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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. 

Self-similarities at different time scales embedded within a self-organizing neural manifold are well recognized. In this study, we hypothesize that the Hurst fractal dimension (HFD) of the scalp electroencephalographic (EEG) signal reveals statistical differences between chronic pain and opioid use. We test this hypothesis by using EEG resting state signals acquired from a total of 23 human subjects: 14 with chronic pain, 9 with chronic pain taking opioid medications, 5 with chronic pain and not taking opioid medications, and 9 healthy controls. Using the multifractal analysis algorithm, the HFD for full spectrum EEG and EEG frequency band time series was computed for all groups. Our results indicate the HFD varies spatially and temporally across all groups and is of lower magnitude in patients not taking opioids as compared to those taking opioids and healthy controls. A global decrease in HFD was observed with changes in gamma and beta power in the chronic pain group compared to controls and when paired to subject handedness and sex. Our results show the loss of complexity representative of brain wide dysfunction and reduced neural processing can be used as an EEG biomarker for chronic pain and subsequent opioid use.