Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos

Sander Koelstra, Ashkan Yazdani, Mohammad Soleymani, Christian Mühl, Jong Seok Lee, Anton Nijholt, Thierry Pun, Touradj Ebrahimi, Ioannis Patras

Research output: Chapter in Book/Report/Conference proceedingConference contribution

147 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationBrain Informatics - International Conference, BI 2010, Proceedings
Number of pages12
Publication statusPublished - 2010
Event2010 International Conference on Brain Informatics, BI 2010 - Toronto, ON, Canada
Duration: 2010 Aug 282010 Aug 30

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6334 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other2010 International Conference on Brain Informatics, BI 2010
CityToronto, ON

Bibliographical note

Funding Information:
The research leading to these results has been performed in the frameworks of European Community’s Seventh Framework Program (FP7/2007-2011) under grant agreement no. 216444 (PetaMedia). Furthermore, the authors gratefully acknowledge the support of the BrainGain Smart Mix Programme of the Netherlands Ministry of Economic Affairs, the Netherlands Ministry of Education, Culture and Science and the Swiss National Foundation for Scientific Research in the framework of NCCR Interactive Multimodal Information Management (IM2).

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos'. Together they form a unique fingerprint.

Cite this