Identifying the effects of microsaccades in tripolar EEG signals.

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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.

Quantitative change of EEG and respiration signals during mindfulness meditation

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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

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

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

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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.

Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal

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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.

Self-paced brain-computer interface control of ambulation in a virtual reality environment

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Wang, Po T.; King, Christine E.; Chui, Luis A.; Do, An H.; Nenadic, Zoran

2012-10-01

Objective. Spinal cord injury (SCI) often leaves affected individuals unable to ambulate. Electroencephalogram (EEG) based brain-computer interface (BCI) controlled lower extremity prostheses may restore intuitive and able-body-like ambulation after SCI. To test its feasibility, the authors developed and tested a novel EEG-based, data-driven BCI system for intuitive and self-paced control of the ambulation of an avatar within a virtual reality environment (VRE). Approach. Eight able-bodied subjects and one with SCI underwent the following 10-min training session: subjects alternated between idling and walking kinaesthetic motor imageries (KMI) while their EEG were recorded and analysed to generate subject-specific decoding models. Subjects then performed a goal-oriented online task, repeated over five sessions, in which they utilized the KMI to control the linear ambulation of an avatar and make ten sequential stops at designated points within the VRE. Main results. The average offline training performance across subjects was 77.2±11.0%, ranging from 64.3% (p = 0.001 76) to 94.5% (p = 6.26×10-23), with chance performance being 50%. The average online performance was 8.5±1.1 (out of 10) successful stops and 303±53 s completion time (perfect = 211 s). All subjects achieved performances significantly different than those of random walk (p prosthesis systems may be feasible.

Bluetooth Communication Interface for EEG Signal Recording in Hyperbaric Chambers.

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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.

A real-time classification algorithm for EEG-based BCI driven by self-induced emotions.

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Iacoviello, Daniela; Petracca, Andrea; Spezialetti, Matteo; Placidi, Giuseppe

2015-12-01

The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. The obtained

Analyzing Electroencephalogram Signal Using EEG Lab

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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.

Corrected Four-Sphere Head Model for EEG Signals.

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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.

Corrected Four-Sphere Head Model for EEG Signals

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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.

Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm

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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.

Modulation of EEG Theta Band Signal Complexity by Music Therapy

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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.

Self-Paced Instruction: Hello, Education

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Leuba, Richard J.; Flammer, Gordon H.

1975-01-01

Answers criticisms of self-paced instruction (SPI) by citing advantages of SPI over lecture methods. Concludes that criticisms of SPI are useful since they indicate in which areas further research should be conducted to improve this method of instruction. (MLH)

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

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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.

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

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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. 

Self-Paced Physics, Segments 37-40.

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New York Inst. of Tech., Old Westbury.

Four study segments of the Self-Paced Physics Course materials are presented in this eighth problems and solutions book used as a part of course assignments. The content is related to magnetic induction, Faraday's law, induced currents, Lenz's law, induced electromotive forces, time-varying magnetic fields, self-inductance, inductors,…

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

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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

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

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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°.

Engagement Assessment Using EEG Signals

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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.

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

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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.

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

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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.

Self-Paced (Asynchronous BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic

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Robert Leeb

2007-09-01

Full Text Available The aim of the present study was to demonstrate for the first time that brain waves can be used by a tetraplegic to control movements of his wheelchair in virtual reality (VR. In this case study, the spinal cord injured (SCI subject was able to generate bursts of beta oscillations in the electroencephalogram (EEG by imagination of movements of his paralyzed feet. These beta oscillations were used for a self-paced (asynchronous brain-computer interface (BCI control based on a single bipolar EEG recording. The subject was placed inside a virtual street populated with avatars. The task was to “go” from avatar to avatar towards the end of the street, but to stop at each avatar and talk to them. In average, the participant was able to successfully perform this asynchronous experiment with a performance of 90%, single runs up to 100%.

Predicting Successful Completion Using Student Delay Indicators in Undergraduate Self-Paced Online Courses

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Lim, Janine M.

2016-01-01

Self-paced online courses meet flexibility and learning needs of many students, but skepticism persists regarding the quality and the tendency for students to procrastinate in self-paced courses. Research is needed to understand procrastination and delay patterns of students in online self-paced courses to predict successful completion and…

The Efficacy of Self-Paced Study in Multitrial Learning

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de Jonge, Mario; Tabbers, Huib K.; Pecher, Diane; Jang, Yoonhee; Zeelenberg, René

2015-01-01

In 2 experiments we investigated the efficacy of self-paced study in multitrial learning. In Experiment 1, native speakers of English studied lists of Dutch-English word pairs under 1 of 4 imposed fixed presentation rate conditions (24 × 1 s, 12 × 2 s, 6 × 4 s, or 3 × 8 s) and a self-paced study condition. Total study time per list was equated for…

Hyperspherical Manifold for EEG Signals of Epileptic Seizures

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Tahir Ahmad

2012-01-01

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

Transfer function between EEG and BOLD signals of epileptic activity

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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.

Decoding Individual Finger Movements from One Hand Using Human EEG Signals

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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

A New Approach to Eliminate High Amplitude Artifacts in EEG Signals

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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.

Seizure classification in EEG signals utilizing Hilbert-Huang transform

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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.

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.

Pacing and Self-regulation: Important Skills for Talent Development in Endurance Sports.

Science.gov (United States)

Elferink-Gemser, Marije T; Hettinga, Florentina J

2017-07-01

Pacing has been characterized as a multifaceted goal-directed process of decision making in which athletes need to decide how and when to invest their energy during the race, a process essential for optimal performance. Both physiological and psychological characteristics associated with adequate pacing and performance are known to develop with age. Consequently, the multifaceted skill of pacing might be under construction throughout adolescence, as well. Therefore, the authors propose that the complex skill of pacing is a potential important performance characteristic for talented youth athletes that needs to be developed throughout adolescence. To explore whether pacing is a marker for talent and how talented athletes develop this skill in middle-distance and endurance sports, they aim to bring together literature on pacing and literature on talent development and self-regulation of learning. Subsequently, by applying the cyclical process of self-regulation to pacing, they propose a practical model for the development of performance in endurance sports in youth athletes. Not only is self-regulation essential throughout the process of reaching the long-term goal of athletic excellence, but it also seems crucial for the development of pacing skills within a race and the development of a refined performance template based on previous experiences. Coaches and trainers are advised to incorporate pacing as a performance characteristic in their talent-development programs by stimulating their athletes to reflect, plan, monitor, and evaluate their races on a regular basis to build performance templates and, as such, improve their performance.

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.

Self-Paced Physics, Segments 24-27.

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New York Inst. of Tech., Old Westbury.

Four study segments of the Self-Paced Physics Course materials are presented in this fifth problems and solutions book used as a part of student course work. The subject matter is related to work in electric fields, potential differences, parallel plates, electric potential energies, potential gradients, capacitances, and capacitor circuits.…

Self-Paced Physics, Segments 28-31.

Science.gov (United States)

New York Inst. of Tech., Old Westbury.

Four study segments of the Self-Paced Physics Course materials are presented in this sixth problems and solutions book used as a part of student course work. The subject matter is related to electric currents, current densities, resistances, Ohm's law, voltages, Joule heating, electromotive forces, single loop circuits, series and parallel…

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)

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.

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

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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.

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

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.

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

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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.

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.

Self-Paced Physics, Segments 19-23.

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New York Inst. of Tech., Old Westbury.

Five study segments of the Self-Paced Physics Course materials are presented in this fourth problems and solutions book used as a part of student course work. The subject matter is related to electric charges, insulators, Coulomb's law, electric fields, lines of force, solid angles, conductors, motion of charged particles, dipoles, electric flux,…

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...

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.

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

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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.

Optimizing classroom instruction through self-paced learning prototype

Directory of Open Access Journals (Sweden)

Romiro Gordo Bautista

2015-09-01

Full Text Available This study investigated the learning impact of self-paced learning prototype in optimizing classroom instruction towards students’ learning in Chemistry. Two sections of 64 Laboratory High School students in Chemistry were used as subjects of the study. The Quasi-Experimental and Correlation Research Design was used in the study: a pre-test was conducted, scored and analyzed which served as the basis in determining the initial learning schema of the respondents. A questionnaire was adopted to find the learning motivation of the students in science. Using Pearson-r correlation, it was found out that there is a highly significant relationship between their internal drive and their academic performance. Moreover, a post-test was conducted after self-paced learning prototype was used in the development of select topics in their curricular plot. It was found out that the students who experienced the self-paced learning prototype performed better in their academic performance as evidenced by the difference of their mean post-test results. ANCOVA results on the post-test mean scores of the respondents were utilized in establishing the causal-effect of the learning prototype to the academic performance of the students in Chemistry. A highly significant effect on their academic performance (R-square value of 70.7% and significant interaction of the models to the experimental grouping and mental abilities of the respondents are concluded in the study.

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

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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.

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

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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.

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

Artifact removal from EEG signals using adaptive filters in cascade

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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.

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

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.

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

[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.

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.

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

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.

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

Design Recommendations for Self-Paced Online Faculty Development Courses

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Rizzuto, Melissa

2017-01-01

An increased need for self-paced, online professional development opportunities in higher education has emerged from a variety of factors including dispersed geographic locations of faculty, full teaching loads, and institutional evaluation requirements. This article is a report of the examination of the design and evaluation of a self-paced…

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

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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

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 ...

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)

Social Interaction in Self-paced Distance Education

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Terry Anderson

2015-02-01

Full Text Available In this paper we present a case study of a self-paced university course that was originally designed to support independent, self-paced study at distance. We developed a social media intervention, in design-based research terms, that allows these independent students to contribute archived content to enhance the course, to engage in discussions with other students and to share as little or as much personal information with each other as they wished. We describe the learning design for the intervention and present survey data of student and tutor perception of value and content analysis of the archived contributions. The results indicate that the intervention was positively received by tutors and by the majority (but not all students and that the archive created by the students’ contributions was adding value to the course. We conclude that the intervention was a modest, yet manageable example of a learning enhancement to a traditional cognitive-behavioral, course that has positive impact and potential with little negative impact on workload.

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

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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.

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

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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.

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.

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

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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

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

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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.

Single Trial Classification of Evoked EEG Signals Due to RGB Colors

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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.

MCTS self-paced training kit (exam 70-680) configuring Windows 7

CERN Document Server

McLean, Ian

2010-01-01

NOTE: The most recent printings of this title incorporate corrections to errors found in the earlier printings. This Self-Paced Training Kit is designed to help maximize your performance on 70-680, the required exam for the Microsoft® Certified Technology Specialist (MCTS): Windows 7, Configuration certification.This 2-in-1 kit includes the official Microsoft study guide, plus practice tests on CD to help you assess your skills. It comes packed with the tools and features exam candidates want most-including in-depth, self-paced training based on final exam content; rigorous, objective-by-obj

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

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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.

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

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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

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 ...

Optimizing Classroom Instruction through Self-Paced Learning Prototype

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Bautista, Romiro G.

2015-01-01

This study investigated the learning impact of self-paced learning prototype in optimizing classroom instruction towards students' learning in Chemistry. Two sections of 64 Laboratory High School students in Chemistry were used as subjects of the study. The Quasi-Experimental and Correlation Research Design was used in the study: a pre-test was…

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

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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.

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

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.

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

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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.

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

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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.

Challenge of Helping Introductory Physics Students Transfer Their Learning by Engaging with a Self-Paced Learning Tutorial

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Emily Megan Marshman

2018-03-01

Full Text Available With advances in digital technology, research-validated self-paced learning tools can play an increasingly important role in helping students with diverse backgrounds become good problem solvers and independent learners. Thus, it is important to ensure that all students engage with self-paced learning tools effectively in order to learn the content deeply, develop good problem-solving skills, and transfer their learning from one context to another. Here, we first provide an overview of a holistic framework for engaging students with self-paced learning tools so that they can transfer their learning to solve novel problems. The framework not only takes into account the features of the self-paced learning tools but also how those tools are implemented, the extent to which the tools take into account student characteristics, and whether factors related to students’ social environments are accounted for appropriately in the implementation of those tools. We then describe an investigation in which we interpret the findings using the framework. In this study, a research-validated self-paced physics tutorial was implemented in both controlled one-on-one interviews and in large enrollment, introductory calculus-based physics courses as a self-paced learning tool. We find that students who used the tutorial in a controlled one-on-one interview situation performed significantly better on transfer problems than those who used it as a self-paced learning tool in the large-scale implementation. The findings suggest that critically examining and taking into account how the self-paced tools are implemented and incentivized, student characteristics including their self-regulation and time-management skills, and social and environmental factors can greatly impact the extent and manner in which students engage with these learning tools. Getting buy in from students about the value of these tools and providing appropriate support while implementing them is

Challenge of Engaging All Students via Self-Paced Interactive Electronic Learning Tutorials for Introductory Physics

Science.gov (United States)

DeVore, Seth; Marshman, Emily; Singh, Chandralekha

2017-01-01

As research-based, self-paced electronic learning tools become increasingly available, a critical issue educators encounter is implementing strategies to ensure that all students engage with them as intended. Here, we first discuss the effectiveness of electronic learning tutorials as self-paced learning tools in large enrollment brick and mortar…

EEG Controlled Wheelchair

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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.

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

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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.

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.

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

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

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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

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

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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

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

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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.

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

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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

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

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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

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

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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.

Challenge of engaging all students via self-paced interactive electronic learning tutorials for introductory physics

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Seth DeVore

2017-05-01

Full Text Available As research-based, self-paced electronic learning tools become increasingly available, a critical issue educators encounter is implementing strategies to ensure that all students engage with them as intended. Here, we first discuss the effectiveness of electronic learning tutorials as self-paced learning tools in large enrollment brick and mortar introductory physics courses and then propose a framework for helping students engage effectively with the learning tools. The tutorials were developed via research in physics education and were found to be effective for a diverse group of introductory physics students in one-on-one implementation. Instructors encouraged the use of these tools in a self-paced learning environment by telling students that they would be helpful for solving the assigned homework problems and that the underlying physics principles in the tutorial problems would be similar to those in the in-class quizzes (which we call paired problems. We find that many students in the courses in which these interactive electronic learning tutorials were assigned as a self-study tool performed poorly on the paired problems. In contrast, a majority of student volunteers in one-on-one implementation greatly benefited from the tutorials and performed well on the paired problems. The significantly lower overall performance on paired problems administered as an in-class quiz compared to the performance of student volunteers who used the research-based tutorials in one-on-one implementation suggests that many students enrolled in introductory physics courses did not effectively engage with the tutorials outside of class and may have only used them superficially. The findings suggest that many students in need of out-of-class remediation via self-paced learning tools may have difficulty motivating themselves and may lack the self-regulation and time-management skills to engage effectively with tools specially designed to help them learn at their

Challenge of engaging all students via self-paced interactive electronic learning tutorials for introductory physics

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DeVore, Seth; Marshman, Emily; Singh, Chandralekha

2017-06-01

As research-based, self-paced electronic learning tools become increasingly available, a critical issue educators encounter is implementing strategies to ensure that all students engage with them as intended. Here, we first discuss the effectiveness of electronic learning tutorials as self-paced learning tools in large enrollment brick and mortar introductory physics courses and then propose a framework for helping students engage effectively with the learning tools. The tutorials were developed via research in physics education and were found to be effective for a diverse group of introductory physics students in one-on-one implementation. Instructors encouraged the use of these tools in a self-paced learning environment by telling students that they would be helpful for solving the assigned homework problems and that the underlying physics principles in the tutorial problems would be similar to those in the in-class quizzes (which we call paired problems). We find that many students in the courses in which these interactive electronic learning tutorials were assigned as a self-study tool performed poorly on the paired problems. In contrast, a majority of student volunteers in one-on-one implementation greatly benefited from the tutorials and performed well on the paired problems. The significantly lower overall performance on paired problems administered as an in-class quiz compared to the performance of student volunteers who used the research-based tutorials in one-on-one implementation suggests that many students enrolled in introductory physics courses did not effectively engage with the tutorials outside of class and may have only used them superficially. The findings suggest that many students in need of out-of-class remediation via self-paced learning tools may have difficulty motivating themselves and may lack the self-regulation and time-management skills to engage effectively with tools specially designed to help them learn at their own pace. We

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...

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

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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

Breath pacing system and method for pacing the respiratory activity of a subject

NARCIS (Netherlands)

2016-01-01

To provide a breath pacing system and a corresponding method for pacing the respiratory activity of a subject that provide the possibility to adapt the output signal to the respiration characteristics of the subject automatically and effectively a breath pacing system (10) for pacing the respiratory

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

Continuous EEG signal analysis for asynchronous BCI application.

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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.

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

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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.

Self-Paced Prioritized Curriculum Learning With Coverage Penalty in Deep Reinforcement Learning.

