Setting the Parameters for an Accurate EEG (Electroencephalography)-Based Emotion Recognition System


Por: Sorinas, J, Murcia, M, Minguillon, J, Sanchez-Ferrer, F, Val-Calvo, M, Ferrandez, J and Fernandez, E

Publicada: 1 ene 2017
Resumen:
The development of a suitable EEG-based emotion recognition system has become a target in the last decades for BCI (Brain Computer Interface) applications. However, there are scarce algorithms and procedures for real time classification of emotions. In this work we introduce a new approach to select the appropriate parameters in order to build up a real-time emotion recognition system. We recorded the EEG-neural activity of 5 participants while they were looking and listening to an audiovisual database composed by positive and negative emotional video clips. We tested 11 different temporal window sizes, 6 ranges of frequency bands and 5 areas of interest located mainly on prefrontal and frontal brain regions. The most accurate time window segment was selected for each participant, giving us probable positive and negative emotional characteristic patterns, in terms of the most informative frequency-location pairs. Our preliminary results provide a reliable way to establish the more appropriate parameters to develop an accurate EEG-based emotion classifier in real-time.

Filiaciones:
Sorinas, J:
 Univ Miguel Hernandez, Inst Bioengn, Ave Univ, Elche 03202, Spain

 GIBER BBN, Ave Univ, Elche 03202, Spain

Murcia, M:
 Univ Miguel Hernandez, Inst Bioengn, Ave Univ, Elche 03202, Spain

 GIBER BBN, Ave Univ, Elche 03202, Spain

 Univ Cartagena, Dept Elect & Comp Technol, Cartagena, Spain

Minguillon, J:
 Univ Granada, Dept Comp Architecture & Technol, Granada, Spain

:
 Univ Miguel Hernandez, San Juan Univ Clin Hosp, Dept Pediat, Alicante, Spain

Val-Calvo, M:
 Univ Cartagena, Dept Elect & Comp Technol, Cartagena, Spain

Ferrandez, J:
 Univ Cartagena, Dept Elect & Comp Technol, Cartagena, Spain

Fernandez, E:
 Univ Miguel Hernandez, Inst Bioengn, Ave Univ, Elche 03202, Spain

 GIBER BBN, Ave Univ, Elche 03202, Spain
ISSN: 03029743





Lecture Notes in Computer Science
Editorial
SPRINGER INTERNATIONAL PUBLISHING AG, GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND, Alemania
Tipo de documento: Proceedings Paper
Volumen: 10337 Número:
Páginas: 265-273
WOS Id: 000432197700026

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