Classification of Emotional Signals from the DEAP dataset

Giuseppe Placidi, Paolo Di Giamberardino, Andrea Petracca, Matteo Spezialetti, Daniela Iacoviello

Abstract

A Brain Computer Interface (BCI) is a useful instrument to support human communication, frequently implemented by using electroencephalography (EEG). Regarding the used communication paradigm, a very large number of strategies exist and, recently, self-induced emotions have been introduced. However, in general the actual emotion- based BCIs are just binary, since they are capable of recognizing just a single emotion. A crucial node is the introduction of more than a single emotional state for improving the efficiency of a BCI. In order to be used in BCIs, signals from different emotional states have to be collected, recognized and classified. In the present paper, a method for mapping several emotional states was described and tested on EEG signals collected from a publicly available dataset for emotion analysis using physiological signals (DEAP). The proposed method, its experimental protocol, and preliminary numerical results on three different emotional states were presented and discussed. The method, based on multiple binary classification, was capable of optimizing the most discriminative channels and the features combination for each emotional state and of recognizing between several emotional states through a polling system.

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Paper Citation


in Harvard Style

Placidi G., Di Giamberardino P., Petracca A., Spezialetti M. and Iacoviello D. (2016). Classification of Emotional Signals from the DEAP dataset . In Proceedings of the 4th International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX, ISBN 978-989-758-204-2, pages 15-21. DOI: 10.5220/0006043400150021


in Bibtex Style

@conference{neurotechnix16,
author={Giuseppe Placidi and Paolo Di Giamberardino and Andrea Petracca and Matteo Spezialetti and Daniela Iacoviello},
title={Classification of Emotional Signals from the DEAP dataset},
booktitle={Proceedings of the 4th International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,},
year={2016},
pages={15-21},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006043400150021},
isbn={978-989-758-204-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,
TI - Classification of Emotional Signals from the DEAP dataset
SN - 978-989-758-204-2
AU - Placidi G.
AU - Di Giamberardino P.
AU - Petracca A.
AU - Spezialetti M.
AU - Iacoviello D.
PY - 2016
SP - 15
EP - 21
DO - 10.5220/0006043400150021