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Ren, Zhipeng; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Zhipeng Ren; Daoyi Dong; Huaxiong Li; Chunlin Chen; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Ren, Zhipeng

2018-06-01

In this paper, a new training paradigm is proposed for deep reinforcement learning using self-paced prioritized curriculum learning with coverage penalty. The proposed deep curriculum reinforcement learning (DCRL) takes the most advantage of experience replay by adaptively selecting appropriate transitions from replay memory based on the complexity of each transition. The criteria of complexity in DCRL consist of self-paced priority as well as coverage penalty. The self-paced priority reflects the relationship between the temporal-difference error and the difficulty of the current curriculum for sample efficiency. The coverage penalty is taken into account for sample diversity. With comparison to deep Q network (DQN) and prioritized experience replay (PER) methods, the DCRL algorithm is evaluated on Atari 2600 games, and the experimental results show that DCRL outperforms DQN and PER on most of these games. More results further show that the proposed curriculum training paradigm of DCRL is also applicable and effective for other memory-based deep reinforcement learning approaches, such as double DQN and dueling network. All the experimental results demonstrate that DCRL can achieve improved training efficiency and robustness for deep reinforcement learning.

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

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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.

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

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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.

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

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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.

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

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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.

The Effects of Self-Paced Blended Learning of Mathematics

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Balentyne, Phoebe; Varga, Mary Alice

2016-01-01

As online and blended learning gain more popularity in education, it becomes more important to understand their effects on student learning. The purpose of this study was to explore the effects of self-paced blended learning of mathematics on the attitudes and achievement of 26 high ability middle school students, and investigate the relationship…

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. ...

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

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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

A Closer Look at Split Visual Attention in System- and Self-Paced Instruction in Multimedia Learning

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Schmidt-Weigand, Florian; Kohnert, Alfred; Glowalla, Ulrich

2010-01-01

Two experiments examined visual attention distribution in learning from text and pictures. Participants watched a 16-step multimedia instruction on the formation of lightning. In Experiment 1 (N=90) the instruction was system-paced (fast, medium, slow pace), while it was self-paced in Experiment 2 (N=31). In both experiments the text modality was…

Effect of Music Tempo on Attentional Focus and Perceived Exertion during Self-selected Paced Walking

OpenAIRE

SILVA, ALDO COELHO; DOS SANTOS FERREIRA, SANDRO; ALVES, RAGAMI CHAVES; FOLLADOR, LUCIO; DA SILVA, SERGIO GREGORIO

2016-01-01

This study investigated the influence of music on the rating of perceived exertion (RPE) and attentional focus during walking at a self-selected pace. Fifteen overweight and obese women volunteered to participate in the study. They underwent four sessions: the first for incremental maximal test and anthropometric measurement followed by three experimental sessions. After the first session, they were exposed to three 30-minute walking sessions at a self-selected pace in a counterbalanced order...

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

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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.

Altering Pace Control and Pace Regulation: Attentional Focus Effects during Running.

Science.gov (United States)

Brick, Noel E; Campbell, Mark J; Metcalfe, Richard S; Mair, Jacqueline L; Macintyre, Tadhg E

2016-05-01

To date, there are no published studies directly comparing self-controlled (SC) and externally controlled (EC) pace endurance tasks. However, previous research suggests pace control may impact on cognitive strategy use and effort perceptions. The primary aim of this study was to investigate the effects of manipulating perception of pace control on attentional focus, physiological, and psychological outcomes during running. The secondary aim was to determine the reproducibility of self-paced running performance when regulated by effort perceptions. Twenty experienced endurance runners completed four 3-km time trials on a treadmill. Subjects completed two SC pace trials, one perceived exertion clamped (PE) trial, and one EC pace time trial. PE and EC were completed in a counterbalanced order. Pacing strategy for EC and perceived exertion instructions for PE replicated the subjects' fastest SC time trial. Subjects reported a greater focus on cognitive strategies such as relaxing and optimizing running action during EC than during SC. The mean HR was 2% lower during EC than that during SC despite an identical pacing strategy. Perceived exertion did not differ between the three conditions. However, increased internal sensory monitoring coincided with elevated effort perceptions in some subjects during EC and a 10% slower completion time for PE (13.0 ± 1.6 min) than that for SC (11.8 ± 1.2 min). Altering pace control and pace regulation impacted on attentional focus. External control over pacing may facilitate performance, particularly when runners engage attentional strategies conducive to improved running efficiency. However, regulating pace based on effort perceptions alone may result in excessive monitoring of bodily sensations and a slower running speed. Accordingly, attentional focus interventions may prove beneficial for some athletes to adopt task-appropriate attentional strategies to optimize performance.

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

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 ...

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.

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.

Effects of wind application on thermal perception and self-paced performance

NARCIS (Netherlands)

Teunissen, L.P.J.; Haan, A. de; Koning, J.J. de; Daanen, H.A.M.

2013-01-01

Physiological and perceptual effects of wind cooling are often intertwined and have scarcely been studied in self-paced exercise. Therefore, we aimed to investigate (1) the independent perceptual effect of wind cooling and its impact on performance and (2) the responses to temporary wind cooling

Spontaneous Velocity Effect of Musical Expression on Self-Paced Walking

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Buhmann, Jeska; Desmet, Frank; Moens, Bart; Van Dyck, Edith; Leman, Marc

2016-01-01

The expressive features of music can influence the velocity of walking. So far, studies used instructed (and intended) synchronization. But is this velocity effect still present with non-instructed (spontaneous) synchronization? To figure that out, participants were instructed to walk in their own comfort tempo on an indoor track, first in silence and then with tempo-matched music. We compared velocities of silence and music conditions. The results show that some music has an activating influence, increasing velocity and motivation, while other music has a relaxing influence, decreasing velocity and motivation. The influence of musical expression on the velocity of self-paced walking can be predicted with a regression model using only three sonic features explaining 56% of the variance. Phase-coherence between footfall and beat did not contribute to the velocity effect, due to its implied fixed pacing. The findings suggest that the velocity effect depends on vigor entrainment that influences both stride length and pacing. Our findings are relevant for preventing injuries, for gait improvement in walking rehabilitation, and for improving performance in sports activities. PMID:27167064

Spontaneous Velocity Effect of Musical Expression on Self-Paced Walking.

Science.gov (United States)

Buhmann, Jeska; Desmet, Frank; Moens, Bart; Van Dyck, Edith; Leman, Marc

2016-01-01

The expressive features of music can influence the velocity of walking. So far, studies used instructed (and intended) synchronization. But is this velocity effect still present with non-instructed (spontaneous) synchronization? To figure that out, participants were instructed to walk in their own comfort tempo on an indoor track, first in silence and then with tempo-matched music. We compared velocities of silence and music conditions. The results show that some music has an activating influence, increasing velocity and motivation, while other music has a relaxing influence, decreasing velocity and motivation. The influence of musical expression on the velocity of self-paced walking can be predicted with a regression model using only three sonic features explaining 56% of the variance. Phase-coherence between footfall and beat did not contribute to the velocity effect, due to its implied fixed pacing. The findings suggest that the velocity effect depends on vigor entrainment that influences both stride length and pacing. Our findings are relevant for preventing injuries, for gait improvement in walking rehabilitation, and for improving performance in sports activities.

Spontaneous Velocity Effect of Musical Expression on Self-Paced Walking.

Directory of Open Access Journals (Sweden)

Jeska Buhmann

Full Text Available The expressive features of music can influence the velocity of walking. So far, studies used instructed (and intended synchronization. But is this velocity effect still present with non-instructed (spontaneous synchronization? To figure that out, participants were instructed to walk in their own comfort tempo on an indoor track, first in silence and then with tempo-matched music. We compared velocities of silence and music conditions. The results show that some music has an activating influence, increasing velocity and motivation, while other music has a relaxing influence, decreasing velocity and motivation. The influence of musical expression on the velocity of self-paced walking can be predicted with a regression model using only three sonic features explaining 56% of the variance. Phase-coherence between footfall and beat did not contribute to the velocity effect, due to its implied fixed pacing. The findings suggest that the velocity effect depends on vigor entrainment that influences both stride length and pacing. Our findings are relevant for preventing injuries, for gait improvement in walking rehabilitation, and for improving performance in sports activities.

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

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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.

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

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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.

A positron emission tomography study of self-paced finger movements at different frequencies

International Nuclear Information System (INIS)

Kawashima, R.; Inoue, K.; Sugiura, M.; Okada, K.; Ogawa, A.; Fukuda, H.

1999-01-01

Regional cerebral blood flow was measured in six right-handed volunteers using positron emission tomography during tasks involving repetitive self-paced finger tapping at five different frequencies. The contralateral primary sensorimotor cortex, the pre-supplementary motor area and the cingulate motor area showed significant activation during self-paced finger tapping tasks, compared with the resting state. A positive correlation between the regional cerebral blood flow and the movement frequency was found only in the primary sensorimotor cortex. In the pre-supplementary motor area and the cingulate motor area, however, activity increased when the subject employed movement frequencies faster or slower than his own pace. The same tendency was noted with respect to the relative variability of the inter-tapping interval.The results therefore indicate that the activity of the pre-supplementary motor area and the cingulate motor area may well be related to the increased difficulty in motor control rather than to the execution of the movement itself. (Copyright (c) 1999 Elsevier Science B.V., Amsterdam. All rights reserved.)

Influence of music on maximal self-paced running performance and passive post-exercise recovery rate.

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Lee, Sam; Kimmerly, Derek S

2016-01-01

The purpose of this study was to examine the influence of fast tempo music (FM) on self-paced running performance (heart rate, running speed, ratings of perceived exertion), and slow tempo music (SM) on post-exercise heart rate and blood lactate recovery rates. Twelve participants (5 women) completed three randomly assigned conditions: static noise (control), FM and SM. Each condition consisted of self-paced treadmill running, and supine postexercise recovery periods (20 min each). Average running speed, heart rate (HR) and ratings of perceived exertion (RPE) were measured during the treadmill running period, while HR and blood lactate were measured during the recovery period. Listening to FM during exercise resulted in a faster self-selected running speed (10.8±1.7 vs. 9.9±1.4 km•hour-1, Peffect P<0.001) and blood lactate at the end of recovery (2.8±0.4 vs. 4.7±0.8 mmol•L-1, P<0.05). Listening to FM during exercise can increase self-paced intensity without altering perceived exertion levels while listening to SM after exercise can accelerate the recovery rate back to resting levels.

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

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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

Compressive sensing scalp EEG signals: implementations and practical performance.

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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.

a Signal-Tuned Gabor Transform with Application to Eeg Analysis

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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.

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

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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

Children's Use of Self-Paced Slideshows: An Extension of the Video Deficit Effect?

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Sage, Kara D.; Baldwin, Dare

2015-01-01

Past research has established that children typically learn better from live demonstrations than from two-dimensional (2D) media. In the present set of experiments, we investigated the efficacy of a new 2D learning medium-the self-paced slideshow. A primary goal was to determine whether the "video deficit effect" extended to self-paced…

A Pilot Study on the Effects of Transcranial Direct Current Stimulation on Brain Rhythms and Entropy during Self-Paced Finger Movement using the Epoc Helmet

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Mario U. Manto

2017-04-01

Full Text Available Transcranial direct current stimulation (tDCS of the cerebellum is emerging as a novel non-invasive tool to modulate the activity of the cerebellar circuitry. In a single blinded study, we applied anodal tDCS (atDCS of the cerebellum to assess its effects on brain entropy and brain rhythms during self-paced sequential finger movements in a group of healthy volunteers. Although wearable electroencephalogram (EEG systems cannot compete with traditional clinical/laboratory set-ups in terms of accuracy and channel density, they have now reached a sufficient maturity to envision daily life applications. Therefore, the EEG was recorded with a comfortable and easy to wear 14 channels wireless helmet (Epoc headset; electrode location was based on the 10–20 system. Cerebellar neurostimulation modified brain rhythmicity with a decrease in the delta band (electrode F3 and T8, p < 0.05. By contrast, our study did not show any significant change in entropy ratios and laterality coefficients (LC after atDCS of the cerebellum in the 14 channels. The cerebellum is heavily connected with the cerebral cortex including the frontal lobes and parietal lobes via the cerebello-thalamo-cortical pathway. We propose that the effects of anodal stimulation of the cerebellar cortex upon cerebral cortical rhythms are mediated by this key-pathway. Additional studies using high-density EEG recordings and behavioral correlates are now required to confirm our findings, especially given the limited coverage of Epoc headset.

Self-pacing study of faces of different races: metacognitive control over study does not eliminate the cross-race recognition effect.

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Tullis, Jonathan G; Benjamin, Aaron S; Liu, Xiping

2014-08-01

People often recognize same-race faces better than other-race faces. This cross-race effect (CRE) has been proposed to arise in part because learners devote fewer cognitive resources to encode faces of social out-groups. In three experiments, we evaluated whether learners' other-race mnemonic deficits are due to "cognitive disregard" during study and whether this disregard is under metacognitive control. Learners studied each face either for as long as they wanted (the self-paced condition) or for the average time taken by a self-paced learner (the fixed-rate condition). Self-paced learners allocated equal amounts of study time to same-race and other-race faces, and having control over study time did not change the size of the CRE. In the second and third experiments, both self-paced and fixed-rate learners were given instructions to "individuate" other-race faces. Individuation instructions caused self-paced learners to allocate more study time to other-race faces, but this did not significantly reduce the size of the CRE, even for learners who reported extensive contact with other races. We propose that the differential processing that people apply to faces of different races and the subsequent other-race mnemonic deficit are not due to learners' strategic cognitive disregard of other-race faces.

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

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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

Ice slurry ingestion does not enhance self-paced intermittent exercise in the heat.

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Gerrett, N; Jackson, S; Yates, J; Thomas, G

2017-11-01

This study aimed to determine if ice slurry ingestion improved self-paced intermittent exercise in the heat. After a familiarisation session, 12 moderately trained males (30.4 ± 3.4 year, 1.8 ± 0.1 cm, 73.5 ± 14.3 kg, V˙O 2max 58.5 ± 8.1 mL/kg/min) completed two separate 31 min self-paced intermittent protocols on a non-motorised treadmill in 30.9 ± 0.9 °C, 41.1 ± 4.0% RH. Thirty minutes prior to exercise, participants consumed either 7.5 g/kg ice slurry (0.1 ± 0.1 °C) (ICE) or 7.5 g/kg water (23.4 ± 0.9 °C) (CONTROL). Despite reductions in T c (ΔT c : -0.51 ± 0.3 °C, P exercise, ICE did not enhance self-paced intermittent exercise compared to CONTROL. The average speed during the walk (CONTROL: 5.90 ± 1.0 km, ICE: 5.90 ± 1.0 km), jog (CONTROL: 8.89 ± 1.7 km, ICE: 9.11 ± 1.5 km), run (CONTROL: 12.15 ± 1.7 km, ICE: 12.54 ± 1.5 km) and sprint (CONTROL: 17.32 ± 1.3 km, ICE: 17.18 ± 1.4 km) was similar between conditions (P > 0.05). Mean T sk , T b , blood lactate, heart rate and RPE were similar between conditions (P > 0.05). The findings suggest that lowering T c prior to self-paced intermittent exercise does not translate into an improved performance. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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

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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.

The impact of the perception of rhythmic music on self-paced oscillatory movements.

Science.gov (United States)

Peckel, Mathieu; Pozzo, Thierry; Bigand, Emmanuel

2014-01-01

Inspired by theories of perception-action coupling and embodied music cognition, we investigated how rhythmic music perception impacts self-paced oscillatory movements. In a pilot study, we examined the kinematic parameters of self-paced oscillatory movements, walking and finger tapping using optical motion capture. In accordance with biomechanical constraints accounts of motion, we found that movements followed a hierarchical organization depending on the proximal/distal characteristic of the limb used. Based on these findings, we were interested in knowing how and when the perception of rhythmic music could resonate with the motor system in the context of these constrained oscillatory movements. In order to test this, we conducted an experiment where participants performed four different effector-specific movements (lower leg, whole arm and forearm oscillation and finger tapping) while rhythmic music was playing in the background. Musical stimuli consisted of computer-generated MIDI musical pieces with a 4/4 metrical structure. The musical tempo of each song increased from 60 BPM to 120 BPM by 6 BPM increments. A specific tempo was maintained for 20 s before a 2 s transition to the higher tempo. The task of the participant was to maintain a comfortable pace for the four movements (self-paced) while not paying attention to the music. No instruction on whether to synchronize with the music was given. Results showed that participants were distinctively influenced by the background music depending on the movement used with the tapping task being consistently the most influenced. Furthermore, eight strategies put in place by participants to cope with the task were unveiled. Despite not instructed to do so, participants also occasionally synchronized with music. Results are discussed in terms of the link between perception and action (i.e., motor/perceptual resonance). In general, our results give support to the notion that rhythmic music is processed in a motoric

The impact of the perception of rhythmic music on oscillatory self-paced movements

Directory of Open Access Journals (Sweden)

Mathieu ePeckel

2014-09-01

Full Text Available Inspired by theories of perception-action coupling and embodied music cognition, we investigated how rhythmic music perception impacts self-paced oscillatory movements. In a pilot study, we examined the kinematic parameters of self-paced oscillatory movements, walking and finger tapping using optical motion capture. In accordance with biomechanical constraints accounts of motion, we found that movements followed a hierarchical organization depending on the proximal/distal characteristic of the limb used. Based on these findings, we were interested in knowing how and when the perception of rhythmic music could resonate with the motor system in the context of these constrained oscillatory movements. In order to test this, we conducted an experiment where participants performed four different effector-specific movements (lower leg, whole arm and forearm oscillation and finger tapping while rhythmic music was playing in the background. Musical stimuli consisted of computer-generated MIDI musical pieces with a 4/4 metrical structure. The musical tempo of each song increased from 60 BPM to 120 BPM by 6 BPM increments. A specific tempo was maintained for 20s before a 2s transition to the higher tempo. The task of the participant was to maintain a comfortable pace for the four movements (self-paced while not paying attention to the music. No instruction on whether to synchronize with the music was given. Results showed that participants were distinctively influenced by the background music depending on the movement used with the tapping task being consistently the most influenced. Furthermore, eight strategies put in place by participants to cope with task were unveiled. Despite not instructed to do so, participants also occasionally synchronized with music. Results are discussed in terms of the link between perception and action (i.e. motor/perceptual resonance. In general, our results give support to the notion that rhythmic music is processed in a

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.

