Healthy-to-Stroke Translation of EEG-Based BMIs: EEG Characterization and Reinforcement Learning-Based Decoder Evaluation

PurposeEEG-based brain-machine interfaces (BMIs) may support assistive technologies for individuals with stroke-related motor impairment by translating neural activity into control commands for external devices. However, post-stroke neural reorganization and interindividual EEG variability challenge reliable decoding. This study characterized motor imagery EEG features in healthy and acute stroke participants and evaluated whether population-trained Q-learning Kernel Temporal Difference (Q-KTD) decoders could improve individual stroke decoding through transfer learning. These analyses assess the feasibility of healthy-to-stroke translation for EEG-based BMI neural decoding. Materials and MethodsPublicly available motor imagery EEG datasets from healthy participants (n = 109) and individuals with acute stroke (n = 50) were analyzed using left- and right-hand motor imagery trials. The datasets were selected because of their relatively large sample sizes and comparable motor imagery tasks. EEG characterization included baseline and motor imagery-period band power, ERD/ERS, hemispheric asymmetry, and time-frequency representations. For Q-learning Kernel Temporal Difference (Q-KTD) decoding, filtered time-domain EEG from 0- 0.5 s after motor imagery onset was used as the neural-state input. A Q-KTD model trained on the healthy population was transferred to individual stroke participants, and repeated Monte Carlo simulations compared decoding performance with and without transfer learning across multiple learning epochs. ResultsHealthy and acute stroke participants showed shared motor imagery-related EEG structure, including post-onset mu-band suppression, while the stroke group exhibited greater interparticipant variability, more diffuse time- frequency modulation, and altered hemispheric asymmetry. No channel-level healthy-stroke differences in windowed band power remained significant after false discovery rate correction. Healthy-source transfer learning improved first-epoch Q-KTD success rates in 29 of 50 stroke participants (58%). Across all participants, mean success rate increased from 49.46% without transfer learning to 51.82% with transfer learning. Among participants showing positive transfer, the mean gain was 7.34% and the maximum gain was 18.75%. However, 21 participants showed negative transfer, demonstrating substantial subject-level variability. ConclusionHealthy-source Q-KTD transfer learning improved first-epoch motor imagery BMI decoding for a majority of acute stroke participants, supporting the offline feasibility of population-informed Q-KTD decoding in stroke. These early performance gains may reduce subject-specific calibration burden, although substantial interparticipant variability and negative transfer indicate the need for individualized transfer-selection or adaptation strategies. Assistive Technology ImplicationsO_LIEEG-based brain-machine interfaces may support assistive technologies for individuals with stroke-related motor impairment by translating motor imagery-related neural activity into control commands for external devices. C_LIO_LIHealthy-to-stroke transfer learning may improve early BMI neural-decoder performance and potentially reduce the amount of subject-specific calibration required. C_LIO_LIThe findings support the offline feasibility of Q-KTD for motor imagery BMI neural decoding in individuals with acute stroke. C_LIO_LISubstantial interparticipant variability and negative transfer suggest that individualized source-model selection or adaptation strategies may be needed for reliable post-stroke BMI implementation. C_LIO_LIPhysiological EEG characteristics, including ERD/ERS and hemispheric asymmetry, may provide candidate markers for future transfer-selection strategies, although their predictive value requires direct validation. C_LI