Informed consent competency assessment for brain-computer interface clinical research and application in psychiatric disorders: A systematic review
Bibliographic record
Abstract
BACKGROUND: Brain-computer interface (BCI) technology is rapidly advancing in psychiatry. Informed consent competency (ICC) assessment among psychiatric patients is a pivotal concern in clinical research. AIM: To analyze the assessment of ICC and form a framework with multi-dimensional elements involved in ICC of BCI clinical research among psychiatric disorders. METHODS: A systematic review of studies regarding ICC assessments of BCI clinical research in patients with six kinds of psychiatric disorders was conducted. A systematic literature search was performed using PubMed, ScienceDirect, and Web of Science. Peer-reviewed articles and full-text studies were included in the analysis. There were no date restrictions, and all studies published up to February 27, 2025, were included. RESULTS: A total of 103 studies were selected for this review. Fifty-eight studies included ICC factors, and forty-five were classified in ICC related ethical issues of BCI research in six kinds of psychiatric disorders. Executive function impairment is widely recognized as the most significant factor impacting ICC, and processing speed deficits are observed in schizophrenia, mood disorders, and Alzheimer's disease. Memory dysfunction, particularly episodic and working memory, contributes to compromised ICC. Five core ethical issues in BCI research should be addressed: BCI specificity, vulnerability, autonomy, dynamic ICC, comprehensiveness, and uncertainty. CONCLUSION: A Five-Dimensional evaluative framework, including clinical, ethical, sociocultural, legal, and procedural dimensions, is constructed and proposed for future ICC research in BCI clinical research involving psychiatric disorders.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".