Paper Info
Title
Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos
Abstract
Recently, the field of automatic recognition of users. affective states has gained a great deal of attention. Automatic, implicit recognition of affective states has many applications, ranging from personalized content recommendation to automatic tutoring systems. In this work, we present some promising results of our research in classification of emotions induced by watching music videos. We show robust correlations between users' self-assessments of arousal and valence and the frequency powers of their EEG activity. We present methods for single trial classification using both EEG and peripheral physiological signals. For EEG, an average (maximum) classification rate of 55.7% (67.0%) for arousal and 58.8% (76.0%) for valence was obtained. For peripheral physiological signals, the results were 58.9% (85.5%) for arousal and 54.2% (78.5%) for valence.
Year
Venue
Keywords
2010
Brain Informatics
classification rate,implicit recognition,automatic tutoring system,present method,frequency power,automatic recognition,single trial classification,affective state,music video,peripheral physiological signal,eeg activity
Field
DocType
Volume
Arousal,Peripheral,Pattern recognition,Heart rate variability,Psychology,Emotion classification,Speech recognition,Artificial intelligence,Affective computing,Affect (psychology),Classification rate,Electroencephalography
Conference
6334
ISSN
ISBN
Citations 
0302-9743
3-642-15313-5
46
PageRank 
References 
Authors
3.20
8
9
Name
Order
Citations
PageRank
Sander Koelstra154020.80
Ashkan Yazdani240419.79
Mohammad Soleymani3133879.43
Christian Mühl41108.44
Jong-Seok Lee582761.06
Anton Nijholt62356240.31
Thierry Pun73553290.95
Touradj Ebrahimi84327322.13
Ioannis Patras91960123.15