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Abstract Objective: This study quantifies EEG complexity in chronic hemiparetic stroke patients performing hierarchical motor tasks, examining the degree of contralesional motor resource recruitment in maladaptive neural responses. Approach: We applied recurrence quantification analysis (RQA) and nonlinear dynamical measures to examine spatial patterns of motor-related EEG complexity under varying shoulder abduction torque levels (20% and 40%) in both stroke survivors and healthy control participants, enabling comparative analyses of adaptive neural responses. Results: Our findings show a statistically significant increase in EEG signal complexity within the contralesional hemisphere of stroke participants, particularly under higher shoulder abduction 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. Unlike traditional spectral or connectivity-based EEG methods, RQA quantifies temporally evolving, nonlinear recurrence patterns that reflect maladaptive contralesional motor recruitment. Our findings demonstrate that increased EEG complexity correlates with impaired motor control and reliance on compensatory pathways, offering new insight into neural reorganization after stroke. These results position 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.