Abstract
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.
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This work was partially supported by the Swiss National Science Foundation grant CR13I1-138032.
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Masulli, P., Masulli, F., Rovetta, S., Lintas, A., Villa, A.E.P. (2017). Unsupervised Analysis of Event-Related Potentials (ERPs) During an Emotional Go/NoGo Task. In: Petrosino, A., Loia, V., Pedrycz, W. (eds) Fuzzy Logic and Soft Computing Applications. WILF 2016. Lecture Notes in Computer Science(), vol 10147. Springer, Cham. https://doi.org/10.1007/978-3-319-52962-2_13
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DOI: https://doi.org/10.1007/978-3-319-52962-2_13
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