Brain-Computer Interface in the Treatment of Stroke
Bibliographic record
Abstract
Stroke remains a leading cause of adult disability, leaving most survivors enduring persistent motor deficits. Traditional rehabilitation often yields some recovery initially, yet soon plateaus, especially in severely impaired patients. Brain–computer interfaces (BCIs) decode cortical signals (e.g., EEG, ECoG, fNIRS) with pattern‐recognition algorithms to create closed‐loop systems that deliver real‐time feedback. Common paradigms include motor imagery or attempted‐movement tasks, functional electrical stimulation (BCI‐FES), robotic exoskeleton control and virtual‐reality training. There is much evidence from randomized controlled trials and meta-analyses supporting BCI-based interventions’ validity and effectiveness in both motor improvement and enhancing neuroplasticity compared to conventional therapy. Despite this potential, BCIs have usability problems (such as long calibration times and user fatigue). In addition, technical difficulties like noisy and nonstationary signals, limited bandwidth, complicated setup requirements, and ethical issues regarding both informed consent and data privacy do exist. Overcoming all the restrictions mentioned above and fulfilling the clinical potential of BCIs is the current ongoing goal. This review aims to synthesize current evidence, highlight key technological and clinical challenges, and propose strategic directions for translating BCIs into routine post-stroke rehabilitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".