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

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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.

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

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

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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.

Effects of musical tempo on physiological, affective, and perceptual variables and performance of self-selected walking pace.

Science.gov (United States)

Almeida, Flávia Angélica Martins; Nunes, Renan Felipe Hartmann; Ferreira, Sandro Dos Santos; Krinski, Kleverton; Elsangedy, Hassan Mohamed; Buzzachera, Cosme Franklin; Alves, Ragami Chaves; Gregorio da Silva, Sergio

2015-06-01

[Purpose] This study investigated the effects of musical tempo on physiological, affective, and perceptual responses as well as the performance of self-selected walking pace. [Subjects] The study included 28 adult women between 29 and 51 years old. [Methods] The subjects were divided into three groups: no musical stimulation group (control), and 90 and 140 beats per minute musical tempo groups. Each subject underwent three experimental sessions: involved familiarization with the equipment, an incremental test to exhaustion, and a 30-min walk on a treadmill at a self-selected pace, respectively. During the self-selected walking session, physiological, perceptual, and affective variables were evaluated, and walking performance was evaluated at the end. [Results] There were no significant differences in physiological variables or affective response among groups. However, there were significant differences in perceptual response and walking performance among groups. [Conclusion] Fast music (140 beats per minute) promotes a higher rating of perceived exertion and greater performance in self-selected walking pace without significantly altering physiological variables or affective response.

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.

Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification.

Science.gov (United States)

Lu, Na; Li, Tengfei; Pan, Jinjin; Ren, Xiaodong; Feng, Zuren; Miao, Hongyu

2015-05-01

Electroencephalogram (EEG) provides a non-invasive approach to measure the electrical activities of brain neurons and has long been employed for the development of brain-computer interface (BCI). For this purpose, various patterns/features of EEG data need to be extracted and associated with specific events like cue-paced motor imagery. However, this is a challenging task since EEG data are usually non-stationary time series with a low signal-to-noise ratio. In this study, we propose a novel method, called structure constrained semi-nonnegative matrix factorization (SCS-NMF), to extract the key patterns of EEG data in time domain by imposing the mean envelopes of event-related potentials (ERPs) as constraints on the semi-NMF procedure. The proposed method is applicable to general EEG time series, and the extracted temporal features by SCS-NMF can also be combined with other features in frequency domain to improve the performance of motor imagery classification. Real data experiments have been performed using the SCS-NMF approach for motor imagery classification, and the results clearly suggest the superiority of the proposed method. Comparison experiments have also been conducted. The compared methods include ICA, PCA, Semi-NMF, Wavelets, EMD and CSP, which further verified the effectivity of SCS-NMF. The SCS-NMF method could obtain better or competitive performance over the state of the art methods, which provides a novel solution for brain pattern analysis from the perspective of structure constraint. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Effects of self-paced interval and continuous training on health markers in women

DEFF Research Database (Denmark)

Connolly, Luke J; Bailey, Stephen J; Krustrup, Peter

2017-01-01

PURPOSE: To compare the effects of self-paced high-intensity interval and continuous cycle training on health markers in premenopausal women. METHODS: Forty-five inactive females were randomised to a high-intensity interval training (HIIT; n = 15), continuous training (CT; n = 15) or an inactive...... control (CON; n = 15) group. HIIT performed 5 × 5 min sets comprising repetitions of 30-s low-, 20-s moderate- and 10-s high-intensity cycling with 2 min rest between sets. CT completed 50 min of continuous cycling. Training was completed self-paced, 3 times weekly for 12 weeks. RESULTS: Peak oxygen...... uptake (16 ± 8 and 21 ± 12%), resting heart rate (HR) (-5 ± 9 and -4 ± 7 bpm) and visual and verbal learning improved following HIIT and CT compared to CON (P HIIT (P 

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

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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%.

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

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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.

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 ...

Effects of self-paced interval and continuous training on health markers in women.

Science.gov (United States)

Connolly, Luke J; Bailey, Stephen J; Krustrup, Peter; Fulford, Jonathan; Smietanka, Chris; Jones, Andrew M

2017-11-01

To compare the effects of self-paced high-intensity interval and continuous cycle training on health markers in premenopausal women. Forty-five inactive females were randomised to a high-intensity interval training (HIIT; n = 15), continuous training (CT; n = 15) or an inactive control (CON; n = 15) group. HIIT performed 5 × 5 min sets comprising repetitions of 30-s low-, 20-s moderate- and 10-s high-intensity cycling with 2 min rest between sets. CT completed 50 min of continuous cycling. Training was completed self-paced, 3 times weekly for 12 weeks. Peak oxygen uptake (16 ± 8 and 21 ± 12%), resting heart rate (HR) (-5 ± 9 and -4 ± 7 bpm) and visual and verbal learning improved following HIIT and CT compared to CON (P HIIT (P HIIT and CT, and there were no changes in fasting serum lipids, fasting blood [glucose] or [glucose] during an oral glucose tolerance test following either HIIT or CT (P > 0.05). No outcome variable changed in the CON group (P > 0.05). Twelve weeks of self-paced HIIT and CT were similarly effective at improving cardiorespiratory fitness, resting HR and cognitive function in inactive premenopausal women, whereas blood pressure, submaximal HR, well-being and body mass adaptations were training-type-specific. Both training methods improved established health markers, but the adaptations to HIIT were evoked for a lower time commitment.

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.

Wireless recording systems: from noninvasive EEG-NIRS to invasive EEG devices.

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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%.

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)

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.

A Robust and Self-Paced BCI System Based on a Four Class SSVEP Paradigm: Algorithms and Protocols for a High-Transfer-Rate Direct Brain Communication

Directory of Open Access Journals (Sweden)

Sergio Parini

2009-01-01

Full Text Available In this paper, we present, with particular focus on the adopted processing and identification chain and protocol-related solutions, a whole self-paced brain-computer interface system based on a 4-class steady-state visual evoked potentials (SSVEPs paradigm. The proposed system incorporates an automated spatial filtering technique centred on the common spatial patterns (CSPs method, an autoscaled and effective signal features extraction which is used for providing an unsupervised biofeedback, and a robust self-paced classifier based on the discriminant analysis theory. The adopted operating protocol is structured in a screening, training, and testing phase aimed at collecting user-specific information regarding best stimulation frequencies, optimal sources identification, and overall system processing chain calibration in only a few minutes. The system, validated on 11 healthy/pathologic subjects, has proven to be reliable in terms of achievable communication speed (up to 70 bit/min and very robust to false positive identifications.

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

Resting state glutamate predicts elevated pre-stimulus alpha during self-relatedness: A combined EEG-MRS study on "rest-self overlap".

Science.gov (United States)

Bai, Yu; Nakao, Takashi; Xu, Jiameng; Qin, Pengmin; Chaves, Pedro; Heinzel, Alexander; Duncan, Niall; Lane, Timothy; Yen, Nai-Shing; Tsai, Shang-Yueh; Northoff, Georg

2016-01-01

Recent studies have demonstrated neural overlap between resting state activity and self-referential processing. This "rest-self" overlap occurs especially in anterior cortical midline structures like the perigenual anterior cingulate cortex (PACC). However, the exact neurotemporal and biochemical mechanisms remain to be identified. Therefore, we conducted a combined electroencephalography (EEG)-magnetic resonance spectroscopy (MRS) study. EEG focused on pre-stimulus (e.g., prior to stimulus presentation or perception) power changes to assess the degree to which those changes can predict subjects' perception (and judgment) of subsequent stimuli as high or low self-related. MRS measured resting state concentration of glutamate, focusing on PACC. High pre-stimulus (e.g., prior to stimulus presentation or perception) alpha power significantly correlated with both perception of stimuli judged to be highly self-related and with resting state glutamate concentrations in the PACC. In sum, our results show (i) pre-stimulus (e.g., prior to stimulus presentation or perception) alpha power and resting state glutamate concentration to mediate rest-self overlap that (ii) dispose or incline subjects to assign high degrees of self-relatedness to perceptual stimuli.

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.

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.

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.

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.

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.

Leveraging Technology to Alleviate Student Bottlenecks: The Self-Paced Online Tutorial--Writing (SPOT)

Science.gov (United States)

Moore, Scott D.; Sanchez, Rudolph J.; Inoue, Asao B.; Statham, Russel D.; Zelezny, Lynnette; Covino, William A.

2014-01-01

The Self-Paced Online Tutorial (SPOT) represents the best kind of innovation because it uses digital technologies wisely and because it is based on well-established theory, research, and practice. Extended education plays a pivotal role in the attainment of the California State University's (CSU) vision of providing a high-quality, affordable, and…

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.

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.

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.

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.

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

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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.

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

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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.

Understanding the Cranial Nerves: Evaluation of a Self-Paced Online Module in Optometric Education

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Taylor, Daniel Arnett

2016-01-01

Among the faculty of Southern College of Optometry in Memphis, Tennessee, it is perceived that optometry students often enter their clinical assignments with poor clinical judgment. To address this, "Understanding the Cranial Nerves"--an online-self paced instructional intervention of approximately two hours' duration--was developed. In…

Classification of PolSAR Images Using Multilayer Autoencoders and a Self-Paced Learning Approach

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Wenshuai Chen

2018-01-01

Full Text Available In this paper, a novel polarimetric synthetic aperture radar (PolSAR image classification method based on multilayer autoencoders and self-paced learning (SPL is proposed. The multilayer autoencoders network is used to learn the features, which convert raw data into more abstract expressions. Then, softmax regression is applied to produce the predicted probability distributions over all the classes of each pixel. When we optimize the multilayer autoencoders network, self-paced learning is used to accelerate the learning convergence and achieve a stronger generalization capability. Under this learning paradigm, the network learns the easier samples first and gradually involves more difficult samples in the training process. The proposed method achieves the overall classification accuracies of 94.73%, 94.82% and 78.12% on the Flevoland dataset from AIRSAR, Flevoland dataset from RADARSAT-2 and Yellow River delta dataset, respectively. Such results are comparable with other state-of-the-art methods.

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

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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

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

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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.

Brain Network Analysis from High-Resolution EEG Signals

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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

Let's Walk Outdoors! Self-Paced Walking Outdoors Improves Future Intention to Exercise in Women With Obesity.

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Krinski, Kleverton; Machado, Daniel G S; Lirani, Luciana S; DaSilva, Sergio G; Costa, Eduardo C; Hardcastle, Sarah J; Elsangedy, Hassan M

2017-04-01

In order to examine whether environmental settings influence psychological and physiological responses of women with obesity during self-paced walking, 38 women performed two exercise sessions (treadmill and outdoors) for 30 min, where oxygen uptake, heart rate, ratings of perceived exertion, affect, attentional focus, enjoyment, and future intentions to walk were analyzed. Physiological responses were similar during both sessions. However, during outdoor exercise, participants displayed higher externally focused attention, positive affect, and lower ratings of perceived exertion, followed by greater enjoyment and future intention to participate in outdoor walking. The more externally focused attention predicted greater future intentions to participate in walking. Therefore, women with obesity self-selected an appropriate exercise intensity to improve fitness and health in both environmental settings. Also, self-paced outdoor walking presented improved psychological responses. Health care professionals should consider promoting outdoor forms of exercise to maximize psychological benefits and promote long-term adherence to a physically active lifestyle.

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

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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

Electroencephalograph (EEG) study on self-contemplating image formation

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Meng, Qinglei; Hong, Elliot; Choa, Fow-Sen

2016-05-01

Electroencephalography (EEG) is one of the most widely used electrophysiological monitoring methods and plays a significant role in studies of human brain electrical activities. Default mode network (DMN), is a functional connection of brain regions that are activated while subjects are not in task positive state or not focused on the outside world. In this study, EEG was used for human brain signals recording while all subjects were asked to sit down quietly on a chair with eyes closed and thinking about some parts of their own body, such as left and right hands, left and right ears, lips, nose, and the images of faces that they were familiar with as well as doing some simple mathematical calculation. The time is marker when the image is formed in the subject's mind. By analyzing brain activity maps 300ms right before the time marked instant for each of the 4 wave bands, Delta, Theta, Alpha and Beta waves. We found that for most EEG datasets during this 300ms, Delta wave activity would mostly locate at the frontal lobe or the visual cortex, and the change and movement of activities are slow. Theta wave activity tended to rotate along the edge of cortex either clockwise or counterclockwise. Beta wave behaved like inquiry types of oscillations between any two regions spread over the cortex. Alpha wave activity looks like a mix of the Theta and Beta activities but more close to Theta activity. From the observation we feel that Beta and high Alpha are playing utility role for information inquiry. Theta and low Alpha are likely playing the role of binding and imagination formation in DMN operations.

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

Comparing electro- and mechano-myographic muscle activation patterns in self-paced pediatric gait.

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Plewa, Katherine; Samadani, Ali; Chau, Tom

2017-10-01

Electromyography (EMG) is the standard modality for measuring muscle activity. However, the convenience and availability of low-cost accelerometer-based wearables makes mechanomyography (MMG) an increasingly attractive alternative modality for clinical applications. Literature to date has demonstrated a strong association between EMG and MMG temporal alignment in isometric and isokinetic contractions. However, the EMG-MMG relationship has not been studied in gait. In this study, the concurrence of EMG- and MMG-detected contractions in the tibialis anterior, lateral gastrocnemius, vastus lateralis, and biceps femoris muscles were investigated in children during self-paced gait. Furthermore, the distribution of signal power over the gait cycle was statistically compared between EMG-MMG modalities. With EMG as the reference, muscular contractions were detected based on MMG with balanced accuracies between 88 and 94% for all muscles except the gastrocnemius. MMG signal power differed from that of EMG during certain phases of the gait cycle in all muscles except the biceps femoris. These timing and power distribution differences between the two modalities may in part be related to muscle fascicle length changes that are unique to muscle motion during gait. Our findings suggest that the relationship between EMG and MMG appears to be more complex during gait than in isometric and isokinetic contractions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Processing Advantages of Lexical Bundles: Evidence from Self-Paced Reading and Sentence Recall Tasks

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Tremblay, Antoine; Derwing, Bruce; Libben, Gary; Westbury, Chris

2011-01-01

This article examines the extent to which lexical bundles (LBs; i.e., frequently recurring strings of words that often span traditional syntactic boundaries) are stored and processed holistically. Three self-paced reading experiments compared sentences containing LBs (e.g., "in the middle of the") and matched control sentence fragments (e.g., "in…

Reflection in Learning through a Self-monitoring Device: Design Research on EEG Self-Monitoring during a Study Session

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Eva Durall

2017-04-01

Full Text Available The increasing availability of self-monitoring technologies has created opportunities for gaining awareness about one’s own behavior and reflecting on it. In teaching and learning, there is interest in using self-monitoring technologies, but very few studies have explored the possibilities. In this paper, we present a design study that investigates a technology (called Feeler that guides students to follow a specific learning script, monitors changes in their electroencephalogram (EEG while studying, and later provides visualization of the EEG data. The results are two-fold: (1 the hardware/software prototype and (2 the conclusions from the proof-of-concept research conducted with the prototype and six participants. In the research, we collected qualitative data from interviews to identify whether the prototype supported students to develop their reflective skills. The thematic analysis of the interviews showed that the Feeler’s learning script and visualization of the EEG data supported greater levels of reflection by fostering students’ curiosity, puzzlement, and personal inquiry. The proof-of-concept research also provided insights into several factors, such as the value of personal experience, the challenge of assumptions, and the contextualization of the data that trigger reflective thinking. The results validate the design concept and the role of the prototype in supporting awareness of and reflection about students’ mental states when they perform academic tasks.

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

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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.

Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

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François-B. Vialatte

2011-01-01

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

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

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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.

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...

Detection of movement intention using EEG in a human-robot interaction environment

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Ernesto Pablo Lana

Full Text Available Introduction : This paper presents a detection method for upper limb movement intention as part of a brain-machine interface using EEG signals, whose final goal is to assist disabled or vulnerable people with activities of daily living. Methods EEG signals were recorded from six naïve healthy volunteers while performing a motor task. Every volunteer remained in an acoustically isolated recording room. The robot was placed in front of the volunteers such that it seemed to be a mirror of their right arm, emulating a Brain Machine Interface environment. The volunteers were seated in an armchair throughout the experiment, outside the reaching area of the robot to guarantee safety. Three conditions are studied: observation, execution, and imagery of right arm’s flexion and extension movements paced by an anthropomorphic manipulator robot. The detector of movement intention uses the spectral F test for discrimination of conditions and uses as feature the desynchronization patterns found on the volunteers. Using a detector provides an objective method to acknowledge for the occurrence of movement intention. Results When using four realizations of the task, detection rates ranging from 53 to 97% were found in five of the volunteers when the movement was executed, in three of them when the movement was imagined, and in two of them when the movement was observed. Conclusions Detection rates for movement observation raises the question of how the visual feedback may affect the performance of a working brain-machine interface, posing another challenge for the upcoming interface implementation. Future developments will focus on the improvement of feature extraction and detection accuracy for movement intention using EEG data.

Executed Movement Using EEG Signals through a Naive Bayes Classifier

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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.

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

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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

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.

Parahippocampal activation during successful recognition of words: a self-paced event-related fMRI study

NARCIS (Netherlands)

Daselaar, S. M.; Rombouts, S. A.; Veltman, D. J.; Raaijmakers, J. G.; Lazeron, R. H.; Jonker, C.

2001-01-01

In this study, we investigated retrieval from verbal episodic memory using a self-paced event-related fMRI paradigm, similar to the designs typically used in behavioral studies of memory function. We tested the hypothesis that the medial temporal lobe (MTL) is involved in the actual recovery of

Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF).

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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.

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

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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.

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...

Automated approach to detecting behavioral states using EEG-DABS

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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.

EEG feature selection method based on decision tree.

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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.

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

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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.

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

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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.

Structured Approach vs. Self-Paced Modular Approach in Teaching Trigonometry

Directory of Open Access Journals (Sweden)

Rodin M. Paspasan

2015-12-01

Full Text Available The study aimed to determine which approach in the teaching of Mathematics allowed students to achieve a higher mathematical performance and to establish the learning styles of the students to showed greater confidence on a written posttest - the self-paced modular approach or the structured lecture demonstration based approach. The instruments used in the study are Trigonometry Achievement Test (PTAT designed by the researcher and the Grasha - Reichmann Student Learning Style Survey. Hence. the result shows on the test of significant difference on the respondents learning styles and level of performance established independent learning conditions and demonstrate remarkably higher mathematical performance, respectively. In the light of the statistical analysis and the findings of the study, it could be generalized that SPMA made the students learning styles more independent because they prefer to work at their own pace. Hence, SPMA help them also improve their level of performance in relation to plane trigonometry regardless of their mathematical abilities compared to structured approach. Along these lines, the subsequent recommendations are presented for consideration: The teachers should use collective learning style inventories so that students remain interested throughout their mathematics course. And should use SPMA in teaching trigonometry and other disciplines in the field of mathematics.

Method for Improving EEG Based Emotion Recognition by Combining It with Synchronized Biometric and Eye Tracking Technologies in a Non-invasive and Low Cost Way.

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López-Gil, Juan-Miguel; Virgili-Gomá, Jordi; Gil, Rosa; García, Roberto

2016-01-01

Technical advances, particularly the integration of wearable and embedded sensors, facilitate tracking of physiological responses in a less intrusive way. Currently, there are many devices that allow gathering biometric measurements from human beings, such as EEG Headsets or Health Bracelets. The massive data sets generated by tracking of EEG and physiology may be used, among other things, to infer knowledge about human moods and emotions. Apart from direct biometric signal measurement, eye tracking systems are nowadays capable of determining the point of gaze of the users when interacting in ICT environments, which provides an added value research on many different areas, such as psychology or marketing. We present a process in which devices for eye tracking, biometric, and EEG signal measurements are synchronously used for studying both basic and complex emotions. We selected the least intrusive devices for different signal data collection given the study requirements and cost constraints, so users would behave in the most natural way possible. On the one hand, we have been able to determine basic emotions participants were experiencing by means of valence and arousal. On the other hand, a complex emotion such as empathy has also been detected. To validate the usefulness of this approach, a study involving forty-four people has been carried out, where they were exposed to a series of affective stimuli while their EEG activity, biometric signals, and eye position were synchronously recorded to detect self-regulation. The hypothesis of the work was that people who self-regulated would show significantly different results when analyzing their EEG data. Participants were divided into two groups depending on whether Electro Dermal Activity (EDA) data indicated they self-regulated or not. The comparison of the results obtained using different machine learning algorithms for emotion recognition shows that using EEG activity alone as a predictor for self-regulation does

Method for Improving EEG Based Emotion Recognition by Combining It with Synchronized Biometric and Eye Tracking Technologies in a Non-invasive and Low Cost Way

Science.gov (United States)

López-Gil, Juan-Miguel; Virgili-Gomá, Jordi; Gil, Rosa; Guilera, Teresa; Batalla, Iolanda; Soler-González, Jorge; García, Roberto

2016-01-01

Technical advances, particularly the integration of wearable and embedded sensors, facilitate tracking of physiological responses in a less intrusive way. Currently, there are many devices that allow gathering biometric measurements from human beings, such as EEG Headsets or Health Bracelets. The massive data sets generated by tracking of EEG and physiology may be used, among other things, to infer knowledge about human moods and emotions. Apart from direct biometric signal measurement, eye tracking systems are nowadays capable of determining the point of gaze of the users when interacting in ICT environments, which provides an added value research on many different areas, such as psychology or marketing. We present a process in which devices for eye tracking, biometric, and EEG signal measurements are synchronously used for studying both basic and complex emotions. We selected the least intrusive devices for different signal data collection given the study requirements and cost constraints, so users would behave in the most natural way possible. On the one hand, we have been able to determine basic emotions participants were experiencing by means of valence and arousal. On the other hand, a complex emotion such as empathy has also been detected. To validate the usefulness of this approach, a study involving forty-four people has been carried out, where they were exposed to a series of affective stimuli while their EEG activity, biometric signals, and eye position were synchronously recorded to detect self-regulation. The hypothesis of the work was that people who self-regulated would show significantly different results when analyzing their EEG data. Participants were divided into two groups depending on whether Electro Dermal Activity (EDA) data indicated they self-regulated or not. The comparison of the results obtained using different machine learning algorithms for emotion recognition shows that using EEG activity alone as a predictor for self-regulation does

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

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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.

Work Rate during Self-paced Exercise is not Mediated by the Rate of Heat Storage.

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Friesen, Brian J; Périard, Julien D; Poirier, Martin P; Lauzon, Martin; Blondin, Denis P; Haman, Francois; Kenny, Glen P

2018-01-01

To date, there have been mixed findings on whether greater anticipatory reductions in self-paced exercise intensity in the heat are mediated by early differences in rate of body heat storage. The disparity may be due to an inability to accurately measure minute-to-minute changes in whole-body heat loss. Thus, we evaluated whether early differences in rate of heat storage can mediate exercise intensity during self-paced cycling at a fixed rate of perceived exertion (RPE of 16; hard-to-very-hard work effort) in COOL (15°C), NORMAL (25°C), and HOT (35°C) ambient conditions. On separate days, nine endurance-trained cyclists exercised in COOL, NORMAL, and HOT conditions at a fixed RPE until work rate (measured after first 5 min of exercise) decreased to 70% of starting values. Whole-body heat loss and metabolic heat production were measured by direct and indirect calorimetry, respectively. Total exercise time was shorter in HOT (57 ± 20 min) relative to both NORMAL (72 ± 23 min, P = 0.004) and COOL (70 ± 26 min, P = 0.045). Starting work rate was lower in HOT (153 ± 31 W) compared with NORMAL (166 ± 27 W, P = 0.024) and COOL (170 ± 33 W, P = 0.037). Rate of heat storage was similar between conditions during the first 4 min of exercise (all P > 0.05). Thereafter, rate of heat storage was lower in HOT relative to NORMAL and COOL until 30 min of exercise (last common time-point between conditions; all P exercise. No differences were measured at end exercise. We show that rate of heat storage does not mediate exercise intensity during self-paced exercise at a fixed RPE in cool to hot ambient conditions.

Self-Paced Tutorial Courses for Mineral Science - Metallurgy Departments. Final Progress Report (July 1975-August 1980).

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Twidwell, L. G.

Four courses in extractive metallurgy (Pyrometallurgy, Hydrometallurgy, Electrometallurgy; and Physical Chemistry of Iron and Steel) were prepared in a modular, self-paced format. Development of the course materials included: (1) preparation of course outlines by unit coordinators and advisory committees; (2) approval of course outlines (included…

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

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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.

Self-induced stretch syncope of adolescence: a video-EEG documentation.

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Mazzuca, Michel; Thomas, Pierre

2007-12-01

We present the first video-EEG documentation, with ECG and EMG features, of stretch syncope of adolescence in a young, healthy 16-year-old boy. Stretch syncope of adolescence is a rarely reported, benign cause of fainting in young patients, which can be confused with epileptic seizures. In our patient, syncopes were self-induced to avoid school. Dynamic transcranial Doppler showed evidence of blood flow decrease in both posterior cerebral arteries mimicking effects of a Valsalva manoeuvre. Dynamic angiogram of the vertebral arteries was normal. Hypotheses concerning the physiopathology are discussed. [Published with video sequences].

Multimedia Approach to Self-Paced Individualized Instruction in Automotive Mechanics and Other Vocational Programs. Final Report.

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Ozarka Vocational Technical School, Melbourne, AR.

A project developed, field tested, implemented, and disseminated a management system, support materials, and references for a self-paced individualized instructional program in automotive mechanics and food services. During the program, the Planwriter component of the SAGE/Compute-a-Match Assessment System was used to develop a management system…

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...

EEG in connection with coma.

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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.

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

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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.

Stimulus-dependent spiking relationships with the EEG

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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

Prioritization of arbitrary faces associated to self: An EEG study.

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Mateusz Woźniak

Full Text Available Behavioral and neuroimaging studies have demonstrated that people process preferentially self-related information such as an image of their own face. Furthermore, people rapidly incorporate stimuli into their self-representation even if these stimuli do not have an intrinsic relation to self. In the present study, we investigated the time course of the processes involved in preferential processing of self-related information. In two EEG experiments three unfamiliar faces were identified with verbal labels as either the participant, a friend, or a stranger. Afterwards, participants judged whether two stimuli presented in succession (ISI = 1500ms matched. In experiment 1, faces were followed by verbal labels and in experiment 2, labels were followed by faces. Both experiments showed the same pattern of behavioral and electrophysiological results. If the first stimulus (face or label was associated with self, reaction times were faster and the late frontal positivity following the first stimulus was more pronounced. The self-association of the second stimulus (label or face did not affect response times. However, the central-parietal P3 following presentation of the second stimulus was more pronounced when the second stimulus was preceded by self-related first stimulus. These results indicate that even unfamiliar faces that are associated to self can activate a self-representation. Once the self-representation has been activated the processing of ensuing stimuli is facilitated, irrespective of whether they are associated with the self.

Analysis of Small Muscle Movement Effects on EEG Signals

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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

Self-paced versus fixed speed walking and the effect of virtual reality in children with cerebral palsy

NARCIS (Netherlands)

Sloot, L.H.; Harlaar, J.; van der Krogt, M.M.

2015-01-01

While feedback-controlled treadmills with a virtual reality could potentially offer advantages for clinical gait analysis and training, the effect of self-paced walking and the virtual environment on the gait pattern of children and different patient groups remains unknown. This study examined the

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

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

2017-01-01

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

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

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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.

Physiological artifacts in scalp EEG and ear-EEG.

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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.

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

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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.

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

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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.

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

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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.

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

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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.

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

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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.

Pacing: a concept analysis of the chronic pain intervention.

Science.gov (United States)

Jamieson-Lega, Kathryn; Berry, Robyn; Brown, Cary A

2013-01-01

The intervention of pacing is regularly recommended for chronic pain patients. However, pacing is poorly defined and appears to be interpreted in varying, potentially contradictory manners within the field of chronic pain. This conceptual lack of clarity has implications for effective service delivery and for researchers' ability to conduct rigorous study. An examination of the background literature demonstrates that while pacing is often one part of a multidisciplinary pain management program, outcome research is hindered by a lack of a clear and shared definition of this currently ill-defined construct. To conduct a formal concept analysis of the term 'pacing'. A standardized concept analysis process (including literature scoping to identify all uses of the concept, analysis to determine defining attributes of the concept and identification of model, borderline and contrary cases) was used to determine what the concept of pacing does and does not represent within the current evidence base. A conceptual model including the core attributes of action, time, balance, learning and self-management emerged. From these attributes, an evidence-based definition for pacing was composed and distributed to stakeholders for review. After consideration of stakeholder feedback, the emergent definition of pacing was finalized as follows: "Pacing is an active self-management strategy whereby individuals learn to balance time spent on activity and rest for the purpose of achieving increased function and participation in meaningful activities". The findings of the present concept analysis will help to standardize the use and definition of the term pacing across disciplines for the purposes of both pain management and research.

Effect of Music Tempo on Attentional Focus and Perceived Exertion during Self-selected Paced Walking.

Science.gov (United States)

Silva, Aldo Coelho; Dos Santos Ferreira, Sandro; Alves, Ragami Chaves; Follador, Lucio; DA Silva, Sergio Gregorio

2016-01-01

This study investigated the influence of music on the rating of perceived exertion (RPE) and attentional focus during walking at a self-selected pace. Fifteen overweight and obese women volunteered to participate in the study. They underwent four sessions: the first for incremental maximal test and anthropometric measurement followed by three experimental sessions. After the first session, they were exposed to three 30-minute walking sessions at a self-selected pace in a counterbalanced order: fast-tempo music (FT), medium-tempo music (MT) and no-music control (NM). Borg's RPE Scale and an Attentional Focus Questionnaire were used to measure the perceptual response and attentional focus, respectively. Results showed that the RPE was higher in the no-music control than in the medium-tempo music (12.05 ± 0.6 vs. 10.5 ± 0.5). Furthermore, dissociative attentional focus was greater for both conditions with music in comparison with the no-music control (NM= 39.0 ± 4.1; MT= 48.4 ± 4.1 and FT= 47.9 ± 4.5). The results indicated that the use of music during walking can modulate attentional focus, increasing dissociative thought, and medium-tempo music can reduce the RPE.

Fast self paced listening times in syntactic comprehension is aphasia -- implications for deficits

Directory of Open Access Journals (Sweden)

Jennifer Michaud

2015-04-01

Full Text Available Sixty one people with aphasia (pwa and forty one matched controls were tested for the ability to understand sentences that required the ability to assign particular syntactic structures. Participants paced themselves word-by-word through twenty examples of eleven spoken sentence types and indicated which of two pictures corresponded to the meaning of each sentence. Sentences were developed in pairs such that comprehension of the experimental version of a pair required an aspect of syntactic processing not required in the corresponding baseline sentence. The need for the syntactic operations required only in the experimental version was triggered at a “critical word” in the experimental sentence. Listening times for critical words in experimental sentences were compared to those for corresponding words in the corresponding baseline sentences. We adjusted self paced listening times for word duration by subtracting word durations from tag-to-tag self paced listening times to correct for word duration, yielding what we have previously called “corrected listening times.” Corrected listening times above ceiling (10,000 msec for sentence-final words and 5,000 msec for all other words were discarded. For controls, this led to 0.2% of data being discarded and for PWAs 2.2% were discarded. Corrected listening times that were more than 3 standard deviations above or below the mean for that sentence type for each subject were adjusted either down to the upper limit or up to the lower limit of the 3SD range (not discarded. For accurate sentences, 1.7% of the control data were adjusted and 1.8% of the aphasic data were adjusted. For inaccurate sentences, 10% of the corrected listening times were adjusted for controls and 3.3% for aphasics. Our interest is in incremental parsing and interpretation. The measure we used of this process was the residual of a regression of corrected self paced listening times for critical words in experimental sentences

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.

Whole-body pre-cooling and heat storage during self-paced cycling performance in warm humid conditions.

Science.gov (United States)

Kay, D; Taaffe, D R; Marino, F E

1999-12-01

The aim of this study was to establish the effect that pre-cooling the skin without a concomitant reduction in core temperature has on subsequent self-paced cycling performance under warm humid (31 degrees C and 60% relative humidity) conditions. Seven moderately trained males performed a 30 min self-paced cycling trial on two separate occasions. The conditions were counterbalanced as control or whole-body pre-cooling by water immersion so that resting skin temperature was reduced by approximately 5-6 degrees C. After pre-cooling, mean skin temperature was lower throughout exercise and rectal temperature was lower (P body sweat fell from 1.7+/-0.1 l x h(-1) to 1.2+/-0.1 l h(-1) (P < 0.05). The distance cycled increased from 14.9+/-0.8 to 15.8+/-0.7 km (P < 0.05) after pre-cooling. The results indicate that skin pre-cooling in the absence of a reduced rectal temperature is effective in reducing thermal strain and increasing the distance cycled in 30 min under warm humid conditions.

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

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.

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.

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.

Active Self-Paced Learning for Cost-Effective and Progressive Face Identification.

Science.gov (United States)

Lin, Liang; Wang, Keze; Meng, Deyu; Zuo, Wangmeng; Zhang, Lei

2018-01-01

This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the "instructor-student-collaborative" learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at: http://hcp.sysu.edu.cn/projects/aspl/.

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.

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

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%.

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%.

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.

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...

Generalized Information Equilibrium Approaches to EEG Sleep Stage Discrimination

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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.

