationships of, movement-related data recorded from a full head montage, thereby providing further results strengthening the global/local hypothesis. (author)

  9. Beamforming applied to surface EEG improves ripple visibility.

    Science.gov (United States)

    van Klink, Nicole; Mol, Arjen; Ferrier, Cyrille; Hillebrand, Arjan; Huiskamp, Geertjan; Zijlmans, Maeike

    2018-01-01

    Surface EEG can show epileptiform ripples in people with focal epilepsy, but identification is impeded by the low signal-to-noise ratio of the electrode recordings. We used beamformer-based virtual electrodes to improve ripple identification. We analyzed ten minutes of interictal EEG of nine patients with refractory focal epilepsy. EEGs with more than 60 channels and 20 spikes were included. We computed ∼79 virtual electrodes using a scalar beamformer and marked ripples (80-250 Hz) co-occurring with spikes in physical and virtual electrodes. Ripple numbers in physical and virtual electrodes were compared, and sensitivity and specificity of ripples for the region of interest (ROI; based on clinical information) were determined. Five patients had ripples in the physical electrodes and eight in the virtual electrodes, with more ripples in virtual than in physical electrodes (101 vs. 57, p = .007). Ripples in virtual electrodes predicted the ROI better than physical electrodes (AUC 0.65 vs. 0.56, p = .03). Beamforming increased ripple visibility in surface EEG. Virtual ripples predicted the ROI better than physical ripples, although sensitivity was still poor. Beamforming can facilitate ripple identification in EEG. Ripple localization needs to be improved to enable its use for presurgical evaluation in people with epilepsy. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  10. The Performance of EEG-P300 Classification using Backpropagation Neural Networks

    Directory of Open Access Journals (Sweden)

    Arjon Turnip

    2013-12-01

    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.

  11. Drowsiness detection for single channel EEG by DWT best m-term approximation

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

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

    2012-01-01

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

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

    Science.gov (United States)

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

    2012-01-01

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

  14. Automated EEG-based screening of depression using deep convolutional neural network.

    Science.gov (United States)

    Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat; Subha, D P

    2018-07-01

    In recent years, advanced neurocomputing and machine learning techniques have been used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In this paper, a novel computer model is presented for EEG-based screening of depression using a deep neural network machine learning approach, known as Convolutional Neural Network (CNN). The proposed technique does not require a semi-manually-selected set of features to be fed into a classifier for classification. It learns automatically and adaptively from the input EEG signals to differentiate EEGs obtained from depressive and normal subjects. The model was tested using EEGs obtained from 15 normal and 15 depressed patients. The algorithm attained accuracies of 93.5% and 96.0% using EEG signals from the left and right hemisphere, respectively. It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere. This discovery is consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere. An exciting extension of this research would be diagnosis of different stages and severity of depression and development of a Depression Severity Index (DSI). Copyright © 2018 Elsevier B.V. All rights reserved.

  15. Acoustic processing of temporally modulated sounds in infants: evidence from a combined near-infrared spectroscopy and EEG study

    Directory of Open Access Journals (Sweden)

    Silke eTelkemeyer

    2011-04-01

    Full Text Available Speech perception requires rapid extraction of the linguistic content from the acoustic signal. The ability to efficiently process rapid changes in auditory information is important for decoding speech and thereby crucial during language acquisition. Investigating functional networks of speech perception in infancy might elucidate neuronal ensembles supporting perceptual abilities that gate language acquisition. Interhemispheric specializations for language have been demonstrated in infants. How these asymmetries are shaped by basic temporal acoustic properties is under debate. We recently provided evidence that newborns process non-linguistic sounds sharing temporal features with language in a differential and lateralized fashion. The present study used the same material while measuring brain responses of 6 and 3 month old infants using simultaneous recordings of electroencephalography (EEG and near-infrared spectroscopy (NIRS. NIRS reveals that the lateralization observed in newborns remains constant over the first months of life. While fast acoustic modulations elicit bilateral neuronal activations, slow modulations lead to right-lateralized responses. Additionally, auditory evoked potentials and oscillatory EEG responses show differential responses for fast and slow modulations indicating a sensitivity for temporal acoustic variations. Oscillatory responses reveal an effect of development, that is, 6 but not 3 month old infants show stronger theta-band desynchronization for slowly modulated sounds. Whether this developmental effect is due to increasing fine-grained perception for spectrotemporal sounds in general remains speculative. Our findings support the notion that a more general specialization for acoustic properties can be considered the basis for lateralization of speech perception. The results show that concurrent assessment of vascular based imaging and electrophysiological responses have great potential in the research on language

  16. The effect of signal acquisition and processing choices on ApEn values: towards a "gold standard" for distinguishing effort levels from isometric force records.

