Contralesional recruitment and localization of EEG signal complexity in stroke: a recurrence quantification analysis of hierarchical motor tasks.

Abstract

Objective.Effective characterization of neural complexity during motor execution tasks enhances understanding of maladaptive cortical reorganization in stroke and inform targeted rehabilitation. While traditional EEG analyses often do not consider nonlinear temporal dynamics, we introduce a recurrence based computational framework to quantify cortical complexity during hierarchical motor tasks. Here, we evaluate contralesional motor system engagement in stroke survivors using recurrence quantification analysis (RQA), ensuring sensitivity to nonlinear and temporally structured cortical activity.Approach. RQA was applied to EEG signals recorded during shoulder abduction (SABD) at 20% and 40% torque levels to characterize nonlinear cortical dynamics and quantify complexity distinguishing adaptive from maladaptive motor system engagement. Spatially resolved recurrence metrics were compared between stroke and control participants to elucidate compensatory cortical reorganization linked to motor impairment and hierarchical task demands.Results. Our findings show a statistically significant increase in EEG signal complexity within the contralesional hemisphere of stroke participants, particularly under higher SABD loads. Consistent with previous studies, we observed abnormal muscle coactivation patterns between proximal and distal muscles, along with distinct shifts in EMG vector direction in stroke-impaired limbs. These shifts in coactivation patterns suggest constraints in muscle coactivation patterns resulting from losses in corticofugal projections and upregulated brainstem pathways.Significance. We introduce a novel application of RQA to quantify nonlinear EEG complexity during motor execution in chronic stroke. Our results show that increased EEG complexity reflects greater recruitment of contralesional motor pathways, indicating maladaptive cortical reorganization linked to impaired motor control. Unlike traditional spectral or connectivity-based EEG signal processing methods, RQA quantifies temporally evolving, nonlinear recurrence dynamics, serving as a marker of maladaptive contralesional motor recruitment, positioning RQA as a promising, clinically meaningful, and computationally efficient tool to evaluate cortical dynamics and guide targeted neurorehabilitation strategies aimed at minimizing maladaptive plasticity.