Effects of self-paced interval and continuous training on health markers in women

OpenAIRE

Connolly, Luke J; Bailey, Stephen J; Krustrup, Peter; Fulford, Jonathan; Smietanka, Chris; Jones, Andrew M

2017-01-01

PURPOSE: To compare the effects of self-paced high-intensity interval and continuous cycle training on health markers in premenopausal women.METHODS: Forty-five inactive females were randomised to a high-intensity interval training (HIIT; n = 15), continuous training (CT; n = 15) or an inactive control (CON; n = 15) group. HIIT performed 5 × 5 min sets comprising repetitions of 30-s low-, 20-s moderate- and 10-s high-intensity cycling with 2 min rest between sets. CT completed 50 min of conti...

Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task

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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.

Visual aided pacing in respiratory maneuvers

Energy Technology Data Exchange (ETDEWEB)

Rambaudi, L R [Laboratorio de Biofisica y Fisiologia ' Antonio Sadi Frumento' (Argentina); Rossi, E [Catedra de Bioingenieria II (Argentina); Mantaras, M C [Catedra de Bioingenieria II (Argentina); Perrone, M S [Laboratorio de Biofisica y Fisiologia ' Antonio Sadi Frumento' (Argentina); Siri, L Nicola [Catedra de Bioingenieria II (Argentina)

2007-11-15

A visual aid to pace self-controlled respiratory cycles in humans is presented. Respiratory manoeuvres need to be accomplished in several clinic and research procedures, among others, the studies on Heart Rate Variability. Free running respiration turns to be difficult to correlate with other physiologic variables. Because of this fact, voluntary self-control is asked from the individuals under study. Currently, an acoustic metronome is used to pace respiratory frequency, its main limitation being the impossibility to induce predetermined timing in the stages within the respiratory cycle. In the present work, visual driven self-control was provided, with separate timing for the four stages of a normal respiratory cycle. This visual metronome (ViMet) was based on a microcontroller which power-ON and -OFF an eight-LED bar, in a four-stage respiratory cycle time series handset by the operator. The precise timing is also exhibited on an alphanumeric display.

Visual aided pacing in respiratory maneuvers

International Nuclear Information System (INIS)

Rambaudi, L R; Rossi, E; Mantaras, M C; Perrone, M S; Siri, L Nicola

2007-01-01

A visual aid to pace self-controlled respiratory cycles in humans is presented. Respiratory manoeuvres need to be accomplished in several clinic and research procedures, among others, the studies on Heart Rate Variability. Free running respiration turns to be difficult to correlate with other physiologic variables. Because of this fact, voluntary self-control is asked from the individuals under study. Currently, an acoustic metronome is used to pace respiratory frequency, its main limitation being the impossibility to induce predetermined timing in the stages within the respiratory cycle. In the present work, visual driven self-control was provided, with separate timing for the four stages of a normal respiratory cycle. This visual metronome (ViMet) was based on a microcontroller which power-ON and -OFF an eight-LED bar, in a four-stage respiratory cycle time series handset by the operator. The precise timing is also exhibited on an alphanumeric display

The Influence of a Pacesetter on Psychological Responses and Pacing Behavior during a 1600 m Run

Directory of Open Access Journals (Sweden)

Christopher L. Fullerton, Andrew M. Lane, Tracey J. Devonport

2017-12-01

Full Text Available This study compared the effects of following a pacer versus following a self-paced plan on psychological responses and pacing behavior in well-trained distance runners. Pacing in the present study was individually tailored where each participant developed a personal strategy to ensure their goal time was achieved. We expected that following a pacer would associate with goal achievement, higher pre-run confidence, positive emotions and lower perceived exertion during performance. In a mixed-design repeated-measures study, nineteen well-trained runners completed two 1600m running time trials. Ten runners had a pacer (paced group who supported their individual pacing strategy, and nine participants self-paced running alone (control group. Both groups could check pace using their wrist watch. In contrast to our expectation, results indicated that the paced group reported higher pre-run anxiety with no significant differences in finish time, goal confidence, goal difficulty, perceived exertion, and self-rated performance between groups. We suggest that following a pacer is a skill that requires learning. Following a personalised pacer might associate with higher anxiety due to uncertainty in being able to keep up with the pacer and public visibility of dropping behind, something that is not so observable in a self-paced run completed alone. Future research should investigate mechanisms associated with effective pacing.

INTELLIGENT EEG ANALYSIS

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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.

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.

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

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Kei Omata

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

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.

The neural bases of the constructive nature of autobiographical memories studied with a self-paced fMRI design

Energy Technology Data Exchange (ETDEWEB)

Botzung, A.; Denkova, E.; Ciuciu, P.; Scheiber, C.; Manning, L. [CNRS, Strasbourg (France)

2008-07-01

In Conway and Pleydell-Pearce's model (2000), autobiographical memories are viewed as transitory mental representations, more often generated in an effort-full way. An important claim of the model concerns the dynamic process that evolves over time, from the left prefrontal areas to posterior regions, to retrieve specific memories. The present work aims at investigating, using fMRI, the temporal distribution of effort-full autobiographical memory construction. In addition, a self-paced design was implemented to elucidate the question of the timing window required to evoke recollections. The results showed a large pattern of brain regions, which included the two major poles of activation predicted by Conway and Pleydell-Pearce's model. Likewise, we were able to detect the earlier implication of the left dorso-lateral prefrontal cortex, by comparison with posterior structures, which seemed to confirm its involvement in the effort-full retrieval process. Finally, the self-paced procedure allowed us to refine the timing window necessary to construct past events. (authors)

The neural bases of the constructive nature of autobiographical memories studied with a self-paced fMRI design

International Nuclear Information System (INIS)

Botzung, A.; Denkova, E.; Ciuciu, P.; Scheiber, C.; Manning, L.

2008-01-01

In Conway and Pleydell-Pearce's model (2000), autobiographical memories are viewed as transitory mental representations, more often generated in an effort-full way. An important claim of the model concerns the dynamic process that evolves over time, from the left prefrontal areas to posterior regions, to retrieve specific memories. The present work aims at investigating, using fMRI, the temporal distribution of effort-full autobiographical memory construction. In addition, a self-paced design was implemented to elucidate the question of the timing window required to evoke recollections. The results showed a large pattern of brain regions, which included the two major poles of activation predicted by Conway and Pleydell-Pearce's model. Likewise, we were able to detect the earlier implication of the left dorso-lateral prefrontal cortex, by comparison with posterior structures, which seemed to confirm its involvement in the effort-full retrieval process. Finally, the self-paced procedure allowed us to refine the timing window necessary to construct past events. (authors)

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.

Effect of Music Tempo on Attentional Focus and Perceived Exertion during Self-selected Paced Walking

Science.gov (United States)

SILVA, ALDO COELHO; DOS SANTOS FERREIRA, SANDRO; ALVES, RAGAMI CHAVES; FOLLADOR, LUCIO; DA SILVA, SERGIO GREGORIO

2016-01-01

This study investigated the influence of music on the rating of perceived exertion (RPE) and attentional focus during walking at a self-selected pace. Fifteen overweight and obese women volunteered to participate in the study. They underwent four sessions: the first for incremental maximal test and anthropometric measurement followed by three experimental sessions. After the first session, they were exposed to three 30-minute walking sessions at a self-selected pace in a counterbalanced order: fast-tempo music (FT), medium-tempo music (MT) and no-music control (NM). Borg’s RPE Scale and an Attentional Focus Questionnaire were used to measure the perceptual response and attentional focus, respectively. Results showed that the RPE was higher in the no-music control than in the medium-tempo music (12.05 ± 0.6 vs. 10.5 ± 0.5). Furthermore, dissociative attentional focus was greater for both conditions with music in comparison with the no-music control (NM= 39.0 ± 4.1; MT= 48.4 ± 4.1 and FT= 47.9 ± 4.5). The results indicated that the use of music during walking can modulate attentional focus, increasing dissociative thought, and medium-tempo music can reduce the RPE. PMID:27990220

Examining the Effectiveness of a Semi-Self-Paced Flipped Learning Format in a College General Chemistry Sequence

Science.gov (United States)

Hibbard, Lisa; Sung, Shannon; Wells, Breche´

2016-01-01

Flipped learning has come to the forefront in education. It maximizes learning by moving content delivery online, where learning can be self-paced, allowing for class time to focus on student-centered active learning. This five-year cross-sectional study assessed student performance in a college general chemistry for majors sequence taught by a…

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 %

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.

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.

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....

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...

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.

The Role of Perceived User-Interface Design in Continued Usage Intention of Self-Paced E-Learning Tools

Science.gov (United States)

Cho, Vincent; Cheng, T. C. Edwin; Lai, W. M. Jennifer

2009-01-01

While past studies on user-interface design focused on a particular system or application using the experimental approach, we propose a theoretical model to assess the impact of perceived user-interface design (PUID) on continued usage intention (CUI) of self-paced e-learning tools in general. We argue that the impact of PUID is mediated by two…

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.

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

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.

Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis

Directory of Open Access Journals (Sweden)

Yuan-Pin Lin

2017-07-01

Full Text Available Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability, arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18 and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40% as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing neither achieved the cross-day classification (the accuracy was around chance level nor replicated the encouraging improvement due to the inter-day EEG variability. This result

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.

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

The corticospinal responses of metronome-paced, but not self-paced strength training are similar to motor skill training.

Science.gov (United States)

Leung, Michael; Rantalainen, Timo; Teo, Wei-Peng; Kidgell, Dawson

2017-12-01

The corticospinal responses to skill training may be different to strength training, depending on how the strength training is performed. It was hypothesised that the corticospinal responses would not be different following skill training and metronome-paced strength training (MPST), but would differ when compared with self-paced strength training (SPST). Corticospinal excitability, short-interval intra-cortical inhibition (SICI) and strength and tracking error were measured at baseline and 2 and 4 weeks. Participants (n = 44) were randomly allocated to visuomotor tracking, MPST, SPST or a control group. MPST increased strength by 7 and 18%, whilst SPST increased strength by 12 and 26% following 2 and 4 weeks of strength training. There were no changes in strength following skill training. Skill training reduced tracking error by 47 and 58% at 2 and 4 weeks. There were no changes in tracking error following SPST; however, tracking error reduced by 24% following 4 weeks of MPST. Corticospinal excitability increased by 40% following MPST and by 29% following skill training. There was no change in corticospinal excitability following 4 weeks of SPST. Importantly, the magnitude of change between skill training and MPST was not different. SICI decreased by 41 and 61% following 2 and 4 weeks of MPST, whilst SICI decreased by 41 and 33% following 2 and 4 weeks of skill training. Again, SPST had no effect on SICI at 2 and 4 weeks. There was no difference in the magnitude of SICI reduction between skill training and MPST. This study adds new knowledge regarding the corticospinal responses to skill and MPST, showing they are similar but different when compared with SPST.

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)

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

Science.gov (United States)

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

2012-01-01

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

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....

A delivery mode study: The effect of self-paced video learning on first-year college students' achievement in calculus

Science.gov (United States)

Oktaviyanthi, Rina; Herman, Tatang

2016-10-01

In this paper, the effect of two different modes of deliver are proposed. The use of self-paced video learning and conventional learning methods in mathematics are compared. The research design classified as a quasi-experiment. The participants were 80 students in the first-year college and divided into two groups. One group as an experiment class received self-paced video learning method and the other group as a control group taught by conventional learning method. Pre and posttest were employed to measure the students' achievement, while questionnaire and interviews were applied to support the pre and posttest data. Statistical analysis included the independent samples t-test showed differences (p Calculus, such as appropriate learning for both audio and visual of students' characteristics, useful to learn Calculus, assisting students to be more engaging and paying attention in learning, helping students in making the concepts of Calculus are visible, interesting media and motivating students to learn independently.

Self-marketing and self-consumption by third parties according to the EEG. Practical and legal problems of marketing and selling of self-produced solar electricity; Eigenvermarktung und Selbstverbrauch durch Dritte nach EEG. Praktische und rechtliche Fragen der Veraeusserung selbsterzeugten Solarstroms

Energy Technology Data Exchange (ETDEWEB)

Hahn, Christopher; Naumann, Daniel [Luther Rechtsanwaltsgesellschaft mbH, Leipzig (Germany)

2012-07-01

According to Section 33 No. 2 p. 1 EEG, reimbursement for photovoltaic power generation must be granted also if a third party consumes the generated power in the immediate vicinity in the plant and is able to prove it. The wording of the law is ambiguous as 'third party' and 'self-consumption' do not immediately specify when a right to reimbursement has arisen and when not. The contribution attempts to help with the solution of these interpretation problems and also throws a light on the rights and obligations of a PV plant owner in general. The high practical relevance of the issue is also reflected in the fact that the legal problems discussed here were implemented, and solved to a large extent, in the amendment of the EEG that came into force on 1 January 2012.

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)

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

Science.gov (United States)

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

2014-02-01

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

Optimal pacing strategy: From theoretical modeling to reality in 1500m speed skating

NARCIS (Netherlands)

Hettinga, F.J.; de Koning, J.J.; Schmidt, L.J.I.; Wind, N.A.C.; McIntosh, B.; Foster, C.

2011-01-01

Purpose: Athletes are trained to choose the pace which is perceived to be correct during a specific effort, such as the 1500-m speed skating competition. The purpose of the present study was to "override" self-paced (SP) performance by instructing athletes to execute a theoretically optimal pacing

Optimal pacing strategy : from theoretical modelling to reality in 1500-m speed skating

NARCIS (Netherlands)

Hettinga, F. J.; De Koning, J. J.; Schmidt, L. J. I.; Wind, N. A. C.; MacIntosh, B. R.; Foster, C.

Purpose Athletes are trained to choose the pace which is perceived to be correct during a specific effort, such as the 1500-m speed skating competition. The purpose of the present study was to "override" self-paced (SP) performance by instructing athletes to execute a theoretically optimal pacing

Prediction of advertisement preference by fusing EEG response and sentiment analysis.

Science.gov (United States)

Gauba, Himaanshu; Kumar, Pradeep; Roy, Partha Pratim; Singh, Priyanka; Dogra, Debi Prosad; Raman, Balasubramanian

2017-08-01

This paper presents a novel approach to predict rating of video-advertisements based on a multimodal framework combining physiological analysis of the user and global sentiment-rating available on the internet. We have fused Electroencephalogram (EEG) waves of user and corresponding global textual comments of the video to understand the user's preference more precisely. In our framework, the users were asked to watch the video-advertisement and simultaneously EEG signals were recorded. Valence scores were obtained using self-report for each video. A higher valence corresponds to intrinsic attractiveness of the user. Furthermore, the multimedia data that comprised of the comments posted by global viewers, were retrieved and processed using Natural Language Processing (NLP) technique for sentiment analysis. Textual contents from review comments were analyzed to obtain a score to understand sentiment nature of the video. A regression technique based on Random forest was used to predict the rating of an advertisement using EEG data. Finally, EEG based rating is combined with NLP-based sentiment score to improve the overall prediction. The study was carried out using 15 video clips of advertisements available online. Twenty five participants were involved in our study to analyze our proposed system. The results are encouraging and these suggest that the proposed multimodal approach can achieve lower RMSE in rating prediction as compared to the prediction using only EEG data. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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.

Parametric and Nonparametric EEG Analysis for the Evaluation of EEG Activity in Young Children with Controlled Epilepsy

Directory of Open Access Journals (Sweden)

Vangelis Sakkalis

2008-01-01

Full Text Available There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform and a parametric, signal modeling technique (ARMA are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.

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

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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.

How do children deal with inconsistencies in text? An eye fixation and self-paced reading study in primary school children.

NARCIS (Netherlands)

van der Schoot, M.; Reijntjes, A.H.A.; van Lieshout, E.C.D.M.

2012-01-01

In two experiments, we investigated comprehension monitoring in 10-12 years old children differing in reading comprehension skill. The children's self-paced reading times (Experiment 1) and eye fixations and regressions (Experiment 2) were measured as they read narrative texts in which an action of

Assessment of the perception of verticality and horizontality with self-paced saccades.

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Pettorossi, V E; Bambagioni, D; Bronstein, A M; Gresty, M A

1998-07-01

We investigated the ability of human subjects (Ss) to make self-paced saccades in the earth-vertical and horizontal directions (space-referenced task) and in the direction of the head-vertical and horizontal axis (self-referenced task) during whole body tilts of 0 degrees, 22.5 degrees, 45 degrees and 90 degrees in the frontal (roll) plane. Saccades were recorded in the dark with computerised video-oculography. During space-referenced tasks, the saccade vectors did not fully counter-rotate to compensate for larger angles of body tilt. This finding is in agreement with the 'A' effect reported for the visual vertical. The error was significantly larger for saccades intended to be space-horizontal than space-vertical. This vertico-horizontal dissociation implies greater difficulty in defining horizontality than verticality with the non-visual motor task employed. In contrast, normal Ss (and an alabyrinthine subject tested) were accurate in orienting saccades to their own (cranio-centric) vertical and horizontal axes regardless of tilt indicating that cranio-centric perception is robust and apparently not affected by gravitational influences.

Only low frequency event-related EEG activity is compromised in multiple sclerosis: insights from an independent component clustering analysis.

