ICLabel: An automated electroencephalographic independent component classifier, dataset, and website

The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low cost measure of mesoscale brain dynamics with high temporal resolution. Although signals recorded in parallel by multiple, near-adjacent EEG scalp electrode channels are highly-correlated and combine signals from many different sources, biological and non-biological, independent component analysis (ICA) has been shown to isolate the various source generator processes underlying those recordings. Independent components (IC) found by ICA decomposition can be manually inspected, selected, and interpreted, but doing so requires both time and practice as ICs have no particular order or intrinsic interpretations and therefore require further study of their properties. Alternatively, sufficiently-accurate automated IC classifiers can be used to classify ICs into broad source categories, speeding the analysis of EEG studies with many subjects and enabling the use of ICA decomposition in near-real-time applications. While many such classifiers have been proposed recently, this work presents the ICLabel project comprised of (1) an IC dataset containing spatiotemporal measures for over 200,000 ICs from more than 6,000 EEG recordings, (2) a website for collecting crowdsourced IC labels and educating EEG researchers and practitioners about IC interpretation, and (3) the automated ICLabel classifier. The classifier improves upon existing methods in two ways: by improving the accuracy of the computed label estimates and by enhancing its computational efficiency. The ICLabel classifier outperforms or performs comparably to the previous best publicly available method for all measured IC categories while computing those labels ten times faster than that classifier as shown in a rigorous comparison against all other publicly available EEG IC classifiers.Comment: Intended for NeuroImage. Updated from version one with minor editorial and figure change

Online Extraction and Single Trial Analysis of Regions Contributing to Erroneous Feedback Detection

International audienceUnderstanding how the brain processes errors is an essential and active field of neuroscience. Real time extraction and analysis of error signals provide an innovative method of assessing how individuals perceive ongoing interactions without recourse to overt behaviour. This area of research is critical in modern Brain–Computer Interface (BCI) design, but may also open fruitful perspectives in cognitive neuroscience research. In this context, we sought to determine whether we can extract discriminatory error-related activity in the source space, online, and on a trial by trial basis from electroencephalography data recorded during motor imagery. Using a data driven approach, based on interpretable inverse solution algorithms, we assessed the extent to which automatically extracted error-related activity was physiologically and functionally interpretable according to performance monitoring literature. The applicability of inverse solution based methods for automatically extracting error signals, in the presence of noise generated by motor imagery, was validated by simulation. Representative regions of interest, outlining the primary generators contributing to classification, were found to correspond closely to networks involved in error detection and performance monitoring. We observed discriminative activity in non-frontal areas, demonstrating that areas outside of the medial frontal cortex can contribute to the classification of error feedback activity

Application of cepstrum analysis and linear predictive coding for motor imaginary task classification

In this paper, classification of electroencephalography (EEG) signals of motor imaginary tasks is studied using cepstrum analysis and linear predictive coding (LPC). The Brain-Computer Interface (BCI) competition III dataset IVa containing motor imaginary tasks for right hand and foot of five subjects are used. The data was preprocessed by applying whitening and then filtering the signal followed by feature extraction. A random forest classifier is then trained using the cepstrum and LPC features to classify the motor imaginary tasks. The resulting classification accuracy is found to be over 90%. This research shows that concatenating appropriate different types of features such as cepstrum and LPC features hold some promise for the classification of motor imaginary tasks, which can be helpful in the BCI context

Reconciling the influence of task-set switching and motor inhibition processes on stop signal after-effects.

Executive response functions can be affected by preceding events, even if they are no longer associated with the current task at hand. For example, studies utilizing the stop signal task have reported slower response times to "GO" stimuli when the preceding trial involved the presentation of a "STOP" signal. However, the neural mechanisms that underlie this behavioral after-effect are unclear. To address this, behavioral and electroencephalography (EEG) measures were examined in 18 young adults (18-30 years) on "GO" trials following a previously "Successful Inhibition" trial (pSI), a previously "Failed Inhibition" trial (pFI), and a previous "GO" trial (pGO). Like previous research, slower response times were observed during both pSI and pFI trials (i.e., "GO" trials that were preceded by a successful and unsuccessful inhibition trial, respectively) compared to pGO trials (i.e., "GO" trials that were preceded by another "GO" trial). Interestingly, response time slowing was greater during pSI trials compared to pFI trials, suggesting executive control is influenced by both task set switching and persisting motor inhibition processes. Follow-up behavioral analyses indicated that these effects resulted from between-trial control adjustments rather than repetition priming effects. Analyses of inter-electrode coherence (IEC) and inter-trial coherence (ITC) indicated that both pSI and pFI trials showed greater phase synchrony during the inter-trial interval compared to pGO trials. Unlike the IEC findings, differential ITC was present within the beta and alpha frequency bands in line with the observed behavior (pSI > pFI > pGO), suggestive of more consistent phase synchrony involving motor inhibition processes during the ITI at a regional level. These findings suggest that between-trial control adjustments involved with task-set switching and motor inhibition processes influence subsequent performance, providing new insights into the dynamic nature of executive control

Análise do testemunho ocular utilizando sinais de eletroencefalograma

The application of Brain Computer Interfaces techniques to vital crime witnesses could and probably will be a key feature in the justice system. Features from the electroencephalogram signals were extracted with information detailing their domain (time or frequency), and their spacial scalp and time placement. For both domains, two different classification pipelines were applied in order to select the most relevant features: one to rank and select the top features and another to recursively eliminate the least relevant feature. The Support Vector Machine (linear and non-linear) is the classification model included in the pipeline. Further observations on the selected features by the applied techniques were performed and discussed in relation to the available knowledge about face recognition. The present work provides an experimental study on the electroencephalogram signals acquired from an experiment in which an array of subjects were asked to identify both culprit and distractor being the culprit related to a previously shown crime scene video.A aplicação de técnicas de Interfaces Cérebro-Computador a testemunhas vitais de um crime pode e provavelmente será uma funcionalidade chave no sistema de justiça. Características de sinais provenientes de eletroencefalograma foram extraídas com informações sobre o seu domínio (tempo ou frequência), e a sua localização espacial e temporal. Para ambos os domínios, dois modelos de classificação diferentes foram aplicados com vista a selecionar as características mais relevantes: um para classificar, ordenar e selecionar as características mais importantes e outro para eliminar recursivamente a característica menos relevante. O modelo utilizado para classificação foi o Support Vector Machine (linear e não linear). Outras observações sobre as características selecionadas pelas técnicas aplicadas foram realizadas e discutidas tendo em conta o conhecimento disponível sobre reconhecimento facial. O presente trabalho fornece um estudo experimental sobre os sinais de eletroencefalograma adquiridos numa experiência na qual foi pedido a um grupo de indivíduos para identificar tanto culpado como distrator, sendo que o culpado estava relacionado a um vídeo de cenário de crime mostrado anteriormente.Mestrado em Engenharia de Computadores e Telemátic