Multimodal Behavioral Data Predict Stroke Patient’s Response to BCI Treatment Through Explainable AI
Bibliographic record
Abstract
Brain-computer interface (BCI)-based neurorehabilitation holds promise in enhancing motor recovery after stroke. However, recent research has reported heterogeneous results, indicating both responders and non-responders to BCI therapy. Using explainable artificial intelligence (XAI) methods, this study aims to investigate the independent and combined importance of multimodal behavioral data to predict patients' response to BCI therapy. Forty-two subacute stroke patients with lower-limb motor impairment underwent behavioral assessments, and received two-week BCI rehabilitation training. Linear regression, elastic net and artificial neural network models were developed to predict response to BCI therapy. Two XAI techniques, the stepwise method and Shapley additive explanation, were used to interpret model outcomes. The multivariate model (R ${}^{\mathbf {{2}}} =0.852$ , P<0.001) that combines an optimal subset of multimodal behavioral data outperformed the univariate model (R ${}^{\mathbf {{2}}} =0.758$ , P<0.001) trained on a single variable. Elastic net and artificial neural network models both demonstrated high prediction performance, as indicated by classification accuracies of 0.810 and 0.762, and areas under the receiver operating characteristic curve of 0.782 and 0.771. Our results revealed that multimodal behavioral data, including demographic, clinical, and biomechanical characteristics, provided unique and complementary information for interpreting the response of subacute patients to BCI therapy. Particularly, baseline motor impairment, muscle spasticity and balance function were primary predictors. Our findings highlight the core role of XAI methods towards precision medicine, which can help clinicians to identify individual recovery potentials and plan optimal treatment strategies.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".