Electrospinography for non-invasively recording spinal sensorimotor networks in humans.

filtering, artifact subspace reconstruction and independent component analysis. Next, data were segmented by task and independent components (ICs) of EEG were clustered across participants. Within-participant analysis of ICs and ESG data was conducted, and ESG was characterized in the time and frequency domains. Generalized Partial Directed Coherence (gPDC) analysis was performed within ICs and between ICs and ESG data by participant and task. &#xD;Results. K-means clustering resulted in five clusters of ICs at Brodmann areas (BA) 9, BA 8, BA 39, BA 4, and BA 22. Areas associated with motor planning, working memory, visual processing, movement, and attention, respectively. Time-frequency analysis of ESG data found localized changes during movement execution when compared to no movement. Lastly, we found bi-directional changes in functional connectivity (p < 0.05, adjusted for multiple comparisons) within IC's and between IC's and ESG sensors during movement when compared to the no movement condition. &#xD;Significance. To our knowledge this is the first report using high density ESG for characterizing lower limb movements. Our findings provide support that ESG contains information about efferent and afferent signaling in neurologically intact adults and suggests that we can utilize ESG to directly study the spinal cord.