Surface Electromyography and Electroencephalogram-Based Gait Phase Recognition and Correlations Between Cortical and Locomotor Muscle in the Seven Gait Phases

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.

Electrophysiological approaches to understanding brain-muscle interactions during gait: a systematic review

“…Additionally, three studies independently found distinct patterns related to walking speed. They discovered that walking slowly resulted in higher NMC compared to walking at a faster pace (Chen et al, 2021; Petersen et al, 2012; Wei et al, 2021). Meanwhile, Chen et al reported that walking forward exhibited higher NMC when contrasted with walking backward (Chen et al, 2021).…”

“…Likewise, slow walking showed higher NMC compared to fast walking (Chen et al, 2021;Wei et al, 2021). During slow walking, there is a conscious adjustment of step length, balance, and control of limb movements needed to ensure stability.…”

EEG signal motion recognition based on GSA-SVM

“…Gait recognition is a technique that evaluates an individual's health status and rehabilitation level by analyzing their walking patterns [1][2][3] . In the medical and rehabilitation fields, gait recognition is of great significance for evaluating the rehabilitation process of patients with neurological diseases, musculoskeletal diseases, and neurological injuries [4][5][6] . In addition, gait recognition can also be applied in fields such as robot navigation, animation production, and movie special effects [7][8][9] .…”