    Science.gov (United States)

    Forrest, Sarah M; Challis, John H; Winter, Samantha L

    2014-06-01

    Approximate entropy (ApEn) is frequently used to identify changes in the complexity of isometric force records with ageing and disease. Different signal acquisition and processing parameters have been used, making comparison or confirmation of results difficult. This study determined the effect of sampling and parameter choices by examining changes in ApEn values across a range of submaximal isometric contractions of the first dorsal interosseus. Reducing the sample rate by decimation changed both the value and pattern of ApEn values dramatically. The pattern of ApEn values across the range of effort levels was not sensitive to the filter cut-off frequency, or the criterion used to extract the section of data for analysis. The complexity increased with increasing effort levels using a fixed 'r' value (which accounts for measurement noise) but decreased with increasing effort level when 'r' was set to 0.1 of the standard deviation of force. It is recommended isometric force records are sampled at frequencies >200Hz, template length ('m') is set to 2, and 'r' set to measurement system noise or 0.1SD depending on physiological process to be distinguished. It is demonstrated that changes in ApEn across effort levels are related to changes in force gradation strategy. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

  17. Automatic detection and classification of artifacts in single-channel EEG

    DEFF Research Database (Denmark)

    Olund, Thomas; Duun-Henriksen, Jonas; Kjaer, Troels W.

    2014-01-01

    Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single......-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different...... artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated...

  18. Magnetic resonance imaging in relation to EEG epileptic foci in tuberous sclerosis

    International Nuclear Information System (INIS)

    Tamaki, Kyoko; Okuno, Takehiko; Ito, Masatoshi; Asato, Reinin; Konishi, Junji; Mikawa, Haruki

    1990-01-01

    In 20 patients with tuberous sclerosis (TS), who were sequentially treated for epilepsy at our clinic, the high signal lesions in the cerebral cortex and subcortex detected on T2 weighted magnetic resonance imaging (MRI) were compared with the interictal EEG findings. In four cases who showed a unilateral distribution of the MRI lesions, there was a good correlation between the laterality of the affected lobes and the localization of the EEG epileptic foci. Thirteen cases with more than four affected lobes in both hemispheres also showed bilateral epileptic foci on EEG. The MRI lesions in the occipital lobes showed the best correlation with the EEG epileptic foci, while the worst correlation was seen in the frontal lobes. In addition, the cases with four or more affected lobes without laterality on MRI are more likely to show bilateral synchronization on EEG. The prognosis of epilepsy in these cases was found to be rather poor. (author)

  19. Evaluation of a modified Fitts law brain-computer interface target acquisition task in able and motor disabled individuals

    Science.gov (United States)

    Felton, E. A.; Radwin, R. G.; Wilson, J. A.; Williams, J. C.

    2009-10-01

    A brain-computer interface (BCI) is a communication system that takes recorded brain signals and translates them into real-time actions, in this case movement of a cursor on a computer screen. This work applied Fitts' law to the evaluation of performance on a target acquisition task during sensorimotor rhythm-based BCI training. Fitts' law, which has been used as a predictor of movement time in studies of human movement, was used here to determine the information transfer rate, which was based on target acquisition time and target difficulty. The information transfer rate was used to make comparisons between control modalities and subject groups on the same task. Data were analyzed from eight able-bodied and five motor disabled participants who wore an electrode cap that recorded and translated their electroencephalogram (EEG) signals into computer cursor movements. Direct comparisons were made between able-bodied and disabled subjects, and between EEG and joystick cursor control in able-bodied subjects. Fitts' law aptly described the relationship between movement time and index of difficulty for each task movement direction when evaluated separately and averaged together. This study showed that Fitts' law can be successfully applied to computer cursor movement controlled by neural signals.

  20. Resting-state qEEG predicts rate of second language learning in adults.

    Science.gov (United States)

    Prat, Chantel S; Yamasaki, Brianna L; Kluender, Reina A; Stocco, Andrea

    2016-01-01

    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.

