Unsupervised Analysis of Event-Related Potentials (ERPs) During an Emotional Go/NoGo Task
We propose a framework for an unsupervised analysis of electroencephalography (EEG) data based on possibilistic clustering, including a preliminary noise and artefact rejection. The proposed data flow identifies the existing similarities in a set of segments of EEG signals and their grouping according to relevant experimental conditions. The analysis is applied to a set of event-related potentials (ERPs) recorded during the performance of an emotional Go/NoGo task. We show that the clusterization rate of trials in two experimental conditions is able to characterize the participants. The extension of the method and its generalization is discussed.
KeywordsEEG ERP Possibilistic clustering Decoding Brain activity
This work was partially supported by the Swiss National Science Foundation grant CR13I1-138032.
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