Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (4): 35-39.doi: 10.6040/j.issn.1672-3961.0.2019.679

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Emotional EEG recognition based on Bi-LSTM

LIU Shuai1,2, WANG Lei1,2*, DING Xutao1,2   

  1. 1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China;
    2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China
  • Published:2020-08-13

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Abstract: To solved a challenging task of emotional electroencephalogram(EEG)recognition, this study proposed a bidirectional long short-term memory(Bi-LSTM)EEG classification model and explored the emotional mechanism of the brain, with the highest arousal accuracy of 76.78% and the highest valence accuracy of 77.28%. The Bi-LSTM model, compared with other models, had excellent performances in the recognition of emotional EEG. The Bi-LSTM model was used to explore the brain emotion mechanism by comparing the accuracy of different frequency bands, brain regions and feature density, and the results showed that the frequency bands, brain regions and feature density with the highest emotional correlation in the brain were respectively the α and β regions, Parietal Lobe and Frontal Lobe, 50 and 15.

Key words: EEG emotional recognition, deep learning, emotion, EEG, Bi-LSTM

CLC Number: 

  • TP183
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