Chinese Journal of Intelligent Science and Technology ›› 2021, Vol. 3 ›› Issue (1): 59-64.doi: 10.11959/j.issn.2096-6652.202106

• Special topic:emotional brain computer interface • Previous Articles     Next Articles

Cross-subject emotional EEG recognition based on multi-source domain adaptation

Hanbing GAO1, Chi ZHANG1, Mingyan JIN1, Yang XIAO1, Fengyu CONG1,2   

  1. 1 School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
    2 School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
  • Revised:2021-02-24 Online:2021-03-15 Published:2021-03-01
  • Supported by:
    The National Natural Science Foundation of China(61703069);The National Key Basic Research Program of China(JCKY2019110B009)

Abstract:

During the recognition of emotion based on electroencephalography (EEG) signals, traditional machine learning and deep learning methods cannot establish a general classification and detection model for EEG data due to the differences in EEG signals among subjects.Each subject was treated as an independent domain, a multi-source cross-subject emotional EEG recognition model was established, and the model was verified on the public dataset.The evaluation results show that compared with single-source domain model, the proposed model has better cross-subject feature extraction and classification capabilities.

Key words: multi-source domain, cross-subject, domain adaptation, neural network, affective brain-computer interface, EEG signal

CLC Number: 

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