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

    Science.gov (United States)

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

    2018-04-01

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

  2. EEG frequency PCA in EEG-ERP dynamics.

    Science.gov (United States)

    Barry, Robert J; De Blasio, Frances M

    2018-05-01

    Principal components analysis (PCA) has long been used to decompose the ERP into components, and these mathematical entities are increasingly accepted as meaningful and useful representatives of the electrophysiological components constituting the ERP. A similar expansion appears to be beginning in regard to decomposition of the EEG amplitude spectrum into frequency components via frequency PCA. However, to date, there has been no exploration of the brain's dynamic EEG-ERP linkages using PCA decomposition to assess components in each measure. Here, we recorded intrinsic EEG in both eyes-closed and eyes-open resting conditions, followed by an equiprobable go/no-go task. Frequency PCA of the EEG, including the nontask resting and within-task prestimulus periods, found seven frequency components within the delta to beta range. These differentially predicted PCA-derived go and no-go N1 and P3 ERP components. This demonstration suggests that it may be beneficial in future brain dynamics studies to implement PCA for the derivation of data-driven components from both the ERP and EEG. © 2017 Society for Psychophysiological Research.

  3. Nuevo Enfoque para la Clasificación de Señales EEG usando la Varianza de la Diferencia entre las Clases de un Clasificador Bayesiano

    Directory of Open Access Journals (Sweden)

    Thomaz R. Botelho

    2017-10-01

    Full Text Available Resumen: Los avances en robótica de rehabilitación están beneficiando en gran medida a los pacientes con discapacidad física. Los dispositivos de asistencia y rehabilitación pueden basar su funcionamiento en información fisiológica de los músculos y del cerebro a través de electromiografía (EMG y electroencefalografía (EEG, para detectar la intención de movimiento de los usuarios. En este trabajo se presenta una propuesta de interfaz multimodal para la adquisición, sincronización y procesamiento de señales EEG y de sensores inerciales, para ser aplicada en tareas de rehabilitación con exoesqueletos robóticos. Se realizaron experimentos con individuos sanos con el objetivo de analizar la intención de movimiento, la activación muscular e inicio de movimiento durante los movimientos de extensión de la rodilla. Esta propuesta es un nuevo enfoque para la clasificación de señales EEG usando un clasificador bayesiano tomando en cuenta la varianza de la diferencia entre las clases usadas. El aporte de este trabajo se sustenta con los resultados que muestran un incremento del 30% en la precisión de clasificación con señales EEG en comparación con los enfoques tradicionales de clasificación, en un análisis off-line para el reconocimiento de la intención de movimiento de los miembros inferiores. Abstract: Patients with physical disabilities can benefit from robotic rehabilitation. This improves the efficiency of recovery and, therefore, the rehabilitation of the patient. Assistive and rehabilitation devices can make use of physiological data, such as electromyography (EMG and electroencephalography (EEG, in order to detect movement intentions. This work presents a multimodal interface for signal acquisition, synchronization and processing of EEG and inertial sensors signals, to be applied in rehabilitation robotic exoskeletons. Experiments were performed with healthy individuals executing knee extension. The goal is to analyze

  4. Automatic classification of background EEG activity in healthy and sick neonates

    Science.gov (United States)

    Löfhede, Johan; Thordstein, Magnus; Löfgren, Nils; Flisberg, Anders; Rosa-Zurera, Manuel; Kjellmer, Ingemar; Lindecrantz, Kaj

    2010-02-01

    The overall aim of our research is to develop methods for a monitoring system to be used at neonatal intensive care units. When monitoring a baby, a range of different types of background activity needs to be considered. In this work, we have developed a scheme for automatic classification of background EEG activity in newborn babies. EEG from six full-term babies who were displaying a burst suppression pattern while suffering from the after-effects of asphyxia during birth was included along with EEG from 20 full-term healthy newborn babies. The signals from the healthy babies were divided into four behavioural states: active awake, quiet awake, active sleep and quiet sleep. By using a number of features extracted from the EEG together with Fisher's linear discriminant classifier we have managed to achieve 100% correct classification when separating burst suppression EEG from all four healthy EEG types and 93% true positive classification when separating quiet sleep from the other types. The other three sleep stages could not be classified. When the pathological burst suppression pattern was detected, the analysis was taken one step further and the signal was segmented into burst and suppression, allowing clinically relevant parameters such as suppression length and burst suppression ratio to be calculated. The segmentation of the burst suppression EEG works well, with a probability of error around 4%.

