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A Novel DE-CNN-BiLSTM Multi-Fusion Model for EEG Emotion Recognition

Author

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  • Fachang Cui

    (College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing 210003, China)

  • Ruqing Wang

    (College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing 210003, China)

  • Weiwei Ding

    (College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing 210003, China)

  • Yao Chen

    (College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing 210003, China)

  • Liya Huang

    (College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing 210003, China)

Abstract

As a long-standing research topic in the field of brain–computer interface, emotion recognition still suffers from low recognition accuracy. In this research, we present a novel model named DE-CNN-BiLSTM deeply integrating the complexity of EEG signals, the spatial structure of brain and temporal contexts of emotion formation. Firstly, we extract the complexity properties of the EEG signal by calculating Differential Entropy in different time slices of different frequency bands to obtain 4D feature tensors according to brain location. Subsequently, the 4D tensors are input into the Convolutional Neural Network to learn brain structure and output time sequences; after that Bidirectional Long-Short Term Memory is used to learn past and future information of the time sequences. Compared with the existing emotion recognition models, the new model can decode the EEG signal deeply and extract key emotional features to improve accuracy. The simulation results show the algorithm achieves an average accuracy of 94% for DEAP dataset and 94.82% for SEED dataset, confirming its high accuracy and strong robustness.

Suggested Citation

  • Fachang Cui & Ruqing Wang & Weiwei Ding & Yao Chen & Liya Huang, 2022. "A Novel DE-CNN-BiLSTM Multi-Fusion Model for EEG Emotion Recognition," Mathematics, MDPI, vol. 10(4), pages 1-11, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:582-:d:748300
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    References listed on IDEAS

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    1. Huiping Jiang & Demeng Wu & Rui Jiao & Zongnan Wang & Ning Cai, 2021. "Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks," Complexity, Hindawi, vol. 2021, pages 1-9, February.
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