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Authors: Giuseppe Placidi 1 ; Paolo Di Giamberardino 2 ; Andrea Petracca 1 ; Matteo Spezialetti 1 and Daniela Iacoviello 2

Affiliations: 1 University of L'Aquila, Italy ; 2 Sapienza University of Rome, Italy

ISBN: 978-989-758-204-2

Keyword(s): BCI, Emotional Signals, DEAP Dataset, Machine Learning, PCA, SVM.

Related Ontology Subjects/Areas/Topics: Applications ; Assistive Technologies ; Biomedical Engineering ; Biomedical Instruments and Devices ; Brain-Computer Interfaces ; Devices ; EMG Signal Processing and Applications ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Neural Rehabilitation ; Neural Signal Processing ; NeuroSensing and Diagnosis ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Software Engineering

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 di scussed. 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. (More)

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Paper citation in several formats:
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

@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},
}

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

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