Gait pattern recognition based on electroencephalogram signals with common spatial pattern and graph attention networks

Assisting human locomotion in various gait patterns is one of the challenges in the interaction between human and lower limb exoskeleton. In this paper, we propose the graph attention network with electroencephalogram (EEG) signal graph structure data constructed by common spatial pattern (CSP) model to extract the spatial- temporal information from multi-channel EEG signals to recognize gait patterns including walk, run, stair descent, stair ascent, stand-to-sit, sit-to-stand, and jump. The CSP spatial filters are analyzed for EEG signals with multiple gait patterns. The EEG signal graph structure data is constructed based on the spatial-temporal features and spatial domain features of the CSP filtered data. The graph structure based on the correlation between channels and the graph structure based on the spatial relative position of EEG electrodes are constructed for comparison. The gait pattern recognition performance of the proposed model is significantly higher than that of comparison models, and the average recognition accuracy is 86.747%. The accuracy of gait pattern recognition along gait cycle is analyzed. Based on the analysis of the EEG signal graph structure reconstructed by multi-head attention coefficients of the proposed model, the learning characteristics of the model can be obtained. The relationship between EEG channels is analyzed based on the graph structures the models focusing on. Finally, the proposed method is validated on the open access brain-computer interface (BCI) competition IV Datasets 2a of EEG signals.