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Hanni Kiiski

Full Text Available Cognitive impairment (CI, often examined with neuropsychological tests such as the Paced Auditory Serial Addition Test (PASAT, affects approximately 65% of multiple sclerosis (MS patients. The P3b event-related potential (ERP, evoked when an infrequent target stimulus is presented, indexes cognitive function and is typically compared across subjects' scalp electroencephalography (EEG data. However, the clustering of independent components (ICs is superior to scalp-based EEG methods because it can accommodate the spatiotemporal overlap inherent in scalp EEG data. Event-related spectral perturbations (ERSPs; event-related mean power spectral changes and inter-trial coherence (ITCs; event-related consistency of spectral phase reveal a more comprehensive overview of EEG activity. Ninety-five subjects (56 MS patients, 39 controls completed visual and auditory two-stimulus P3b event-related potential tasks and the PASAT. MS patients were also divided into CI and non-CI groups (n = 18 in each based on PASAT scores. Data were recorded from 128-scalp EEG channels and 4 IC clusters in the visual, and 5 IC clusters in the auditory, modality were identified. In general, MS patients had significantly reduced ERSP theta power versus controls, and a similar pattern was observed for CI vs. non-CI MS patients. The ITC measures were also significantly different in the theta band for some clusters. The finding that MS patients had reduced P3b task-related theta power in both modalities is a reflection of compromised connectivity, likely due to demyelination, that may have disrupted early processes essential to P3b generation, such as orientating and signal detection. However, for posterior sources, MS patients had a greater decrease in alpha power, normally associated with enhanced cognitive function, which may reflect a compensatory mechanism in response to the compromised early cognitive processing.

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.

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

The effects of presentation pace and modality on learning a multimedia science lesson

Science.gov (United States)

Chung, Wen-Hung

Working memory is a system that consists of multiple components. The visuospatial sketchpad is the main entrance for visual and spatial information, whereas acoustic and verbal information is processed in the phonological loop. The central executive works as a coordinator of information from these two subsystems. Numerous studies have shown that working memory has a very limited capacity. Based on these characteristics of working memory, theories such as cognitive load theory and the cognitive theory of multimedia learning provide multimedia design principles. One of these principles is that when verbal information accompanying pictures is presented in audio mode instead of visually, learning can be more effective than if both text and pictures are presented visually. This is called the modality effect. However, some studies have found that the modality effect does not occur in some situations. In most experiments examining the modality effect, the multimedia is presented as system-paced. If learners are able to repeat listening as many times as they need, the superiority of spoken text over visual text seems lessened. One aim of this study was to examine the modality effect in a learner-controlled condition. This study also used the one-word-at-a-time technique to investigate whether the modality effect would still occur if both reading and listening rates were equal. There were 182 college students recruited for this study. Participants were randomly assigned to seven groups: a self-paced listening group, a self-paced reading group, a self text-block reading group, a general-paced listening group, a general-paced reading group, a fast-paced listening group, and a fast-paced reading group. The experimental material was a cardiovascular multimedia module. A three-by-two between-subjects design was used to test the main effect. Results showed that modality effect was still present but not between the self-paced listening group and the self text-block reading group

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

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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.

Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification

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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%.

The Performance of EEG-P300 Classification using Backpropagation Neural Networks

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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.

EEG analyses with SOBI.

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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.

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

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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.

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

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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.

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...

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

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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.

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

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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.

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

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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

Preterm EEG: a multimodal neurophysiological protocol.

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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.

Self-paced versus fixed speed walking and the effect of virtual reality in children with cerebral palsy.

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Sloot, Lizeth H; Harlaar, Jaap; van der Krogt, Marjolein M

2015-10-01

While feedback-controlled treadmills with a virtual reality could potentially offer advantages for clinical gait analysis and training, the effect of self-paced walking and the virtual environment on the gait pattern of children and different patient groups remains unknown. This study examined the effect of self-paced (SP) versus fixed speed (FS) walking and of walking with and without a virtual reality (VR) in 11 typically developing (TD) children and nine children with cerebral palsy (CP). We found that subjects walked in SP mode with twice as much between-stride walking speed variability (pinteraction effects between SP and group (TD versus CP) were found for five out of 33 parameters. This suggests that children with CP might need more time to familiarize to SP walking, however, these differences were generally too small to be clinically relevant. The VR environment did not affect the kinematic or kinetic parameters, but walking with VR was rated as more similar to overground walking by both groups (p=0.02). The results of this study indicate that both SP and FS walking, with and without VR, can be used interchangeably for treadmill-based clinical gait analysis in children with and without CP. Copyright © 2015 Elsevier B.V. All rights reserved.

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

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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.

Superior Self-Paced Memorization of Digits in Spite of a Normal Digit Span: The Structure of a Memorist's Skill

Science.gov (United States)

Hu, Yi; Ericsson, K. Anders; Yang, Dan; Lu, Chao

2009-01-01

Over the last century many individuals with exceptional memory have been studied and tested in the laboratory. This article studies Chao Lu, who set a Guinness World Record by memorizing 67,890 decimals of pi. Chao Lu's superior self-paced memorization of digits is shown through analyses of study times and verbal reports to be mediated by mnemonic…

Textile Electrodes for EEG Recording — A Pilot Study

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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.

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

ReliefF-Based EEG Sensor Selection Methods for Emotion Recognition.

Science.gov (United States)

Zhang, Jianhai; Chen, Ming; Zhao, Shaokai; Hu, Sanqing; Shi, Zhiguo; Cao, Yu

2016-09-22

Electroencephalogram (EEG) signals recorded from sensor electrodes on the scalp can directly detect the brain dynamics in response to different emotional states. Emotion recognition from EEG signals has attracted broad attention, partly due to the rapid development of wearable computing and the needs of a more immersive human-computer interface (HCI) environment. To improve the recognition performance, multi-channel EEG signals are usually used. A large set of EEG sensor channels will add to the computational complexity and cause users inconvenience. ReliefF-based channel selection methods were systematically investigated for EEG-based emotion recognition on a database for emotion analysis using physiological signals (DEAP). Three strategies were employed to select the best channels in classifying four emotional states (joy, fear, sadness and relaxation). Furthermore, support vector machine (SVM) was used as a classifier to validate the performance of the channel selection results. The experimental results showed the effectiveness of our methods and the comparison with the similar strategies, based on the F-score, was given. Strategies to evaluate a channel as a unity gave better performance in channel reduction with an acceptable loss of accuracy. In the third strategy, after adjusting channels' weights according to their contribution to the classification accuracy, the number of channels was reduced to eight with a slight loss of accuracy (58.51% ± 10.05% versus the best classification accuracy 59.13% ± 11.00% using 19 channels). In addition, the study of selecting subject-independent channels, related to emotion processing, was also implemented. The sensors, selected subject-independently from frontal, parietal lobes, have been identified to provide more discriminative information associated with emotion processing, and are distributed symmetrically over the scalp, which is consistent with the existing literature. The results will make a contribution to the

ReliefF-Based EEG Sensor Selection Methods for Emotion Recognition

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Jianhai Zhang

2016-09-01

Full Text Available Electroencephalogram (EEG signals recorded from sensor electrodes on the scalp can directly detect the brain dynamics in response to different emotional states. Emotion recognition from EEG signals has attracted broad attention, partly due to the rapid development of wearable computing and the needs of a more immersive human-computer interface (HCI environment. To improve the recognition performance, multi-channel EEG signals are usually used. A large set of EEG sensor channels will add to the computational complexity and cause users inconvenience. ReliefF-based channel selection methods were systematically investigated for EEG-based emotion recognition on a database for emotion analysis using physiological signals (DEAP. Three strategies were employed to select the best channels in classifying four emotional states (joy, fear, sadness and relaxation. Furthermore, support vector machine (SVM was used as a classifier to validate the performance of the channel selection results. The experimental results showed the effectiveness of our methods and the comparison with the similar strategies, based on the F-score, was given. Strategies to evaluate a channel as a unity gave better performance in channel reduction with an acceptable loss of accuracy. In the third strategy, after adjusting channels’ weights according to their contribution to the classification accuracy, the number of channels was reduced to eight with a slight loss of accuracy (58.51% ± 10.05% versus the best classification accuracy 59.13% ± 11.00% using 19 channels. In addition, the study of selecting subject-independent channels, related to emotion processing, was also implemented. The sensors, selected subject-independently from frontal, parietal lobes, have been identified to provide more discriminative information associated with emotion processing, and are distributed symmetrically over the scalp, which is consistent with the existing literature. The results will make a

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...

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

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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.

Diagnostic accuracy of pace spikes in the electrocardiogram to diagnose paced rhythm

DEFF Research Database (Denmark)

Andersson, Hedvig Bille; Hansen, Marco Bo; Thorsberger, Mads

2015-01-01

OBJECTIVE: To determine how often cardiac resynchronization therapy (CRT) pacing systems generate visible pace spikes in the electrocardiogram (ECG). METHODS: In 46 patients treated with CRT pacing systems, we recorded ECGs during intrinsic rhythm, atrial pacing and ventricular pacing. ECGs were...

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

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.

Analysis of absence seizure generation using EEG spatial-temporal regularity measures.

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Mammone, Nadia; Labate, Domenico; Lay-Ekuakille, Aime; Morabito, Francesco C

2012-12-01

Epileptic seizures are thought to be generated and to evolve through an underlying anomaly of synchronization in the activity of groups of neuronal populations. The related dynamic scenario of state transitions is revealed by detecting changes in the dynamical properties of Electroencephalography (EEG) signals. The recruitment procedure ending with the crisis can be explored through a spatial-temporal plot from which to extract suitable descriptors that are able to monitor and quantify the evolving synchronization level from the EEG tracings. In this paper, a spatial-temporal analysis of EEG recordings based on the concept of permutation entropy (PE) is proposed. The performance of PE are tested on a database of 24 patients affected by absence (generalized) seizures. The results achieved are compared to the dynamical behavior of the EEG of 40 healthy subjects. Being PE a feature which is dependent on two parameters, an extensive study of the sensitivity of the performance of PE with respect to the parameters' setting was carried out on scalp EEG. Once the optimal PE configuration was determined, its ability to detect the different brain states was evaluated. According to the results here presented, it seems that the widely accepted model of "jump" transition to absence seizure should be in some cases coupled (or substituted) by a gradual transition model characteristic of self-organizing networks. Indeed, it appears that the transition to the epileptic status is heralded before the preictal state, ever since the interictal stages. As a matter of fact, within the limits of the analyzed database, the frontal-temporal scalp areas appear constantly associated to PE levels higher compared to the remaining electrodes, whereas the parieto-occipital areas appear associated to lower PE values. The EEG of healthy subjects neither shows any similar dynamic behavior nor exhibits any recurrent portrait in PE topography.

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

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Ridha Djemal

2017-01-01

Full Text Available Autism spectrum disorder (ASD is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD of autism ‎based on electroencephalography (EEG signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT, entropy (En, and artificial neural network (ANN. DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia.

Unilateral Laryngeal Pacing System and Its Functional Evaluation

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Taiping Zeng

2017-01-01

Full Text Available Goal. To establish a reliable instrumental system for synchronized reactivation of a unilaterally paralyzed vocal fold and evaluate its functional feasibility. Methods. Unilateral vocal fold paralysis model was induced by destruction of the left recurrent laryngeal nerve (RLN in anesthetized dogs. With a micro controller-based electronic system, electromyography (EMG signals from cricothyroid (CT muscle on the ipsilateral side were recorded and used to trigger pacing of paralyzed vocalis muscles. The dynamic movement of vocal folds was continuously monitored using an endoscope, and the opening and closing of the glottis were quantified with customized imaging processing software. Results. The recorded video images showed that left side vocal fold was obviously paralyzed after destructing the RLN. Using the pacing system with feedback triggering EMG signals from the ipsilateral CT muscle, the paralyzed vocal fold was successfully reactivated, and its movement was shown to be synchronized with the healthy side. Significance. The developed unilateral laryngeal pacing system triggered by EMG from the ipsilateral side CT muscle could be successfully used in unilateral vocal fold paralysis with the advantage of avoiding disturbance to the healthy side muscles.

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...

Drowsiness detection for single channel EEG by DWT best m-term approximation

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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.

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...

Proepileptic patterns in EEG of WAG/Rij rats

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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.

[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.

myBrain: a novel EEG embedded system for epilepsy monitoring.

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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

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

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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.

Prolonged self-paced exercise in the heat - environmental factors affecting performance

DEFF Research Database (Denmark)

Junge, Nicklas; Jørgensen, Rasmus; Flouris, Andreas D

2016-01-01

) was on average reduced by 15% in the 14 studies that fulfilled the inclusion criteria. Ambient temperature per se was a poor predictor of the integrated environmental heat stress and 2 of the prevailing heat stress indices (WBGT and UTCI) failed to predict the environmental influence on performance. The weighing......In this review we examine how self-paced performance is affected by environmental heat stress factors during cycling time trial performance as well as considering the effects of exercise mode and heat acclimatization. Mean power output during prolonged cycling time trials in the heat (≥30°C...... of wind speed appears to be too low for predicting the effect for cycling in trained acclimatized subjects, where performance may be maintained in outdoor time trials at ambient temperatures as high as 36°C (36°C UTCI; 28°C WBGT). Power output during indoor trials may also be maintained with temperatures...

Activation of the Prefrontal Cortex While Performing a Task at Preferred Slow Pace and Metronome Slow Pace: A Functional Near-Infrared Spectroscopy Study

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Kaori Shimoda

2014-01-01

Full Text Available Individuals have a preferred pace at which they perform voluntary repetitive movements. Previous studies have reported that greater activation of the prefrontal cortex was observed during self-initiated movements than during externally triggered movements. The purpose of the present study is to compare the activation of the prefrontal cortex induced when the subjects performed a peg-board task at their preferred slow pace (PSP, the self-initiated condition with that induced when they performed the same task at metronome slow pace (MSP, the externally triggered condition using functional near-infrared spectroscopy. Healthy subjects performed the task while sitting in a chair. By assessing the activated channels individually, we confirmed that all of the prefrontal regions of interest were activated by both tasks. In the second-level analyses, we found that the activation detected in the frontopolar cortex (FPPFC; Brodmann area 10 was higher during the PSP task than during the MSP task. The FPPFC is known to be at the top of prefrontal hierarchy, and specifically involved in evaluating self-generated information. In addition, the FPPFC plays a role in coordinating lateral prefrontal cortex. In the present study, the subjects evaluated and managed the internally generated PSP by coordinating the activity of other lower level prefrontal regions.

Activation of the prefrontal cortex while performing a task at preferred slow pace and metronome slow pace: a functional near-infrared spectroscopy study.

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Shimoda, Kaori; Moriguchi, Yoshiya; Tsuchiya, Kenji; Katsuyama, Shiori; Tozato, Fusae

2014-01-01

Individuals have a preferred pace at which they perform voluntary repetitive movements. Previous studies have reported that greater activation of the prefrontal cortex was observed during self-initiated movements than during externally triggered movements. The purpose of the present study is to compare the activation of the prefrontal cortex induced when the subjects performed a peg-board task at their preferred slow pace (PSP, the self-initiated condition) with that induced when they performed the same task at metronome slow pace (MSP, the externally triggered condition) using functional near-infrared spectroscopy. Healthy subjects performed the task while sitting in a chair. By assessing the activated channels individually, we confirmed that all of the prefrontal regions of interest were activated by both tasks. In the second-level analyses, we found that the activation detected in the frontopolar cortex (FPPFC; Brodmann area 10) was higher during the PSP task than during the MSP task. The FPPFC is known to be at the top of prefrontal hierarchy, and specifically involved in evaluating self-generated information. In addition, the FPPFC plays a role in coordinating lateral prefrontal cortex. In the present study, the subjects evaluated and managed the internally generated PSP by coordinating the activity of other lower level prefrontal regions.

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

International Nuclear Information System (INIS)

Gutierrez, David

2008-01-01

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

Young children pause on phrase boundaries in self-paced music listening: The role of harmonic cues.

Science.gov (United States)

Kragness, Haley E; Trainor, Laurel J

2018-05-01

Proper segmentation of auditory streams is essential for understanding music. Many cues, including meter, melodic contour, and harmony, influence adults' perception of musical phrase boundaries. To date, no studies have examined young children's musical grouping in a production task. We used a musical self-pacing method to investigate (1) whether dwell times index young children's musical phrase grouping and, if so, (2) whether children dwell longer on phrase boundaries defined by harmonic cues specifically. In Experiment 1, we asked 3-year-old children to self-pace through chord progressions from Bach chorales (sequences in which metrical, harmonic, and melodic contour grouping cues aligned) by pressing a computer key to present each chord in the sequence. Participants dwelled longer on chords in the 8th position, which corresponded to phrase endings. In Experiment 2, we tested 3-, 4-, and 7-year-old children's sensitivity to harmonic cues to phrase grouping when metrical regularity cues and melodic contour cues were misaligned with the harmonic phrase boundaries. In this case, 7 and 4 year olds but not 3 year olds dwelled longer on harmonic phrase boundaries, suggesting that the influence of harmonic cues on phrase boundary perception develops substantially between 3 and 4 years of age in Western children. Overall, we show that the musical dwell time method is child-friendly and can be used to investigate various aspects of young children's musical understanding, including phrase grouping and harmonic knowledge. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Neural representation of cost-benefit selections in rat anterior cingulate cortex in self-paced decision making.

