Surface Electromyography and Electroencephalogram-Based Gait Phase Recognition and Correlations Between Cortical and Locomotor Muscle in the Seven Gait Phases - Wei, Pengna; Zhang, Jinhua; Wang, Baozeng; Hong, Jun (Frontiers in Neuroscience, 2021)
The classification of gait phases based on surface electromyography (sEMG) and electroencephalogram (EEG) can be used to the control systems of lower limb exoskeletons for the rehabilitation of patients with lower limb disorders. In this study, the slope sign change (SSC) and mean power frequency (MPF) features of EEG and sEMG were used to recognize the seven gait phases (loading response(LR), mid-stance(MST), terminal stance(TST), pre-swing(PSW), initial swing(ISW), mid-swing(MSW) and terminal swing(TSW)). Previous researchers have found that the cortex is involved in the regulation of treadmill walking. However, cortico-muscular interaction analysis in a high level of gait phase granularity remains lacking in the time-frequency domain and the feasibility of gait phase recognition based on EEG combined with sEMG is unknown. Therefore, the time-frequency cross mutual information (TFCMI) method was applied to research the theoretical basis of gait control in seven gait phases using beta-band EEG and sEMG data. We firstly found that the feature set comprising SSC of EEG as well as SSC and MPF of sEMG was robust for seven gait phases recognition under three different walking speeds. Secondly, the distribution of TFCMI values in eight topography (eight muscles) was different at PSW and TSW phases. Thirdly, the differences of cortico-muscular interaction between LR and MST, TST and PSW of eight muscles were not significant. These insights enrich previous findings from authors who have carried out gait phase recognition and provide a theoretical basis for gait recognition based on EEG and sEMG.