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EEG-based Valence Recognition: What do we Know About the influence of Individual Specificity?

Authors:
Timo Schuster
Sascha Gruss
Stefanie Rukavina
Steffen Walter
Harald C. Traue

Keywords: Human-Computer Interaction; Emotion recognition; Affective Computing; EEG; classification

Abstract:
The fact that training classification algorithms in a within-subject design is inferior to training on between subject data is discussed for an electrophysiological data set. Eventrelated potentials were recorded from 18 subjects, emotionally stimulated by a series of 18 negative, 18 positive and 18 neutral pictures of the International Affective Picture System. In addition to traditional averaging and group comparison of event related potentials, electroencephalographical data have been intra- and inter-individually classified using a Support Vector Machine for emotional conditions. Support vector machine classifications based upon intraindividual data showed significantly higher classification rates [F(19.498),p<.001] than global ones. An effect size was calculated (d = 1.47) and the origin of this effect is discussed within the context of individual response specificities. This study clearly shows that classification accuracy can be boosted by using individual specific settings.

Pages: 71 to 76

Copyright: Copyright (c) IARIA, 2012

Publication date: July 22, 2012

Published in: conference

ISSN: 2308-4197

ISBN: 978-1-61208-218-9

Location: Nice, France

Dates: from July 22, 2012 to July 27, 2012