  5. Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification

    Directory of Open Access Journals (Sweden)

    Demi Soetraprawata

    2013-06-01

    Full Text Available Brain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%.

  6. The use of Matlab for colour fuzzy representation of multichannel EEG short time spectra.

    Science.gov (United States)

    Bigan, C; Strungaru, R

    1998-01-01

    During the last years, a lot of EEG research efforts was directed to intelligent methods for automatic analysis of data from multichannel EEG recordings. However, all the applications reported were focused on specific single tasks like detection of one specific "event" in the EEG signal: spikes, sleep spindles, epileptic seizures, K complexes, alpha or other rhythms or even artefacts. The aim of this paper is to present a complex system being able to perform a representation of the dynamic changes in frequency components of each EEG channel. This representation uses colours as a powerful means to show the only one frequency range chosen from the shortest epoch of signal able to be processed with the conventional "Short Time Fast Fourier Transform" (S.T.F.F.T.) method.

  7. In the twinkling of an eye: synchronization of EEG and eye tracking based on blink signatures

    DEFF Research Database (Denmark)

    Bækgaard, Per; Petersen, Michael Kai; Larsen, Jakob Eg

    2014-01-01

    function based algorithm to correlate the signals. Comparing the accuracy of the method against a state of the art EYE-EEG plug-in for offline analysis of EEG and eye tracking data, we propose our approach could be applied for robust synchronization of biometric sensor data collected in a mobile context.......ACHIEVING ROBUST ADAPTIVE SYNCHRONIZATION OF MULTIMODAL BIOMETRIC INPUTS: The recent arrival of wireless EEG headsets that enable mobile real-time 3D brain imaging on smartphones, and low cost eye trackers that provide gaze control of tablets, will radically change how biometric sensors might...... be integrated into next generation user interfaces. In experimental lab settings EEG neuroimaging and eye tracking data are traditionally combined using external triggers to synchronize the signals. However, with biometric sensors increasingly being applied in everyday usage scenarios, there will be a need...

  8. A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings

    Directory of Open Access Journals (Sweden)

    Logothetis Nikos K

    2009-07-01

    Full Text Available Abstract Background Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Results Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. Conclusion The new toolbox presented here implements fast

  9. A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings.

    Science.gov (United States)

    Magri, Cesare; Whittingstall, Kevin; Singh, Vanessa; Logothetis, Nikos K; Panzeri, Stefano

    2009-07-16

    Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. The new toolbox presented here implements fast and data-robust computations of the most relevant

  10. More attention, less deficit:wearable eeg-based serious game for focus improvement

    OpenAIRE

    Alchalabi, Alaa Eddin; Eddin, Amer Nour; Shirmohammadi, Shervin

    2017-01-01

    Attention Deficit Hyperactivity Disorder (ADHD) and Attention Deficit Disorders (ADD) are two of the most spread mental disorders characterized by the lack of attention and focus. One way to measure focus is through Electroencephalogram (EEG) signals that can be read using the new wireless EEG reading devices often used by Brain-computer Interface (BCI) researchers. In parallel, serious games have been recently providing opportunities for the rehabilitation of various cognitive and emotional ...

  11. Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task

    Directory of Open Access Journals (Sweden)

    Lorraine Perronnet

    2017-04-01

    Full Text Available Neurofeedback is a promising tool for brain rehabilitation and peak performance training. Neurofeedback approaches usually rely on a single brain imaging modality such as EEG or fMRI. Combining these modalities for neurofeedback training could allow to provide richer information to the subject and could thus enable him/her to achieve faster and more specific self-regulation. Yet unimodal and multimodal neurofeedback have never been compared before. In the present work, we introduce a simultaneous EEG-fMRI experimental protocol in which participants performed a motor-imagery task in unimodal and bimodal NF conditions. With this protocol we were able to compare for the first time the effects of unimodal EEG-neurofeedback and fMRI-neurofeedback versus bimodal EEG-fMRI-neurofeedback by looking both at EEG and fMRI activations. We also propose a new feedback metaphor for bimodal EEG-fMRI-neurofeedback that integrates both EEG and fMRI signal in a single bi-dimensional feedback (a ball moving in 2D. Such a feedback is intended to relieve the cognitive load of the subject by presenting the bimodal neurofeedback task as a single regulation task instead of two. Additionally, this integrated feedback metaphor gives flexibility on defining a bimodal neurofeedback target. Participants were able to regulate activity in their motor regions in all NF conditions. Moreover, motor activations as revealed by offline fMRI analysis were stronger during EEG-fMRI-neurofeedback than during EEG-neurofeedback. This result suggests that EEG-fMRI-neurofeedback could be more specific or more engaging than EEG-neurofeedback. Our results also suggest that during EEG-fMRI-neurofeedback, participants tended to regulate more the modality that was harder to control. Taken together our results shed first light on the specific mechanisms of bimodal EEG-fMRI-neurofeedback and on its added-value as compared to unimodal EEG-neurofeedback and fMRI-neurofeedback.

