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Reversed Correlation-Based Pairwised EEG Channel Selection in Emotional State Recognition

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12744))

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Abstract

Emotions play an important role in everyday life and contribute to physical and emotional well-being. They can be identified by verbal or non-verbal signs. Emotional states can be also detected by electroencephalography (EEG signals). However, efficient information retrieval from the EEG sensors is a difficult and complex task due to noise from the internal and external artifacts and overlapping signals from different electrodes. Therefore, the appropriate electrode selection and discovering the brain parts and electrode locations that are most or least correlated with different emotional states is of great importance. We propose using reversed correlation-based algorithm for intra-user electrode selection, and the inter-subject subset analysis to establish electrodes least correlated with emotions for all users. Moreover, we identified subsets of electrodes most correlated with emotional states. The proposed method has been verified by experiments done on the DEAP dataset. The obtained results have been evaluated regarding the recognition of two emotions: valence and arousal. The experiments showed that the appropriate reduction of electrodes has no negative influence on emotion recognition. The differences between errors for recognition based on all electrodes and the selected subsets were not statistically significant. Therefore, where appropriate, reducing the number of electrodes may be beneficial in terms of collecting less data, simplifying the EEG analysis, and improving interaction problems without recognition loss.

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Authors and Affiliations

  1. Institute of Information Technology, Lodz University of Technology, Wólczańska 215, 90-924, Łódź, Poland

    Aleksandra Dura, Agnieszka Wosiak, Bartłomiej Stasiak, Adam Wojciechowski & Jan Rogowski

Authors
  1. Aleksandra Dura
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  2. Agnieszka Wosiak
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  3. Bartłomiej Stasiak
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  4. Adam Wojciechowski
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  5. Jan Rogowski
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Corresponding author

Correspondence to Agnieszka Wosiak .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Dura, A., Wosiak, A., Stasiak, B., Wojciechowski, A., Rogowski, J. (2021). Reversed Correlation-Based Pairwised EEG Channel Selection in Emotional State Recognition. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12744. Springer, Cham. https://doi.org/10.1007/978-3-030-77967-2_44

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  • DOI: https://doi.org/10.1007/978-3-030-77967-2_44

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  • Online ISBN: 978-3-030-77967-2

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