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Wang, Shuai; Shi, Yi; Li, Bao-Ming

2017-03-01

The anterior cingulate cortex (ACC) is crucial for decision making which involves the processing of cost-benefit information. Our previous study has shown that ACC is essential for self-paced decision making. However, it is unclear how ACC neurons represent cost-benefit selections during the decision-making process. In the present study, we trained rats on the same "Do More Get More" (DMGM) task as in our previous work. In each trial, the animals stand upright and perform a sustained nosepoke of their own will to earn a water reward, with the amount of reward positively correlated to the duration of the nosepoke (i.e., longer nosepokes earn larger rewards). We then recorded ACC neuronal activity on well-trained rats while they were performing the DMGM task. Our results show that (1) approximately 3/5 ACC neurons (296/496, 59.7%) exhibited changes in firing frequency that were temporally locked with the main events of the DMGM task; (2) about 1/5 ACC neurons (101/496, 20.4%) or 1/3 of the event-modulated neurons (101/296, 34.1%) showed differential firing rate changes for different cost-benefit selections; and (3) many ACC neurons exhibited linear encoding of the cost-benefit selections in the DMGM task events. These results suggest that ACC neurons are engaged in encoding cost-benefit information, thus represent the selections in self-paced decision making. Copyright © 2016 Elsevier Inc. All rights reserved.

Applying Improved Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

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Ming-ai Li

2017-01-01

Full Text Available Electroencephalography (EEG is considered the output of a brain and it is a bioelectrical signal with multiscale and nonlinear properties. Motor Imagery EEG (MI-EEG not only has a close correlation with the human imagination and movement intention but also contains a large amount of physiological or disease information. As a result, it has been fully studied in the field of rehabilitation. To correctly interpret and accurately extract the features of MI-EEG signals, many nonlinear dynamic methods based on entropy, such as Approximate Entropy (ApEn, Sample Entropy (SampEn, Fuzzy Entropy (FE, and Permutation Entropy (PE, have been proposed and exploited continuously in recent years. However, these entropy-based methods can only measure the complexity of MI-EEG based on a single scale and therefore fail to account for the multiscale property inherent in MI-EEG. To solve this problem, Multiscale Sample Entropy (MSE, Multiscale Permutation Entropy (MPE, and Multiscale Fuzzy Entropy (MFE are developed by introducing scale factor. However, MFE has not been widely used in analysis of MI-EEG, and the same parameter values are employed when the MFE method is used to calculate the fuzzy entropy values on multiple scales. Actually, each coarse-grained MI-EEG carries the characteristic information of the original signal on different scale factors. It is necessary to optimize MFE parameters to discover more feature information. In this paper, the parameters of MFE are optimized independently for each scale factor, and the improved MFE (IMFE is applied to the feature extraction of MI-EEG. Based on the event-related desynchronization (ERD/event-related synchronization (ERS phenomenon, IMFE features from multi channels are fused organically to construct the feature vector. Experiments are conducted on a public dataset by using Support Vector Machine (SVM as a classifier. The experiment results of 10-fold cross-validation show that the proposed method yields

Pacing, Pixels, and Paper: Flexibility in Learning Words from Flashcards

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Kara Sage

2016-08-01

Full Text Available The present study focused on how self-control over pace might help learners successfully extract information from digital learning aids. Past research has indicated that too much control over pace can be overwhelming, but too little control over pace can be ineffective. Within the popular self-testing domain of flashcards, we sought to elucidate the optimal level of user control for digital learning and compare learning outcomes between paper and digital flashcards. College students learned vocabulary from paper flashcards or one of several digital flashcard versions and were scored on their memory recall and asked about their perceptions of the learning process. With digital flashcards, students were randomly assigned to an automatic slideshow of cards with no user control, automatic slideshow with pre-set pauses, automatic slideshow where users could press the spacebar to pause at any time, or a self-paced slideshow with complete user control. Users reported feeling more in control when indeed having some control, but ultimately memory recall, cognitive load, and satisfaction were similar across the five versions. However, memory recall was positively related to user satisfaction with their specific flashcard set, and negatively related to users’ perceived mental effort and difficulty. Notably, whether paper or digital, students showed individual variability in how they advanced through the words. This research adds to the educational literature by suggesting that paper and digital flashcards are equally viable options for students. Given differences between individual users and the connection between satisfaction and recall, individualistic options that offer, but do not force, some control over pace seem ideal. Paper flashcards may already include such options, and e-flashcards should offer similar adaptive features to appeal to a wide variety of users.

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

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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.

FFT transformed quantitative EEG analysis of short term memory load.

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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.

Comparing the Effectiveness of Self-Paced and Collaborative Frame-of-Reference Training on Rater Accuracy in a Large-Scale Writing Assessment

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Raczynski, Kevin R.; Cohen, Allan S.; Engelhard, George, Jr.; Lu, Zhenqiu

2015-01-01

There is a large body of research on the effectiveness of rater training methods in the industrial and organizational psychology literature. Less has been reported in the measurement literature on large-scale writing assessments. This study compared the effectiveness of two widely used rater training methods--self-paced and collaborative…

Recording human cortical population spikes non-invasively--An EEG tutorial.

Science.gov (United States)

Waterstraat, Gunnar; Fedele, Tommaso; Burghoff, Martin; Scheer, Hans-Jürgen; Curio, Gabriel

2015-07-30

Non-invasively recorded somatosensory high-frequency oscillations (sHFOs) evoked by electric nerve stimulation are markers of human cortical population spikes. Previously, their analysis was based on massive averaging of EEG responses. Advanced neurotechnology and optimized off-line analysis can enhance the signal-to-noise ratio of sHFOs, eventually enabling single-trial analysis. The rationale for developing dedicated low-noise EEG technology for sHFOs is unfolded. Detailed recording procedures and tailored analysis principles are explained step-by-step. Source codes in Matlab and Python are provided as supplementary material online. Combining synergistic hardware and analysis improvements, evoked sHFOs at around 600 Hz ('σ-bursts') can be studied in single-trials. Additionally, optimized spatial filters increase the signal-to-noise ratio of components at about 1 kHz ('κ-bursts') enabling their detection in non-invasive surface EEG. sHFOs offer a unique possibility to record evoked human cortical population spikes non-invasively. The experimental approaches and algorithms presented here enable also non-specialized EEG laboratories to combine measurements of conventional low-frequency EEG with the analysis of concomitant cortical population spike responses. Copyright © 2014 Elsevier B.V. All rights reserved.

Robot-Aided Upper-Limb Rehabilitation Based on Motor Imagery EEG

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Baoguo Xu

2011-09-01

Full Text Available Stroke is a leading cause of disability worldwide. In this paper, a novel robot-assisted rehabilitation system based on motor imagery electroencephalography (EEG is developed for regular training of neurological rehabilitation for upper limb stroke patients. Firstly, three-dimensional animation was used to guide the patient image the upper limb movement and EEG signals were acquired by EEG amplifier. Secondly, eigenvectors were extracted by harmonic wavelet transform (HWT and linear discriminant analysis (LDA classifier was utilized to classify the pattern of the left and right upper limb motor imagery EEG signals. Finally, PC triggered the upper limb rehabilitation robot to perform motor therapy and gave the virtual feedback. Using this robot-assisted upper limb rehabilitation system, the patient's EEG of upper limb movement imagination is translated to control rehabilitation robot directly. Consequently, the proposed rehabilitation system can fully explore the patient's motivation and attention and directly facilitate upper limb post-stroke rehabilitation therapy. Experimental results on unimpaired participants were presented to demonstrate the feasibility of the rehabilitation system. Combining robot-assisted training with motor imagery-based BCI will make future rehabilitation therapy more effective. Clinical testing is still required for further proving this assumption.

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...

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

Science.gov (United States)

Wang, Ching-Sung

2012-01-01

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

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.

Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification

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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.

Automatic classification of background EEG activity in healthy and sick neonates

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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%.

Non-linear Analysis of Scalp EEG by Using Bispectra: The Effect of the Reference Choice

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Federico Chella

2017-05-01

Full Text Available Bispectral analysis is a signal processing technique that makes it possible to capture the non-linear and non-Gaussian properties of the EEG signals. It has found various applications in EEG research and clinical practice, including the assessment of anesthetic depth, the identification of epileptic seizures, and more recently, the evaluation of non-linear cross-frequency brain functional connectivity. However, the validity and reliability of the indices drawn from bispectral analysis of EEG signals are potentially biased by the use of a non-neutral EEG reference. The present study aims at investigating the effects of the reference choice on the analysis of the non-linear features of EEG signals through bicoherence, as well as on the estimation of cross-frequency EEG connectivity through two different non-linear measures, i.e., the cross-bicoherence and the antisymmetric cross-bicoherence. To this end, four commonly used reference schemes were considered: the vertex electrode (Cz, the digitally linked mastoids, the average reference, and the Reference Electrode Standardization Technique (REST. The reference effects were assessed both in simulations and in a real EEG experiment. The simulations allowed to investigated: (i the effects of the electrode density on the performance of the above references in the estimation of bispectral measures; and (ii the effects of the head model accuracy in the performance of the REST. For real data, the EEG signals recorded from 10 subjects during eyes open resting state were examined, and the distortions induced by the reference choice in the patterns of alpha-beta bicoherence, cross-bicoherence, and antisymmetric cross-bicoherence were assessed. The results showed significant differences in the findings depending on the chosen reference, with the REST providing superior performance than all the other references in approximating the ideal neutral reference. In conclusion, this study highlights the importance of

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...

Presentation Time Concerning System-Paced Multimedia Instructions and the Superiority of Learner Pacing

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Stiller, Klaus D.; Petzold, Kirstin; Zinnbauer, Peter

2011-01-01

The superiority of learner-paced over system-paced instructions was demonstrated in multiple experiments. In these experiments, the system-paced presentations were highly speeded, causing cognitive overload, while the learner-paced instructions allowed adjustments of the presentational flow to the learner's needs by pacing facilities, mostly…

A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

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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.

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...

Driver-response relationships between frontal EEG and respiration during affective audiovisual stimuli.

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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.

Faster self-paced rate of drinking for alcohol mixed with energy drinks versus alcohol alone.

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Marczinski, Cecile A; Fillmore, Mark T; Maloney, Sarah F; Stamates, Amy L

2017-03-01

The consumption of alcohol mixed with energy drinks (AmED) has been associated with higher rates of binge drinking and impaired driving when compared with alcohol alone. However, it remains unclear why the risks of use of AmED are heightened compared with alcohol alone even when the doses of alcohol consumed are similar. Therefore, the purpose of this laboratory study was to investigate if the rate of self-paced beverage consumption was faster for a dose of AmED versus alcohol alone using a double-blind, within-subjects, placebo-controlled study design. Participants (n = 16) of equal gender who were social drinkers attended 4 separate test sessions that involved consumption of alcohol (1.97 ml/kg vodka) and energy drinks, alone and in combination. On each test day, the dose assigned was divided into 10 cups. Participants were informed that they would have a 2-h period to consume the 10 drinks. After the self-paced drinking period, participants completed a cued go/no-go reaction time (RT) task and subjective ratings of stimulation and sedation. The results indicated that participants consumed the AmED dose significantly faster (by ∼16 min) than the alcohol dose. For the performance task, participants' mean RTs were slower in the alcohol conditions and faster in the energy-drink conditions. In conclusion, alcohol consumers should be made aware that rapid drinking might occur for AmED beverages, thus heightening alcohol-related safety risks. The fast rate of drinking may be related to the generalized speeding of responses after energy-drink consumption. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

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

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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.

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...

Anti-deception: reliable EEG-based biometrics with real-time capability from the neural response of face rapid serial visual presentation.

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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.

Will the Conscious-Subconscious Pacing Quagmire Help Elucidate the Mechanisms of Self-Paced Exercise? New Opportunities in Dual Process Theory and Process Tracing Methods.

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Micklewright, Dominic; Kegerreis, Sue; Raglin, John; Hettinga, Florentina

2017-07-01

The extent to which athletic pacing decisions are made consciously or subconsciously is a prevailing issue. In this article we discuss why the one-dimensional conscious-subconscious debate that has reigned in the pacing literature has suppressed our understanding of the multidimensional processes that occur in pacing decisions. How do we make our decisions in real-life competitive situations? What information do we use and how do we respond to opponents? These are questions that need to be explored and better understood, using smartly designed experiments. The paper provides clarity about key conscious, preconscious, subconscious and unconscious concepts, terms that have previously been used in conflicting and confusing ways. The potential of dual process theory in articulating multidimensional aspects of intuitive and deliberative decision-making processes is discussed in the context of athletic pacing along with associated process-tracing research methods. In attempting to refine pacing models and improve training strategies and psychological skills for athletes, the dual-process framework could be used to gain a clearer understanding of (1) the situational conditions for which either intuitive or deliberative decisions are optimal; (2) how intuitive and deliberative decisions are biased by things such as perception, emotion and experience; and (3) the underlying cognitive mechanisms such as memory, attention allocation, problem solving and hypothetical thought.

Rett syndrome: EEG presentation.

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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.

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

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Wang, Deng; Miao, Duoqian; Blohm, Gunnar

2012-01-01

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

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

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

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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.

Robot-Aided Upper-Limb Rehabilitation Based on Motor Imagery EEG

Directory of Open Access Journals (Sweden)

Baoguo Xu

2011-09-01

Full Text Available Stroke is a leading cause of disability worldwide. In this paper, a novel robot‐assisted rehabilitation system based on motor imagery electroencephalography (EEG is developed for regular training of neurological rehabilitation for upper limb stroke patients. Firstly, three‐dimensional animation was used to guide the patient image the upper limb movement and EEG signals were acquired by EEG amplifier. Secondly, eigenvectors were extracted by harmonic wavelet transform (HWT and linear discriminant analysis (LDA classifier was utilized to classify the pattern of the left and right upper limb motor imagery EEG signals. Finally, PC triggered the upper limb rehabilitation robot to perform motor therapy and gave the virtual feedback. Using this robot‐assisted upper limb rehabilitation system, the patientʹs EEG of upper limb movement imagination is translated to control rehabilitation robot directly. Consequently, the proposed rehabilitation system can fully explore the patientʹs motivation and attention and directly facilitate upper limb post‐stroke rehabilitation therapy. Experimental results on unimpaired participants were presented to demonstrate the feasibility of the rehabilitation system. Combining robot‐assisted training with motor imagery‐ based BCI will make future rehabilitation therapy more effective. Clinical testing is still required for further proving this assumption.

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

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

Science.gov (United States)

Mu, Zhendong; Yin, Jinhai; Hu, Jianfeng

2018-01-01

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

Introduction to Physics (Mechanics): A Semi-Self Paced Approach.

Science.gov (United States)

Schlenker, Richard M.

Presented is a guide for an introductory college level physics course in mechanics. The course is contract graded and allows students to proceed at their own pace; however, lectures, problem solving sessions, and laboratory sessions are included. Students on an independent basis review video tapes, film loops, library study, and conduct an…

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.

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.

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.

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

Mechanical Alterations during 800-m Self-Paced Track Running.

Science.gov (United States)

Girard, Olivier; Millet, Gregoire P; Micallef, Jean-Paul

2017-04-01

We assessed the time course of running mechanical alterations during an 800-m. On a 200-m indoor track, 18 physical education students performed an 800-m self-paced run. Once per lap, ground reaction forces were measured by a 5-m-long force platform system, and used to determine running kinetics/kinematics and spring-mass characteristics. Compared with 100 m (19.4±1.8 km.h -1 ) running velocity progressively decreased at 300, 500 m but levelled-off at 700 m marks (-5.7±4.6, -10.4±8.3, and -9.1±13.5%, respectively; Ppush-off forces (-5.1±7.2%, P0.05) and leg compression (+2.8±3.9%; P>0.05) remained unchanged, whereas centre of mass vertical displacement (+24.0±7.0%; P0.05). During an 800 m by physical education students, highest running velocity was achieved early during the run, with a progressive decrease in the second half of the trial. While vertical ground force characteristics remained unchanged, non-specialist runners produced lower peak braking and push-off forces, in turn leading to shorter stride length. Spring-mass model characteristics changed toward lower vertical stiffness values, whereas leg stiffness did not change. © Georg Thieme Verlag KG Stuttgart · New York.

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.

Self-Paced Segmentation of Written Words on a Touchscreen Tablet Promotes the Oral Production of Nonverbal and Minimally Verbal Children with Autism

Science.gov (United States)

Vernay, Frédérique; Kahina, Harma; Thierry, Marrone; Jean-Yves, Roussey

2017-01-01

We investigated in a pilot study the effects of various types of visual mediation (photos, written words and self-paced syllabic segmentation of written words displayed on a touchscreen tablet) that are thought to facilitate the oral production of nonverbal and minimally verbal children with autism, according to the participants' level of oral…

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

Measurement and modification of the EEG and related behavior

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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

Activity Pacing Self-Management in Chronic Fatigue Syndrome: A Randomized Controlled Trial

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van Eupen, Inge; Meirte, Jill; Van Cauwenbergh, Deborah; Moorkens, Greta; Meeus, Mira; Nijs, Jo

2015-01-01

OBJECTIVE. The objective of this study was to evaluate the effectiveness of an activity pacing self-management (APSM) intervention in improving performance of daily life activities in women with chronic fatigue syndrome (CFS). METHOD. A total of 33 women with CFS (age 41.1 ± 11.2 yr) were randomly allocated to APSM (experimental group; n = 16) or relaxation (control group; n = 17). Main outcome measures included the Canadian Occupational Performance Measure (COPM; primary) and Checklist Individual Strength (CIS). RESULTS. COPM scores changed significantly over time in both groups (p = .03). The change in Satisfaction scores showed a significant difference in favor only of APSM (effect size = 0.74 [0.11, 1.4]). CIS scores decreased significantly in the experimental group only (p < .01). CONCLUSION. APSM was found to be feasible and effective in optimizing participation in desired daily life activities in women with CFS. Replication in a larger sample with long-term follow-up is required. PMID:26356665

Activity Pacing Self-Management in Chronic Fatigue Syndrome: A Randomized Controlled Trial.

