Effectiveness of motor and prefrontal cortical areas for brain-controlled functional electrical stimulation neuromodulation
Bibliographic record
Abstract
Abstract Objective . Brain-computer interface (BCI)-controlled functional electrical stimulation (FES) could excite the central nervous system to enhance upper limb motor recovery. Our current study assessed the effectiveness of motor and prefrontal cortical activity-based BCI-FES to help elucidate the underlying neuromodulation mechanisms of this neurorehabilitation approach. Approach . The primary motor cortex (M1) and prefrontal cortex (PFC) BCI-FES interventions were performed for 25 min on separate days with twelve non-disabled participants. During the interventions, a single electrode from the contralateral M1 or PFC was used to detect event-related desynchronization (ERD) in the calibrated frequency range. If the BCI system detected ERD within 15 s of motor imagery, FES activated wrist extensor muscles. Otherwise, if the BCI system did not detect ERD within 15 s, a subsequent trial was initiated without FES. To evaluate neuromodulation effects, corticospinal excitability was assessed using single-pulse transcranial magnetic stimulation, and cortical excitability was assessed by motor imagery ERD and resting-state functional connectivity before, immediately, 30 min, and 60 min after each intervention. Main results . M1 and PFC BCI-FES interventions had similar success rates of approximately 80%, while the M1 intervention was faster in detecting ERD activity. Consequently, only the M1 intervention effectively elicited corticospinal excitability changes for at least 60 min around the targeted cortical area in the M1, suggesting a degree of spatial localization. However, cortical excitability measures did not indicate changes after either M1 or PFC BCI-FES. Significance . Neural mechanisms underlying the effectiveness of BCI-FES neuromodulation may be attributed to the M1 direct corticospinal projections and/or the closer timing between ERD detection and FES, which likely enhanced Hebbian-like plasticity by synchronizing cortical activation detected by the BCI system with the sensory nerve activation and movement related reafference elicited by FES.
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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 | 0.001 |
| 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) | 0.000 | 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".