Spatial characterization of functional neural activity during lower limb motion through functional connectivity

Analyzing electroencephalographic signals (EEG) could provide valuable information about functional neural activity (FNA) during human motion. The hypothesis of this work is twofold: spatial patterns emerge in EEG signals from functional connectivity (FC) analysis during lower limb movements, and the spatial patterns are mosto robust in some frequency bands than in others. Accordingly, a set of human subjects without neuromotor pathologies participated in an experimental trial where EEG signals were recorded during lower limb movements. The FC was studied with coherence analysis (in $\delta$, $\theta$, and $\alpha$) and graph theory was proposed to study the characteristics of spatial dynamics by means a set of metrics (degree, maximum connection, and closeness centrality) and two distances (Hamming distance and Jaccard). Finally, a statistical study of the metrics by frequency band was performed to analyze the significant differences between the phases of each stage and movement, considering the proposed metrics. The results of the study indicated that the frequency bands that showed greater statistical significance in the analysis were $\delta$, $\theta$, and $\alpha$ and that the major differences in graph dynamics were shown in degree, maximum connection, and closeness centrality in $\alpha$ band. Present findings portray leading underlying neural networks, implying that discernible spatial patterns exist in FNA during lower limb movements, and such patterns can be characterized with the proposed methodology.