Single Trial Classification of EEG and Peripheral Physiological Signals for Recognition of Emotions Induced by Music Videos

S. Koelstra, A. Yazdani, M. Soleymani, C. Mühl, J.-L. Lee, Antinus Nijholt, T. Pun, T. Ebrahimi, I. Patras

  • 65 Citations

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 languageUndefined
Title of host publicationProceedings 2010 International Conference on Brain Informatics (BI 2010)
EditorsY. Yao, R. Sun, T. Poggio, J. Liu, N. Zhong, J. Huang
Place of PublicationBerlin
PublisherSpringer Verlag
Pages89-100
Number of pages12
ISBN (Print)978-3-642-15313-6
DOIs
StatePublished - 4 Sep 2010

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
Volume6334
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Electroencephalography

Keywords

  • METIS-271010
  • MUSIC
  • Affective Computing
  • IR-73064
  • EWI-18368
  • EEG
  • Emotion induction
  • HMI-MI: MULTIMODAL INTERACTIONS
  • Physiological Signals

Cite this

Koelstra, S., Yazdani, A., Soleymani, M., Mühl, C., Lee, J-L., Nijholt, A., ... Patras, I. (2010). Single Trial Classification of EEG and Peripheral Physiological Signals for Recognition of Emotions Induced by Music Videos. In Y. Yao, R. Sun, T. Poggio, J. Liu, N. Zhong, & J. Huang (Eds.), Proceedings 2010 International Conference on Brain Informatics (BI 2010) (pp. 89-100). (Lecture Notes in Computer Science; Vol. 6334). Berlin: Springer Verlag. DOI: 10.1007/978-3-642-15314-3_9

Koelstra, S.; Yazdani, A.; Soleymani, M.; Mühl, C.; Lee, J.-L.; Nijholt, Antinus; Pun, T.; Ebrahimi, T.; Patras, I. / Single Trial Classification of EEG and Peripheral Physiological Signals for Recognition of Emotions Induced by Music Videos.

Proceedings 2010 International Conference on Brain Informatics (BI 2010). ed. / Y. Yao; R. Sun; T. Poggio; J. Liu; N. Zhong; J. Huang. Berlin : Springer Verlag, 2010. p. 89-100 (Lecture Notes in Computer Science; Vol. 6334).

Research output: Scientific - peer-reviewConference contribution

@inbook{abc05f4f7ac849b2be884a5bd3b316da,
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.",
keywords = "METIS-271010, MUSIC, Affective Computing, IR-73064, EWI-18368, EEG, Emotion induction, HMI-MI: MULTIMODAL INTERACTIONS, Physiological Signals",
author = "S. Koelstra and A. Yazdani and M. Soleymani and C. Mühl and J.-L. Lee and Antinus Nijholt and T. Pun and T. Ebrahimi and I. Patras",
note = "10.1007/978-3-642-15314-3_9",
year = "2010",
month = "9",
doi = "10.1007/978-3-642-15314-3_9",
isbn = "978-3-642-15313-6",
series = "Lecture Notes in Computer Science",
publisher = "Springer Verlag",
pages = "89--100",
editor = "Y. Yao and R. Sun and T. Poggio and J. Liu and N. Zhong and J. Huang",
booktitle = "Proceedings 2010 International Conference on Brain Informatics (BI 2010)",

}

Koelstra, S, Yazdani, A, Soleymani, M, Mühl, C, Lee, J-L, 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 Y Yao, R Sun, T Poggio, J Liu, N Zhong & J Huang (eds), Proceedings 2010 International Conference on Brain Informatics (BI 2010). Lecture Notes in Computer Science, vol. 6334, Springer Verlag, Berlin, pp. 89-100. 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. / Koelstra, S.; Yazdani, A.; Soleymani, M.; Mühl, C.; Lee, J.-L.; Nijholt, Antinus; Pun, T.; Ebrahimi, T.; Patras, I.

Proceedings 2010 International Conference on Brain Informatics (BI 2010). ed. / Y. Yao; R. Sun; T. Poggio; J. Liu; N. Zhong; J. Huang. Berlin : Springer Verlag, 2010. p. 89-100 (Lecture Notes in Computer Science; Vol. 6334).

Research output: Scientific - peer-reviewConference contribution

TY - CHAP

T1 - Single Trial Classification of EEG and Peripheral Physiological Signals for Recognition of Emotions Induced by Music Videos

AU - Koelstra,S.

AU - Yazdani,A.

AU - Soleymani,M.

AU - Mühl,C.

AU - Lee,J.-L.

AU - Nijholt,Antinus

AU - Pun,T.

AU - Ebrahimi,T.

AU - Patras,I.

N1 - 10.1007/978-3-642-15314-3_9

PY - 2010/9/4

Y1 - 2010/9/4

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.

KW - METIS-271010

KW - MUSIC

KW - Affective Computing

KW - IR-73064

KW - EWI-18368

KW - EEG

KW - Emotion induction

KW - HMI-MI: MULTIMODAL INTERACTIONS

KW - Physiological Signals

U2 - 10.1007/978-3-642-15314-3_9

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

M3 - Conference contribution

SN - 978-3-642-15313-6

T3 - Lecture Notes in Computer Science

SP - 89

EP - 100

BT - Proceedings 2010 International Conference on Brain Informatics (BI 2010)

PB - Springer Verlag

ER -

Koelstra S, Yazdani A, Soleymani M, Mühl C, Lee J-L, Nijholt A et al. Single Trial Classification of EEG and Peripheral Physiological Signals for Recognition of Emotions Induced by Music Videos. In Yao Y, Sun R, Poggio T, Liu J, Zhong N, Huang J, editors, Proceedings 2010 International Conference on Brain Informatics (BI 2010). Berlin: Springer Verlag. 2010. p. 89-100. (Lecture Notes in Computer Science). Available from, DOI: 10.1007/978-3-642-15314-3_9