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Unsupervised Analysis of Event-Related Potentials (ERPs) During an Emotional Go/NoGo Task

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10147))

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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Acknowledgments

This work was partially supported by the Swiss National Science Foundation grant CR13I1-138032.

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Correspondence to Paolo Masulli .

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52961-5

  • Online ISBN: 978-3-319-52962-2

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