Abstract
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.
Highlights
Human locomotor disorder seriously affects the quality of life
The use of near-infrared spectroscopy (NIRS) (Miyai et al, 2001) and functional magnetic resonance imaging (Cunnington et al, 2005) has shown that the cortex is involved in steady-state walking
This suggests that the addition of slope sign change (SSC) features of EEG will enrich gait information contained in surface electromyography (sEMG) features and improve the accuracy of gait phase recognition
Summary
Human locomotor disorder seriously affects the quality of life. Surface electromyography (sEMG)based rehabilitative devices or robots have been developed for neurological injury rehabilitation of. Gait Phase Recognition and Correlations lower limb functions (Veneman et al, 2007; Banala et al, 2008). The classification results of gait phases from sEMG can be used to control the gait of lower limb exoskeletons for the rehabilitation of patients with lower limb disorders (Joshi et al, 2013). The human gait cycle is divided into stance and swing phases (Taborri et al, 2016). The stance phase is subdivided into loading response (LR), mid-stance (MST), terminal stance (TST), and pre-swing (PSW). The swing phase is divided into the initial swing (ISW), mid-swing (MSW), and terminal swing (TSW). More information about gait partitioning methods is available elsewhere in the literature (Taborri et al, 2016)
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