Article

Emotion recognition system using brain and peripheral signals: using correlation dimension to improve the results of EEG

Authors:

M. H. MoradiAuthors Info & Claims

IJCNN'09: Proceedings of the 2009 international joint conference on Neural Networks

Pages 1920 - 1924

Published: 14 June 2009 Publication History

Abstract

This paper proposed a multimodal fusion between brain and peripheral signals for emotion detection. The input signals were electroencephalogram, galvanic skin resistance, temperature, blood pressure and respiration, which can reflect the influence of emotion on the central nervous system and autonomic nervous system respectively. The acquisition protocol is based on a subset of pictures which correspond to three specific areas of valance-arousal emotional space (positively excited, negatively excited, and calm). The features extracted from input signals, and to improve the results, correlation dimension as a strong nonlinear feature is used for brain signals. The performance of the Quadratic Discriminant Classifier has been evaluated on different feature sets: peripheral signals, EEG's, and both. In comparison among the results of different feature sets, EEG signals seem to perform better than other physiological signals, and the results confirm the interest of using brain signals as peripherals in emotion assessment. According to the improvement in EEG results compare in each raw of the table, it seems that nonlinear features would lead to better understanding of how emotional activities work.

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Cited By

Khoshnevis SRa ISankar R(2020)Early Stage Diagnosis of Parkinson’s Disease Using HOS Features of EEG SignalsThe 9th International Conference on Smart Media and Applications10.1145/3426020.3426160(438-440)Online publication date: 17-Sep-2020
https://dl.acm.org/doi/10.1145/3426020.3426160
Al-Qazzaz NSabir MGrammer K(2019)Correlation Indices of Electroencephalogram-Based Relative Powers during Human Emotion ProcessingProceedings of the 2019 9th International Conference on Biomedical Engineering and Technology10.1145/3326172.3326179(64-70)Online publication date: 28-Mar-2019
https://dl.acm.org/doi/10.1145/3326172.3326179
Zekri FEllouze ABouazi R(2018)Adjustment of Medical Observations Influenced by Emotional StateInternational Journal of Synthetic Emotions10.4018/IJSE.20180101019:1(1-22)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.4018/IJSE.2018010101
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Index Terms

Emotion recognition system using brain and peripheral signals: using correlation dimension to improve the results of EEG
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection

Index terms have been assigned to the content through auto-classification.

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cover image Guide Proceedings

IJCNN'09: Proceedings of the 2009 international joint conference on Neural Networks

June 2009

3570 pages

ISBN:9781424435494

Sponsors

Georgia Tech: Georgia Institute of Technology
ieee-cis: IEEE Computational Intelligence Society
INNS: International Neural Network Society

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IEEE Press

Publication History

Published: 14 June 2009

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Cited By

Khoshnevis SRa ISankar R(2020)Early Stage Diagnosis of Parkinson’s Disease Using HOS Features of EEG SignalsThe 9th International Conference on Smart Media and Applications10.1145/3426020.3426160(438-440)Online publication date: 17-Sep-2020
https://dl.acm.org/doi/10.1145/3426020.3426160
Al-Qazzaz NSabir MGrammer K(2019)Correlation Indices of Electroencephalogram-Based Relative Powers during Human Emotion ProcessingProceedings of the 2019 9th International Conference on Biomedical Engineering and Technology10.1145/3326172.3326179(64-70)Online publication date: 28-Mar-2019
https://dl.acm.org/doi/10.1145/3326172.3326179
Zekri FEllouze ABouazi R(2018)Adjustment of Medical Observations Influenced by Emotional StateInternational Journal of Synthetic Emotions10.4018/IJSE.20180101019:1(1-22)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.4018/IJSE.2018010101
Wang MZhang SLv YLu H(2018)Anxiety Level Detection Using BCI of Miner's Smart HelmetMobile Networks and Applications10.1007/s11036-017-0935-523:2(336-343)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s11036-017-0935-5
D'mello SKory J(2015)A Review and Meta-Analysis of Multimodal Affect Detection SystemsACM Computing Surveys10.1145/268289947:3(1-36)Online publication date: 17-Feb-2015
https://dl.acm.org/doi/10.1145/2682899
Moon SLee JKalva HChen HAdzic VFernandez G(2014)Classifying Perceptual Experience of Tone-mapped High Dynamic Range Videos through EEGProceedings of the 1st International Workshop on Perception Inspired Video Processing10.1145/2662996.2663010(27-32)Online publication date: 7-Nov-2014
https://dl.acm.org/doi/10.1145/2662996.2663010
D'Mello SKory JMorency LBohus DAghajan HCassell JNijholt AEpps J(2012)Consistent but modestProceedings of the 14th ACM international conference on Multimodal interaction10.1145/2388676.2388686(31-38)Online publication date: 22-Oct-2012
https://dl.acm.org/doi/10.1145/2388676.2388686
Liu YSourina ONguyen M(2011)Real-time EEG-based emotion recognition and its applicationsTransactions on computational science XII10.5555/2028483.2028496(256-277)Online publication date: 1-Jan-2011
https://dl.acm.org/doi/10.5555/2028483.2028496
Zhang XHu BMoore PChen JZhou L(2011)EmotionoProceedings of the 18th international conference on Neural Information Processing - Volume Part II10.1007/978-3-642-24958-7_11(89-98)Online publication date: 13-Nov-2011
https://dl.acm.org/doi/10.1007/978-3-642-24958-7_11

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