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Emotion Recognition Based on EEG Signals Using LIBSVM as the Classifier

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Urban Intelligence and Applications

Part of the book series: Studies in Distributed Intelligence ((SDI))

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Abstract

In order to improve the emotion recognition rate, this paper proposes an electroencephalograph (EEG) emotion recognition model using a library for support vector machine (LIBSVM) as the classifier. In this paper, we collected EEG signals from 10 volunteers along with the participants’ self-assessment of their affective state after each stimulus, in terms of Valence and arousal. After these signals are filtered, we calculate the features of Lempel-Ziv complexity, wavelet detail coefficient, and the co-integration relationship degree first. At the same time, EMD is carried out to calculate the average approximate entropy of the first four Intrinsic Mode Functions (IMFs). At last, all the features extracted will input into the LIBSVM for training and testing, and complete emotion recognition. In this paper, two classifications are carried out on the two dimensions of Valence and Arousal, respectively. The experimental results show that the average emotional recognition rate is 83.64% and 75.11%, respectively, which proves that the proposed scheme has a certain feasibility.

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Acknowledgements

This work supported by The Key Program of the National Natural Science Foundation of China (Grant No. 61432004); The National Natural Science Foundation of China (Grant No. 61474035, 61502140); The fund of Affective Computing and Advanced Intelligent Machine Anhui Province Key Laboratory (Grant No. ACAIM180101); NSFC-Shenzhen Joint Foundation (Key Project) (Grant No. U1613217).

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Authors and Affiliations

  1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui, China

    Tian Chen, Sihang Ju, Fuji Ren, Mingyan Fan & Xin An

  2. Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei, Anhui, China

    Tian Chen, Sihang Ju, Fuji Ren, Mingyan Fan & Xin An

  3. Faculty of Engineering, The University of Tokushima, Tokushima, Japan

    Fuji Ren

Authors
  1. Tian Chen
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  2. Sihang Ju
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  3. Fuji Ren
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  4. Mingyan Fan
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  5. Xin An
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Corresponding author

Correspondence to Tian Chen .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA

    Xiaohui Yuan

  2. Faculty of Computers and Information, Mansoura University, Mansoura, Egypt

    Mohamed Elhoseny

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Chen, T., Ju, S., Ren, F., Fan, M., An, X. (2020). Emotion Recognition Based on EEG Signals Using LIBSVM as the Classifier. In: Yuan, X., Elhoseny, M. (eds) Urban Intelligence and Applications. Studies in Distributed Intelligence . Springer, Cham. https://doi.org/10.1007/978-3-030-45099-1_10

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  • DOI: https://doi.org/10.1007/978-3-030-45099-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-45098-4

  • Online ISBN: 978-3-030-45099-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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