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A Hybrid Fuzzy Cognitive Map/Support Vector Machine Approach for EEG-Based Emotion Classification Using Compressed Sensing

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Abstract

Due to the high dimensional, non-stationary and non-linear properties of electroencephalogram (EEG), a significant portion of research on EEG analysis remains unknown. In this paper, a novel approach to EEG-based human emotion study is presented using Big Data methods with a hybrid classifier. An EEG dataset is firstly compressed using compressed sensing, then, wavelet transform features are extracted, and a hybrid Support Vector Machine (SVM) and Fuzzy Cognitive Map classifier is designed. The compressed data is only one-fourth of the original size, and the hybrid classifier has the average accuracy by 73.32%. Comparing to a single SVM classifier, the average accuracy is improved by 3.23%. These outcomes show that psychological signal can be compressed without the sparsity identity. The stable and high accuracy classification system demonstrates that EEG signal can detect human emotion, and the findings further prove the existence of the inter-relationship between various regions of the brain.

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Authors and Affiliations

  1. School of Biomedical Engineering, University of Technology, 15 Broadway, Ultimo, Sydney, New South Wales, Australia

    Kairui Guo, Henry Candra & Steven Su

  2. Faculty of Science, Engineering & Technology, Swinburne University of Technology, John St, Hawthorn, VIC, Australia

    Rifai Chai & Hung Nguyen

  3. DATA61, The Commonwealth Scientific and Industrial Research Organization Marsfield, Corner Vimiera & Pembroke Roads, Marsfield, Marsfield, Australia

    Ying Guo

  4. School of Biomedical Engineering, Sun Yat-Sen University, 135 Xingang W Rd, BinJiang Lu, Haizhu Qu, Guangzhou Shi, Guangdong Sheng, People’s Republic of China

    Rong Song

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  1. Kairui Guo
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  2. Rifai Chai
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  3. Henry Candra
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  4. Ying Guo
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  5. Rong Song
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  6. Hung Nguyen
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  7. Steven Su
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Correspondence to Kairui Guo.

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Guo, K., Chai, R., Candra, H. et al. A Hybrid Fuzzy Cognitive Map/Support Vector Machine Approach for EEG-Based Emotion Classification Using Compressed Sensing. Int. J. Fuzzy Syst. 21, 263–273 (2019). https://doi.org/10.1007/s40815-018-0567-3

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  • DOI: https://doi.org/10.1007/s40815-018-0567-3

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