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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.
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Classifying Unimanual and Bimanual Upper Extremity Tasks in Individuals Post-Stroke
After stroke, many individuals develop impairments that lead to compensatory motions. Compensation allows individuals to achieve tasks but has long-term detrimental effects and represents maladaptive motor strategies. Increased use of bimanual motions may serve as a biomarker for recovery (and the reduction of reliance on compensatory motion), and tracking such motion using sensor data may provide critical data for health care specialists. However, past work by the authors demonstrated individual variation in motor strategies results in noisy and chaotic sensor data. The goal of the current work is to develop classifiers capable of differentiating unimanual, bimanaual asymmetric, and bimanual symmetric gestures using wearable sensor data. Twenty participants post-stroke (and 20 age-matched controls) performed a set of tasks under the supervision of a trained occupational therapist. Sensor data were recorded for each task. Classifiers were developed using artificial neural networks (ANNs) as a baseline, and the echo state neural network (ESNN) which has demonstrated efficacy with chaotic data. We find that, for control and post-stroke participants, the ESNN results in improved testing accuracy performance (91.3% and 80.3%, respectively). These results suggest a novel method for classifying gestures in individuals post-stroke, and the developed classifiers may facilitate longitudinal monitoring and correction of compensatory motion.
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- Award ID(s):
- 2054191
- PAR ID:
- 10334544
- Date Published:
- Journal Name:
- 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
- Page Range / eLocation ID:
- 6301 to 6305
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/EMBC46164.2021.9629885
