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

doi:10.1007/978-3-642-15314-3_9

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

School of Integrated Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

147 Citations (Scopus)

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.

Original language	English
Title of host publication	Brain Informatics - International Conference, BI 2010, Proceedings
Pages	89-100
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-15314-3_9
Publication status	Published - 2010
Event	2010 International Conference on Brain Informatics, BI 2010 - Toronto, ON, Canada Duration: 2010 Aug 28 → 2010 Aug 30

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6334 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	2010 International Conference on Brain Informatics, BI 2010
Country/Territory	Canada
City	Toronto, ON
Period	10/8/28 → 10/8/30

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)

Access to Document

10.1007/978-3-642-15314-3_9

Cite this

Koelstra, S., Yazdani, A., Soleymani, M., Mühl, C., Lee, J. S., Nijholt, A., Pun, T., Ebrahimi, T., & Patras, I. (2010). Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos. In Brain Informatics - International Conference, BI 2010, Proceedings (pp. 89-100). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6334 LNAI). https://doi.org/10.1007/978-3-642-15314-3_9

Koelstra, Sander ; Yazdani, Ashkan ; Soleymani, Mohammad et al. / Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos. Brain Informatics - International Conference, BI 2010, Proceedings. 2010. pp. 89-100 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

author = "Sander Koelstra and Ashkan Yazdani and Mohammad Soleymani and Christian M{\"u}hl and Lee, {Jong Seok} and Anton Nijholt and Thierry Pun and Touradj Ebrahimi and Ioannis Patras",

note = "Funding Information: The research leading to these results has been performed in the frameworks of European Community{\textquoteright}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).; 2010 International Conference on Brain Informatics, BI 2010 ; Conference date: 28-08-2010 Through 30-08-2010",

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Koelstra, S, Yazdani, A, Soleymani, M, Mühl, C, Lee, JS, Nijholt, A, Pun, T, Ebrahimi, T & Patras, I 2010, Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos. in Brain Informatics - International Conference, BI 2010, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6334 LNAI, pp. 89-100, 2010 International Conference on Brain Informatics, BI 2010, Toronto, ON, Canada, 10/8/28. https://doi.org/10.1007/978-3-642-15314-3_9

Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos. / Koelstra, Sander; Yazdani, Ashkan; Soleymani, Mohammad et al.
Brain Informatics - International Conference, BI 2010, Proceedings. 2010. p. 89-100 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6334 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos

AU - Koelstra, Sander

AU - Yazdani, Ashkan

AU - Soleymani, Mohammad

AU - Mühl, Christian

AU - Lee, Jong Seok

AU - Nijholt, Anton

AU - Pun, Thierry

AU - Ebrahimi, Touradj

AU - Patras, Ioannis

N1 - 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).

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

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DO - 10.1007/978-3-642-15314-3_9

M3 - Conference contribution

AN - SCOPUS:78249264726

SN - 3642153135

SN - 9783642153136

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 89

EP - 100

BT - Brain Informatics - International Conference, BI 2010, Proceedings

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Koelstra S, Yazdani A, Soleymani M, Mühl C, Lee JS, Nijholt A et al. Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos. In Brain Informatics - International Conference, BI 2010, Proceedings. 2010. p. 89-100. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-15314-3_9

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

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