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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, corticomuscular 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 the recognition of seven gait phases under three different walking speeds. Secondly, the distribution of TFCMI values in eight topographies (eight muscles) was different at PSW and TSW phases. Thirdly, the differences of corticomuscular interaction between LR and MST and between TST and PSW of eight muscles were not significant. These insights enrich previous findings of the authors who have carried out gait phase recognition and provide a theoretical basis for gait recognition based on EEG and sEMG.

Background: Previous researchers have found that cortex is involved in the regulation of treadmill walking. However, cortico-muscular interaction analysis in a ‘fine’ gait phase (such as seven phases of the gait cycle) remains lacking in the time-frequency domain. Methods: In this investigation, we used beta band electroencephalogram (EEG) data to find that eight muscle-related cortices are inconsistent at the end of the swing and stance phases. The eight muscle-related cortices differ at each phase according to gamma band EEG data. Firstly, slope sign change (SSC) and mean power frequency (MPF) features of EEG and surface electromyography (sEMG) were used to recognize the seven gait phases, which are loading response (LR), mid-stance (MST), terminal stance (TST), pre-swing (PSW), initial swing (ISW), mid-swing (MSW) and terminal swing (TSW). Following this, the time-frequency cross mutual information (TFCMI) method, a novel time-frequency analysis method, was applied to examine the eight muscle-related cortices in seven gait phases using beta and gamma band EEG data. Results: We firstly found that the feature set comprising SSC of EEG as well as SSC and MPF of sEMG was available for seven gait phases recognition, and secondly that TFCMI values between each sEMG channel and EEG differed significantly in the seven gait phases. Conclusions: This suggests that analysis of the seven gait phases is beneficial. These insights enrich previous findings from authors carrying out cortico-muscular interaction analysis as well as providing critical information for rehabilitation physicians.