Science.gov (United States)

Kos, Daphne; van Eupen, Inge; Meirte, Jill; Van Cauwenbergh, Deborah; Moorkens, Greta; Meeus, Mira; Nijs, Jo

2015-01-01

To evaluate the effectiveness of an activity pacing self-management (APSM) intervention in improving performance of daily life activities in women with chronic fatigue syndrome (CFS). A total of 33 women with CFS (age 41.1±11.2 yr) were randomly allocated to APSM (experimental group; n=16) or relaxation (control group; n=17). Main outcome measures included the Canadian Occupational Performance Measure (COPM; primary) and Checklist Individual Strength (CIS). COPM scores changed significantly over time in both groups (p=.03). The change in Satisfaction scores showed a significant difference in favor only of APSM (effect size=0.74 [0.11, 1.4]). CIS scores decreased significantly in the experimental group only (p<.01). APSM was found to be feasible and effective in optimizing participation in desired daily life activities in women with CFS. Replication in a larger sample with long-term follow-up is required. Copyright © 2015 by the American Occupational Therapy Association, Inc.

Automatic identification and removal of ocular artifacts in EEG--improved adaptive predictor filtering for portable applications.

Science.gov (United States)

Zhao, Qinglin; Hu, Bin; Shi, Yujun; Li, Yang; Moore, Philip; Sun, Minghou; Peng, Hong

2014-06-01

Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.

Chaos based encryption system for encrypting electroencephalogram signals.

Science.gov (United States)

Lin, Chin-Feng; Shih, Shun-Han; Zhu, Jin-De

2014-05-01

In the paper, we use the Microsoft Visual Studio Development Kit and C# programming language to implement a chaos-based electroencephalogram (EEG) encryption system involving three encryption levels. A chaos logic map, initial value, and bifurcation parameter for the map were used to generate Level I chaos-based EEG encryption bit streams. Two encryption-level parameters were added to these elements to generate Level II chaos-based EEG encryption bit streams. An additional chaotic map and chaotic address index assignment process was used to implement the Level III chaos-based EEG encryption system. Eight 16-channel EEG Vue signals were tested using the encryption system. The encryption was the most rapid and robust in the Level III system. The test yielded superior encryption results, and when the correct deciphering parameter was applied, the EEG signals were completely recovered. However, an input parameter error (e.g., a 0.00001 % initial point error) causes chaotic encryption bit streams, preventing the recovery of 16-channel EEG Vue signals.

Magnetoencephalography signals are influenced by skull defects.

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Lau, S; Flemming, L; Haueisen, J

2014-08-01

Magnetoencephalography (MEG) signals had previously been hypothesized to have negligible sensitivity to skull defects. The objective is to experimentally investigate the influence of conducting skull defects on MEG and EEG signals. A miniaturized electric dipole was implanted in vivo into rabbit brains. Simultaneous recording using 64-channel EEG and 16-channel MEG was conducted, first above the intact skull and then above a skull defect. Skull defects were filled with agar gels, which had been formulated to have tissue-like homogeneous conductivities. The dipole was moved beneath the skull defects, and measurements were taken at regularly spaced points. The EEG signal amplitude increased 2-10 times, whereas the MEG signal amplitude reduced by as much as 20%. The EEG signal amplitude deviated more when the source was under the edge of the defect, whereas the MEG signal amplitude deviated more when the source was central under the defect. The change in MEG field-map topography (relative difference measure, RDM(∗)=0.15) was geometrically related to the skull defect edge. MEG and EEG signals can be substantially affected by skull defects. MEG source modeling requires realistic volume conductor head models that incorporate skull defects. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

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)

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,

Decreased Number of Self-Paced Saccades in Post-Concussion Syndrome Associated with Higher Symptom Burden and Reduced White Matter Integrity.

Science.gov (United States)

Taghdiri, Foad; Chung, Jonathan; Irwin, Samantha; Multani, Namita; Tarazi, Apameh; Ebraheem, Ahmed; Khodadadi, Mozghan; Goswami, Ruma; Wennberg, Richard; Mikulis, David; Green, Robin; Davis, Karen; Tator, Charles; Eizenman, Moshe; Tartaglia, Maria Carmela

2018-03-01

The aim of this study was to examine the potential utility of a self-paced saccadic eye movement as a marker of post-concussion syndrome (PCS) and monitoring the recovery from PCS. Fifty-nine persistently symptomatic participants with at least two concussions performed the self-paced saccade (SPS) task. We evaluated the relationships between the number of SPSs and 1) number of self-reported concussion symptoms, and 2) integrity of major white matter (WM) tracts (as measured by fractional anisotropy [FA] and mean diffusivity) that are directly or indirectly involved in saccadic eye movements and often affected by concussion. These tracts included the uncinate fasciculus (UF), cingulum (Cg) and its three subcomponents (subgenual, retrosplenial, and parahippocampal), superior longitudinal fasciculus, and corpus callosum. Mediation analyses were carried out to examine whether specific WM tracts (left UF and left subgenual Cg) mediated the relationship between the number of SPSs and 1) interval from last concussion or 2) total number of self-reported symptoms. The number of SPSs was negatively correlated with the total number of self-reported symptoms (r = -0.419, p = 0.026). The number of SPSs were positively correlated with FA of left UF and left Cg (r = 0.421, p = 0.013 and r = 0.452, p = 0.008; respectively). FA of the subgenual subcomponent of the left Cg partially mediated the relationship between the total number of symptoms and the number of SPSs, while FA of the left UF mediated the relationship between interval from last concussion and the number of SPSs. In conclusion, SPS testing as a fast and objective assessment may reflect symptom burden in patients with PCS. In addition, since the number of SPSs is associated with the integrity of some WM tracts, it may be useful as a diagnostic biomarker in patients with PCS.

Factors affecting the regulation of pacing: current perspectives

Directory of Open Access Journals (Sweden)

Mauger AR

2014-09-01

Full Text Available Alexis R Mauger Endurance Research Group, School of Sport and Exercise Sciences, Faculty of Science, University of Kent, Chatham, UK Abstract: During prolonged dynamic and rhythmic exercise, muscular pain and discomfort arises as a result of an increased concentration of deleterious metabolites. Sensed by peripheral nociceptors and transmitted via afferent feedback to the brain, this provides important information regarding the physiological state of the muscle. These sensations ultimately contribute to what is termed "exercise-induced pain". Despite being well recognized by athletes and coaches, and suggested to be integral to exercise performance, this construct has largely escaped attention in experimental work. This perspective article highlights the current understanding of pacing in endurance performance, and the causes of exercise-induced pain. A new perspective is described, which proposes how exercise-induced pain may be a contributing factor in helping individuals to regulate their work rate during exercise and thus provides an important construct in pacing. Keywords: pain, exercise-induced pain, discomfort, exercise performance, self-paced

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

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

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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.

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

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 ...

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.

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.

Application of decision-making theory to the regulation of muscular work rate during self-paced competitive endurance activity.

Science.gov (United States)

Renfree, Andrew; Martin, Louise; Micklewright, Dominic; St Clair Gibson, Alan

2014-02-01

Successful participation in competitive endurance activities requires continual regulation of muscular work rate in order to maximise physiological performance capacities, meaning that individuals must make numerous decisions with regards to the muscular work rate selected at any point in time. Decisions relating to the setting of appropriate goals and the overall strategic approach to be utilised are made prior to the commencement of an event, whereas tactical decisions are made during the event itself. This review examines current theories of decision-making in an attempt to explain the manner in which regulation of muscular work is achieved during athletic activity. We describe rational and heuristic theories, and relate these to current models of regulatory processes during self-paced exercise in an attempt to explain observations made in both laboratory and competitive environments. Additionally, we use rational and heuristic theories in an attempt to explain the influence of the presence of direct competitors on the quality of the decisions made during these activities. We hypothesise that although both rational and heuristic models can plausibly explain many observed behaviours in competitive endurance activities, the complexity of the environment in which such activities occur would imply that effective rational decision-making is unlikely. However, at present, many proposed models of the regulatory process share similarities with rational models. We suggest enhanced understanding of the decision-making process during self-paced activities is crucial in order to improve the ability to understand regulation of performance and performance outcomes during athletic activity.

NIRS-EEG joint imaging during transcranial direct current stimulation: Online parameter estimation with an autoregressive model.

Science.gov (United States)

Sood, Mehak; Besson, Pierre; Muthalib, Makii; Jindal, Utkarsh; Perrey, Stephane; Dutta, Anirban; Hayashibe, Mitsuhiro

2016-12-01

Transcranial direct current stimulation (tDCS) has been shown to perturb both cortical neural activity and hemodynamics during (online) and after the stimulation, however mechanisms of these tDCS-induced online and after-effects are not known. Here, online resting-state spontaneous brain activation may be relevant to monitor tDCS neuromodulatory effects that can be measured using electroencephalography (EEG) in conjunction with near-infrared spectroscopy (NIRS). We present a Kalman Filter based online parameter estimation of an autoregressive (ARX) model to track the transient coupling relation between the changes in EEG power spectrum and NIRS signals during anodal tDCS (2mA, 10min) using a 4×1 ring high-definition montage. Our online ARX parameter estimation technique using the cross-correlation between log (base-10) transformed EEG band-power (0.5-11.25Hz) and NIRS oxy-hemoglobin signal in the low frequency (≤0.1Hz) range was shown in 5 healthy subjects to be sensitive to detect transient EEG-NIRS coupling changes in resting-state spontaneous brain activation during anodal tDCS. Conventional sliding window cross-correlation calculations suffer a fundamental problem in computing the phase relationship as the signal in the window is considered time-invariant and the choice of the window length and step size are subjective. Here, Kalman Filter based method allowed online ARX parameter estimation using time-varying signals that could capture transients in the coupling relationship between EEG and NIRS signals. Our new online ARX model based tracking method allows continuous assessment of the transient coupling between the electrophysiological (EEG) and the hemodynamic (NIRS) signals representing resting-state spontaneous brain activation during anodal tDCS. Published by Elsevier B.V.

Evaluation of EEG Features in Decoding Individual Finger Movements from One Hand

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Ran Xiao

2013-01-01

Full Text Available With the advancements in modern signal processing techniques, the field of brain-computer interface (BCI is progressing fast towards noninvasiveness. One challenge still impeding these developments is the limited number of features, especially movement-related features, available to generate control signals for noninvasive BCIs. A few recent studies investigated several movement-related features, such as spectral features in electrocorticography (ECoG data obtained through a spectral principal component analysis (PCA and direct use of EEG temporal data, and demonstrated the decoding of individual fingers. The present paper evaluated multiple movement-related features under the same task, that is, discriminating individual fingers from one hand using noninvasive EEG. The present results demonstrate the existence of a broadband feature in EEG to discriminate individual fingers, which has only been identified previously in ECoG. It further shows that multiple spectral features obtained from the spectral PCA yield an average decoding accuracy of 45.2%, which is significantly higher than the guess level (P<0.05 and other features investigated (P<0.05, including EEG spectral power changes in alpha and beta bands and EEG temporal data. The decoding of individual fingers using noninvasive EEG is promising to improve number of features for control, which can facilitate the development of noninvasive BCI applications with rich complexity.

Near-lossless multichannel EEG compression based on matrix and tensor decompositions.

Science.gov (United States)

Dauwels, Justin; Srinivasan, K; Reddy, M Ramasubba; Cichocki, Andrzej

2013-05-01

A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of “lossy plus residual coding,” consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

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

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

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 ...

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.

Driving behavior recognition using EEG data from a simulated car-following experiment.

Science.gov (United States)

Yang, Liu; Ma, Rui; Zhang, H Michael; Guan, Wei; Jiang, Shixiong

2017-11-23

Driving behavior recognition is the foundation of driver assistance systems, with potential applications in automated driving systems. Most prevailing studies have used subjective questionnaire data and objective driving data to classify driving behaviors, while few studies have used physiological signals such as electroencephalography (EEG) to gather data. To bridge this gap, this paper proposes a two-layer learning method for driving behavior recognition using EEG data. A simulated car-following driving experiment was designed and conducted to simultaneously collect data on the driving behaviors and EEG data of drivers. The proposed learning method consists of two layers. In Layer I, two-dimensional driving behavior features representing driving style and stability were selected and extracted from raw driving behavior data using K-means and support vector machine recursive feature elimination. Five groups of driving behaviors were classified based on these two-dimensional driving behavior features. In Layer II, the classification results from Layer I were utilized as inputs to generate a k-Nearest-Neighbor classifier identifying driving behavior groups using EEG data. Using independent component analysis, a fast Fourier transformation, and linear discriminant analysis sequentially, the raw EEG signals were processed to extract two core EEG features. Classifier performance was enhanced using the adaptive synthetic sampling approach. A leave-one-subject-out cross validation was conducted. The results showed that the average classification accuracy for all tested traffic states was 69.5% and the highest accuracy reached 83.5%, suggesting a significant correlation between EEG patterns and car-following behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

Information-Theoretical Analysis of EEG Microstate Sequences in Python

Directory of Open Access Journals (Sweden)

Frederic von Wegner

2018-06-01

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

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.

Higher-Order Spectrum in Understanding Nonlinearity in EEG Rhythms

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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.

Amplitude and phase dynamics associated with acoustically paced finger tapping

NARCIS (Netherlands)

Boonstra, T.W.; Daffertshofer, A.; Peper, C.E.; Beek, P.J.

2006-01-01

To gain insight into the brain activity associated with the performance of an acoustically paced synchronization task, we analyzed the amplitude and phase dynamics inherent in magnetoencephalographic (MEG) signals across frequency bands in order to discriminate between evoked and induced responses.

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Science.gov (United States)

Safieddine, Doha; Kachenoura, Amar; Albera, Laurent; Birot, Gwénaël; Karfoul, Ahmad; Pasnicu, Anca; Biraben, Arnaud; Wendling, Fabrice; Senhadji, Lotfi; Merlet, Isabelle

2012-12-01

Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that

A Biologically Inspired Approach to Frequency Domain Feature Extraction for EEG Classification

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Nurhan Gursel Ozmen

2018-01-01

Full Text Available Classification of electroencephalogram (EEG signal is important in mental decoding for brain-computer interfaces (BCI. We introduced a feature extraction approach based on frequency domain analysis to improve the classification performance on different mental tasks using single-channel EEG. This biologically inspired method extracts the most discriminative spectral features from power spectral densities (PSDs of the EEG signals. We applied our method on a dataset of six subjects who performed five different imagination tasks: (i resting state, (ii mental arithmetic, (iii imagination of left hand movement, (iv imagination of right hand movement, and (v imagination of letter “A.” Pairwise and multiclass classifications were performed in single EEG channel using Linear Discriminant Analysis and Support Vector Machines. Our method produced results (mean classification accuracy of 83.06% for binary classification and 91.85% for multiclassification that are on par with the state-of-the-art methods, using single-channel EEG with low computational cost. Among all task pairs, mental arithmetic versus letter imagination yielded the best result (mean classification accuracy of 90.29%, indicating that this task pair could be the most suitable pair for a binary class BCI. This study contributes to the development of single-channel BCI, as well as finding the best task pair for user defined applications.

EEG Analysis during complex diagnostic tasks in Nuclear Power Plants - Simulator-based Experimental Study

Energy Technology Data Exchange (ETDEWEB)

Ha, Jun Su; Seong, Poong Hyun [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)

2005-07-01

In literature, there are a lot of studies based on EEG signals during cognitive activities of human-beings but most of them dealt with simple cognitive activities such as transforming letters into Morse code, subtraction, reading, semantic memory search, visual search, memorizing a set of words and so on. In this work, EEG signals were analyzed during complex diagnostic tasks in NPP simulator-based environment. Investigated are the theta, alpha, beta, and gamma band EEG powers during the diagnostic tasks. The experimental design and procedure are represented in section 2 and the results are shown in section 3. Finally some considerations are discussed and the direction for the further work is proposed in section 4.

EEG Analysis during complex diagnostic tasks in Nuclear Power Plants - Simulator-based Experimental Study

International Nuclear Information System (INIS)

Ha, Jun Su; Seong, Poong Hyun

2005-01-01

In literature, there are a lot of studies based on EEG signals during cognitive activities of human-beings but most of them dealt with simple cognitive activities such as transforming letters into Morse code, subtraction, reading, semantic memory search, visual search, memorizing a set of words and so on. In this work, EEG signals were analyzed during complex diagnostic tasks in NPP simulator-based environment. Investigated are the theta, alpha, beta, and gamma band EEG powers during the diagnostic tasks. The experimental design and procedure are represented in section 2 and the results are shown in section 3. Finally some considerations are discussed and the direction for the further work is proposed in section 4