Использование метода независимых компонент для автоматического удаления артефактов ЭЭГ, связанных с движениями глаз

В роботі аналізується можливість використання методу незалежних компонент для часових рядів, а саме алгоритму TDSEP, для видалення складових сигналу ЕЕГ, джерелом яких є активність м’язів під час руху очей. Був запропонований алгоритм автоматичного видалення артефактів ЕЕГ та проведена оцінка його ефективності на реальних записах ЕЕГ.Background. Eye movement artifacts contained in EEG recordings hamper a lot the automatic processing and analysis of EEG signal. Therefore, the removal of such artifacts is important stage for any further signal processing. There are artifacts removal methods based on using wavelet transformation, regression analysis in the time and frequency domain, Principal component analysis and Independent component analysis. Methods. The novel method of automatic EEG eye movement artifacts removal based on Independent Component Analysis was proposed. The method utilizes the TDSEP algorithm for blind source separation. Own criteria for artifact components detection were used. The method was implemented with the Python programming language and tested on EEG signals recorded from two healthy volunteers. Results. Comparison of the effectiveness of the method was conducted with the participation of two experts. They were asked to review the EEG fragments before and after artifacts removal and evaluate the quality of artifacts removal. The average value of assessing the quality of artifacts removal was 4.83 for TDSEP based algorithm and 4.58 for FastICA based algorithm. Conclusion. The proposed method is more effective then method based on FastICA algorithm and using it for automatic EEG eye movement artifacts removal is expedient.В работе анализируется возможность использования метода независимых компонент для временных рядов, а именно алгоритма TDSEP, для удаления составляющих сигнала ЭЭГ, источником которых является активность мышц во время движения глаз. Был предложен алгоритм автоматического удаления артефактов ЭЭГ и проведена оценка его эффективности на реальных записях ЭЭГ

An EEG-based brain-computer interface for dual task driving detection

The development of brain-computer interfaces (BCI) for multiple applications has undergone extensive growth in recent years. Since distracted driving is a significant cause of traffic accidents, this study proposes one BCI system based on EEG for distracted driving. The removal of artifacts and the selection of useful brain sources are the essential and critical steps in the application of electroencephalography (EEG)-based BCI. In the first model, artifacts are removed, and useful brain sources are selected based on the independent component analysis (ICA). In the second model, all distracted and concentrated EEG epochs are recognized with a self-organizing map (SOM). This BCI system automatically identified independent components with artifacts for removal and detected distracted driving through the specific brain sources which are also selected automatically. The accuracy of the proposed system approached approximately 90% for the recognition of EEG epochs of distracted and concentrated driving according to the selected frontal and left motor components. © 2013

Artifact Removal Methods in EEG Recordings: A Review

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods

An EEG-based perceptual function integration network for application to drowsy driving

© 2015 Elsevier B.V. All rights reserved. Drowsy driving is among the most critical causes of fatal crashes. Thus, the development of an effective algorithm for detecting a driver's cognitive state demands immediate attention. For decades, studies have observed clear evidence using electroencephalography that the brain's rhythmic activities fluctuate from alertness to drowsiness. Recognition of this physiological signal is the major consideration of neural engineering for designing a feasible countermeasure. This study proposed a perceptual function integration system which used spectral features from multiple independent brain sources for application to recognize the driver's vigilance state. The analysis of brain spectral dynamics demonstrated physiological evidenced that the activities of the multiple cortical sources were highly related to the changes of the vigilance state. The system performances showed a robust and improved accuracy as much as 88% higher than any of results performed by a single-source approach

Independent component analysis of interictal fMRI in focal epilepsy: comparison with general linear model-based EEG-correlated fMRI

The general linear model (GLM) has been used to analyze simultaneous EEG–fMRI to reveal BOLD changes linked to interictal epileptic discharges (IED) identified on scalp EEG. This approach is ineffective when IED are not evident in the EEG. Data-driven fMRI analysis techniques that do not require an EEG derived model may offer a solution in these circumstances. We compared the findings of independent components analysis (ICA) and EEG-based GLM analyses of fMRI data from eight patients with focal epilepsy. Spatial ICA was used to extract independent components (IC) which were automatically classified as either BOLD-related, motion artefacts, EPI-susceptibility artefacts, large blood vessels, noise at high spatial or temporal frequency. The classifier reduced the number of candidate IC by 78%, with an average of 16 BOLD-related IC. Concordance between the ICA and GLM-derived results was assessed based on spatio-temporal criteria. In each patient, one of the IC satisfied the criteria to correspond to IED-based GLM result. The remaining IC were consistent with BOLD patterns of spontaneous brain activity and may include epileptic activity that was not evident on the scalp EEG. In conclusion, ICA of fMRI is capable of revealing areas of epileptic activity in patients with focal epilepsy and may be useful for the analysis of EEG–fMRI data in which abnormalities are not apparent on scalp EEG

Independent component analysis and source analysis of auditory evoked potentials for assessment of cochlear implant users

Source analysis of the Auditory Evoked Potential (AEP) has been used before to evaluate the maturation of the auditory system in both adult and children; in the same way, this technique could be applied to ongoing EEG recordings, in response to acoustic specific frequency stimuli, from children with cochlear implants (CI). This is done in oder to objectively assess the performance of this electronic device and the maturation of the child?s hearing. However, these recordings are contaminated by an artifact produced by the normal operation of the CI; this artifact in particular makes the detection and analysis of AEPs much harder and generates errors in the source analysis process. The artifact can be spatially filtered using Independent Component Analysis (ICA); in this research, three different ICA algorithms were compared in order to establish the more suited algorithm to remove the CI artifact. Additionally, we show that pre-processing the EEG recording, using a temporal ICA algorithm, facilitates not only the identification of the AEP peaks but also the source analysis procedure. From results obtained in this research and limited dataset of CI vs normal recordings, it is possible to conclude that the AEPs source locations change from the inferior temporal areas in the first 2 years after implantation to the superior temporal area after three years using the CIs, close to the locations obtained in normal hearing children. It is intended that the results of this research are used as an objective technique for a general evaluation of the performance of children with CIs

