ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (12): 2481-2489.doi: 10.7544/issn1000-1239.2020.20200723

Special Issue: 2020人机混合增强智能的典型应用专题

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Motor Imagery Classification Based on Multiscale Feature Extraction and Squeeze-Excitation Model

Jia Ziyu1,2, Lin Youfang1,2,3, Liu Tianhang1, Yang Kaixin1, Zhang Xinwang1, Wang Jing1,2,3   

  1. 1(School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044);2(Beijing Key Laboratory of Traffic Data Analysis and Mining (Beijing Jiaotong University), Beijing 100044);3(Key Laboratory of Intelligent Passenger Service of Civil Aviation (Civil Aviation Administration of China), Beijing 100044)
  • Online:2020-12-01
  • Supported by: 
    This work was supported by the Fundamental Research Funds for the Central Universities (2020YJS025) and the National Natural Science Foundation of China (61603029).

Abstract: Brain-computer Interface (BCI) technology based on motor imagery (MI) can establish communication between the human brain and outside world. It has been widely used in medical rehabilitation and other fields. Owing to the characteristics of the motor imagery EEG signals,such as non-linear, non-stationary, and low signal-noise ratio, it is a huge challenge to classify motor imagery EEG signals accurately. Hence, we propose a novel multiscale feature extraction and squeeze-excitation model which is applied for the classification of motor imagery EEG signals. Firstly, the proposed deep learning module, which is based on multiscale structure, automatically extracts time domain features, frequency domain features and time-frequency domain features. Then, the residual module and squeeze-excitation module are applied for feature fusion and selection, respectively. Finally, fully connected network layers are used to classify motor imagery EEG signals. The proposed model is evaluated on two public BCI competition datasets. The results show that the proposed model can effectively improve the recognition performance of motor imagery EEG signals compared with the existing several state-of-the-art models. The average accuracy on the two datasets is 78.0% and 82.5%, respectively. Moreover, the proposed model has higher application value because it classifies motor imagery EEG signals efficiently without manual feature extraction when spatial information is insufficient.

Key words: motor imagery, squeeze-excitation model, EEG signal, Brain-computer Interface, multiscale convolution

CLC Number: