Computational analysis of EEG activity during stance and swing gait phases

Abstract Analysis of cortical electroencephalography (EEG) patterns may suggest novel neural dynamics related to healthy and diseased gait which could be critical for assessing neurological disorders. However, the dynamics of cortical involvement in walking is not clearly understood. In this study, using non-invasive EEG, we recorded and analyzed the stance and swing gait cycle phases in healthy volunteering subjects. Extracted spectral and temporal features of gait data for right toe-off and heel strike were ranked based, using machine learning algorithms to identify patterns related to swing and stance. Increased beta rhythms, positive and negative motor potentials for stance and swing could be targeted as biosignatures discriminating gait cycle phases. Identifying such biosignatures help classify stance and swing phases and may be pertinent to preclinical studies and in resource-limited environments where expensive equipment may not be accessible.