Dataset Information

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.

SUBMITTER: Wei P

PROVIDER: S-EPMC8175803 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:The Timed Up and Go (TUG) test is widely used to assess locomotion in patients with stroke and is considered to predict the risk of falls. The analysis of locomotor trajectories during the TUG appears pertinent in stroke patients. The aims of this study were i) to analyze locomotor trajectories in patients with stroke during the walking and turning sub-tasks of the TUG, and to compare them with healthy subjects, ii) to determine whether trajectory parameters provide additional information to that provided by the conventional measure (performance time), iii) to compare the trajectory parameters of fallers and non-fallers with stroke and of patients with right and left hemisphere stroke, and iv) to evaluate correlations between trajectory parameters and Berg Balance Scale scores. METHODS:29 patients with stroke (mean age 54.2±12.2 years, 18 men, 8 fallers) and 25 healthy subjects (mean age 51.6±8.7 years, 11 men) underwent three-dimensional analysis of the TUG. The trajectory of the center of mass was analyzed by calculation of the global trajectory length, Hausdorff distance and Dynamic Time Warping. The parameters were compared with a reference trajectory during the total task and each sub-task (Go, Turn, Return) of the TUG. RESULTS:Values of trajectory parameters were significantly higher for the stroke group during the total TUG and the Go and Turn sub-tasks (p<0.05). Moreover, logistic regression indicated that these parameters better discriminated stroke patients and healthy subjects than the conventional timed performance during the Go sub-task. In addition, fallers were distinguished by higher Dynamic Time Warping during the Go (p<0.05). There were no differences between patients with right and left hemisphere stroke. DISCUSSION AND CONCLUSION:The trajectories of the stroke patients were longer and more deviated during the turn and the preceding phase. Trajectory parameters provided additional information to timed performance of this locomotor task. Focusing rehabilitation programs on lead-up to turn and turning could be relevant for stroke patients since the Turn was related to the balance and the phase preceding the turn seemed to distinguish fallers.

Dataset Information

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

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