Motor Imagery Signal Classification Based on Multi-scale Self-attentional Mechanism

Abstract

Brain-computer interface (BCI) technology based on motor imagery electroencephalograph (EEG) signals has developed rapidly, and compared with traditional methods, deep learning has achieved competitive results. However, it is still a challenge to design and train an end-to-end network to fully extract the potential features of motor-imaging EEG signals. Based on the temporal and spatial characteristics of EEG, a multi-scale spatiotemporal self-attention network model based on attention mechanism is proposed to classify motor imagery EEG signals into four categories, such as left hand, right hand, foot and tongue/rest. Since the amplitude and response time of motor imagery EEG signal vary with the subjects, it is impossible to determine the most relevant brain region with motor imagery. Therefore, the self-attention mechanism is used to select the best channel by automatically weighting the higher weight to the motion-related channel and the lower weight to the non-motion-related channel in space. And parallel multi-scale TCN layer is used to extract the temporal feature at different scales and to eliminate the noise in time domain. The fusion module fuses the extracted spatial and temporal features, and finally inputs them to the classification module for classification. The model achieves accuracy of 79.26%, 85.90% and 96.96% on the BCI competition datasets IV-2a, IV-2b and HGD dataset. Compared with the baseline method, this method has a higher accuracy in subject classification. The results show that the method has good performance, robustness and transfer learning ability.

Keywords

• motor imagery; attention; brain-computer interface; deep learning; multi-scale