  12. Objective Audiometry using Ear-EEG

    DEFF Research Database (Denmark)

    Christensen, Christian Bech; Kidmose, Preben

    Recently, a novel electroencephalographic (EEG) method called ear-EEG [1], that enable recording of auditory evoked potentials (AEPs) from a personalized earpiece was introduced. Initial investigations show that well established AEPs, such as ASSR and P1-N1-P2 complex can be observed from ear-EEG...

  13. Hypnagogic EEG stages and polysomnogram

    OpenAIRE

    HAYASHI, Mitsuo; HIBINO, Kenji; HORI, Tadao

    1999-01-01

    The aim of this study is to show the polysomnogram of hypnagogic period. Sixteen subjects slept for two nights. Their EEGs (Fz, Cz, Pz, Oz), horizontal and vertical EOGs, submentalis EMG, thoracic and abdominal respiration were recorded. They pressed a button when pip tones (1000Hz, 50dB, max duration : 5s, ISI : 30-90s) were presented, and reported their psychological experiences, According to Hori et al. (1994), the hypnagogic EEGs just 5s before the pip tones were classified into 9 stages,...

  14. Independent component analysis of gait-related movement artifact recorded using EEG electrodes during treadmill walking.

    Directory of Open Access Journals (Sweden)

    Kristine Lynne Snyder

    2015-12-01

    Full Text Available There has been a recent surge in the use of electroencephalography (EEG as a tool for mobile brain imaging due to its portability and fine time resolution. When EEG is combined with independent component analysis (ICA and source localization techniques, it can model electrocortical activity as arising from temporally independent signals located in spatially distinct cortical areas. However, for mobile tasks, it is not clear how movement artifacts influence ICA and source localization. We devised a novel method to collect pure movement artifact data (devoid of any electrophysiological signals with a 256-channel EEG system. We first blocked true electrocortical activity using a silicone swim cap. Over the silicone layer, we placed a simulated scalp with electrical properties similar to real human scalp. We collected EEG movement artifact signals from ten healthy, young subjects wearing this setup as they walked on a treadmill at speeds from 0.4-1.6 m/s. We performed ICA and dipole fitting on the EEG movement artifact data to quantify how accurately these methods would identify the artifact signals as non-neural. ICA and dipole fitting accurately localized 99% of the independent components in non-neural locations or lacked dipolar characteristics. The remaining 1% of sources had locations within the brain volume and low residual variances, but had topographical maps, power spectra, time courses, and event related spectral perturbations typical of non-neural sources. Caution should be exercised when interpreting ICA for data that includes semi-periodic artifacts including artifact arising from human walking. Alternative methods are needed for the identification and separation of movement artifact in mobile EEG signals, especially methods that can be performed in real time. Separating true brain signals from motion artifact could clear the way for EEG brain computer interfaces for assistance during mobile activities, such as walking.

  15. EEG INTERFACE MODULE FOR COGNITIVE ASSESSMENT THROUGH NEUROPHYSIOLOGIC TESTS

    Directory of Open Access Journals (Sweden)

    Kundan Lal Verma

    2014-12-01

    Full Text Available The cognitive signal processing is one of the important interdisciplinary field came from areas of life sciences, psychology, psychiatry, engi-neering, mathematics, physics, statistics and many other fields of research. Neurophysiologic tests are utilized to assess and treat brain injury, dementia, neurological conditions, and useful to investigate psychological and psychiatric disorders. This paper presents an ongoing research work on development of EEG interface device based on the principles of cognitive assessments and instrumentation. The method proposed engineering and science of cogni-tive signal processing in case of brain computer in-terface based neurophysiologic tests. The future scope of this study is to build a low cost EEG device for various clinical and pre-clinical applications with specific emphasis to measure the effect of cognitive action on human brain.

  16. Integration of EEG source imaging and fMRI during continuous viewing of natural movies.

    Science.gov (United States)

    Whittingstall, Kevin; Bartels, Andreas; Singh, Vanessa; Kwon, Soyoung; Logothetis, Nikos K

    2010-10-01

    Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are noninvasive neuroimaging tools which can be used to measure brain activity with excellent temporal and spatial resolution, respectively. By combining the neural and hemodynamic recordings from these modalities, we can gain better insight into how and where the brain processes complex stimuli, which may be especially useful in patients with different neural diseases. However, due to their vastly different spatial and temporal resolutions, the integration of EEG and fMRI recordings is not always straightforward. One fundamental obstacle has been that paradigms used for EEG experiments usually rely on event-related paradigms, while fMRI is not limited in this regard. Therefore, here we ask whether one can reliably localize stimulus-driven EEG activity using the continuously varying feature intensities occurring in natural movie stimuli presented over relatively long periods of time. Specifically, we asked whether stimulus-driven aspects in the EEG signal would be co-localized with the corresponding stimulus-driven BOLD signal during free viewing of a movie. Secondly, we wanted to integrate the EEG signal directly with the BOLD signal, by estimating the underlying impulse response function (IRF) that relates the BOLD signal to the underlying current density in the primary visual area (V1). We made sequential fMRI and 64-channel EEG recordings in seven subjects who passively watched 2-min-long segments of a James Bond movie. To analyze EEG data in this natural setting, we developed a method based on independent component analysis (ICA) to reject EEG artifacts due to blinks, subject movement, etc., in a way unbiased by human judgment. We then calculated the EEG source strength of this artifact-free data at each time point of the movie within the entire brain volume using low-resolution electromagnetic tomography (LORETA). This provided for every voxel in the brain (i.e., in 3D space) an

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

    Science.gov (United States)

    2012-01-01

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

  18. Emotional responses as independent components in EEG

    DEFF Research Database (Denmark)

    Jensen, Camilla Birgitte Falk; Petersen, Michael Kai; Larsen, Jakob Eg

    2014-01-01

    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...... or unpleasant images; early posterior negativity (EPN) and late positive potential (LPP). Recent studies suggest that several time course components may be modulated by emotional content in images or text. However these neural signatures are characterized by small voltage changes that would be highly...... by emotional content. We propose that similar approaches to spatial filtering might allow us to retrieve more robust signals in real life mobile usage scenarios, and potentially facilitate design of cognitive interfaces that adapt the selection of media to our emotional responses....

  19. Analysis of tractable distortion metrics for EEG compression applications

    International Nuclear Information System (INIS)

    Bazán-Prieto, Carlos; Blanco-Velasco, Manuel; Cruz-Roldán, Fernando; Cárdenas-Barrera, Julián

    2012-01-01

    Coding distortion in lossy electroencephalographic (EEG) signal compression methods is evaluated through tractable objective criteria. The percentage root-mean-square difference, which is a global and relative indicator of the quality held by reconstructed waveforms, is the most widely used criterion. However, this parameter does not ensure compliance with clinical standard guidelines that specify limits to allowable noise in EEG recordings. As a result, expert clinicians may have difficulties interpreting the resulting distortion of the EEG for a given value of this parameter. Conversely, the root-mean-square error is an alternative criterion that quantifies distortion in understandable units. In this paper, we demonstrate that the root-mean-square error is better suited to control and to assess the distortion introduced by compression methods. The experiments conducted in this paper show that the use of the root-mean-square error as target parameter in EEG compression allows both clinicians and scientists to infer whether coding error is clinically acceptable or not at no cost for the compression ratio. (paper)

  20. Automatic Removal of Physiological Artifacts in EEG: The Optimized Fingerprint Method for Sports Science Applications.

    Science.gov (United States)

    Stone, David B; Tamburro, Gabriella; Fiedler, Patrique; Haueisen, Jens; Comani, Silvia

    2018-01-01

    Data contamination due to physiological artifacts such as those generated by eyeblinks, eye movements, and muscle activity continues to be a central concern in the acquisition and analysis of electroencephalo