Neural Engagement in Motor Imagery EEG Analysis of Active vs. Guided Commands in Post-Stroke Patients
Bibliographic record
Abstract
Stroke severely impairs hand function, particularly in patients with low Fugl-Meyer hand subscores, where both motor and cognitive deficits challenge rehabilitation. This study aimed to compare neural engagement under different motor imagery (MI) command modalities to optimize brain–computer interface (BCI) rehabilitation strategies. Six post-stroke patients performed hand-focused MI tasks within a five-class paradigm under two conditions: direct command (explicit cues) and active calculation (self-derived MI). Electroencephalography (EEG) was used to assess neural activity through Movement-Related Cortical Potentials (MRCPs) and Event-Related Desynchronization (ERD) in the alpha and beta bands. Direct commands elicited earlier MRCP onset, stronger negative amplitudes, and more sustained ERD compared to the delayed and attenuated responses under active calculation. These findings indicate that structured cues facilitate more robust motor cortex activation, while cognitively demanding tasks may hinder consistent engagement. Direct commands appear more effective in eliciting neural activation for stroke patients with severe impairments, suggesting that reducing cognitive load enhances rehabilitation potential. Clinically, adapting BCI command strategies to patients’ cognitive capacities may improve outcomes, and future work should explore hybrid approaches that balance accessibility with cognitive engagement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".