Використання методу незалежних компонент для автоматичного видалення артефактів ЕЕГ, пов’язаних з рухами очей

Background. Eye movement artifacts contained in EEG recordings hamper a lot the automatic processing and analysis of EEG signal. Therefore, the removal of such artifacts is important stage for any further signal processing. There are artifacts removal methods based on using wavelet transformation, regression analysis in the time and frequency domain, Principal component analysis and Independent component analysis. Methods. The novel method of automatic EEG eye movement artifacts removal based on Independent Component Analysis was proposed. The method utilizes the TDSEP algorithm for blind source separation. Own criteria for artifact components detection were used. The method was implemented with the Python programming language and tested on EEG signals recorded from two healthy volunteers. Results. Comparison of the effectiveness of the method was conducted with the participation of two experts. They were asked to review the EEG fragments before and after artifacts removal and evaluate the quality of artifacts removal. The average value of assessing the quality of artifacts removal was 4.83 for TDSEP based algorithm and 4.58 for FastICA based algorithm. Conclusion. The proposed method is more effective then method based on FastICA algorithm and using it for automatic EEG eye movement artifacts removal is expedient.В работе анализируется возможность использования метода независимых компонент для временных рядов, а именно алгоритма TDSEP, для удаления составляющих сигнала ЭЭГ, источником которых является активность мышц во время движения глаз. Был предложен алгоритм автоматического удаления артефактов ЭЭГ и проведена оценка его эффективности на реальных записях ЭЭГ.В роботі аналізується можливість використання методу незалежних компонент для часових рядів, а саме алгоритму TDSEP, для видалення складових сигналу ЕЕГ, джерелом яких є активність м’язів під час руху очей. Був запропонований алгоритм автоматичного видалення артефактів ЕЕГ та проведена оцінка його ефективності на реальних записах ЕЕГ

Automatic Sleep EEG Pattern Detection

Analýza mozkové aktivity je jednou z klícových vyšetrovacích metod v moderní spánkové medicíne a výzkumu.nalysis of recorded brain activity is one of the main investigation methods in modern sleep medicine and research

Predictive analysis of auditory attention from physiological signals

In recent years, there has been considerable interest in recording physiological signals from the human body to investigate various responses. Attention is one of the key aspects that physiologists, neuroscientists, and engineers have been exploring. Many theories have been established on auditory and visual selective attention. To date, the number of studies investigating the physiological responses of the human body to auditory attention on natural speech is, surprisingly, very limited, and there is a lack of public datasets. Investigating such physiological responses can open the door to new opportunities, as auditory attention plays a key role in many cognitive functionalities, thus impacting on learning and general task performance. In this thesis, we investigated auditory attention on the natural speech by processing physiological signals such as Electroencephalogram (EEG), Galvanic Skin Response (GSR), and Photoplethysmogram (PPG). An experiment was designed based on the well established dichotic listening task. In the experiment, we presented an audio stimulus under different auditory conditions: background noise level, length, and semanticity of the audio message. The experiment was conducted with 25 healthy, non-native speakers. The attention score was computed by counting the number of correctly identified words in the transcribed text response. All the physiological signals were labeled with their auditory condition and attention score. We formulated four predictive tasks exploiting the collected signals: Attention score, Noise level, Semanticity, and LWR (Listening, Writing, Resting, i.e., the state of the participant). In the first part, we analysed all the user text responses collected in the experiment. The statistical analysis reveals a strong dependency of the attention level on the auditory conditions. By applying hierarchical clustering, we could identify the experimental conditions that have similar effects on attention score. Significantly, the effect of semanticity appeared to vanish under high background noise. Then, analysing the signals, we found that the-state-of-the-art algorithms for artifact removal were inefficient for large datasets, as they require manual intervention. Thus, we introduced an EEG artifact removal algorithm with tuning parameters based on Wavelet Packet Decomposition (WPD). The proposed algorithm operates with two tuning parameters and three modes of wavelet filtering: Elimination, Linear Attenuation, and Soft-thresholding. Evaluating the algorithm performance, we observed that it outperforms state-of-the-art algorithms based on Independent Component Analysis (ICA). The evaluation was based on the spectrum, correlation, and distribution of the signals along with the performance in predictive tasks. We also demonstrate that a proper tuning of the algorithm parameters allows achieving further better results. After applying the artifact removal algorithm on EEG, we analysed the signals in terms of correlation of spectral bands of each electrode and attention score, semanticity, noise level, and state of the participant LWR). Next, we analyse the Event-Related Potential (ERP) on Listening, Writing and Resting segments of EEG signal, in addition to spectral analysis of GSR and PPG. With this thesis, we release the collected experimental dataset in the public domain, in order for the scientific community to further investigate the various auditory processing phenomena and their relation with EEG, GSR and PPG responses. The dataset can be used also to improve predictive tasks or design novel Brain-Computer-Interface (BCI) systems based on auditory attention. We also use the deeplearning approach to exploit the spatial relationship of EEG electrodes and inter-subject dependency of a model. As a domain application, we finally discuss the implications of auditory attention assessment for serious games and propose a 3-dimensional difficulty model to design game levels and dynamically adapt the difficulty to the player status

Processing and analysis of multichannel extracellular neuronal signals: state-of-the-art and challenges

In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data