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Record W7117484082 · doi:10.1002/brx2.70043

Brain‐computer interface in clinical application: How far is it from realization?

2025· article· en· W7117484082 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBrain‐X · 2025
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsnot available
FundersScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of China
KeywordsClinical trialBrain–computer interfaceSpinal cord injuryBeijingClinical researchChinaRandomized controlled trialParaplegia

Abstract

fetched live from OpenAlex

Brain-computer interface (BCI) is rapidly transitioning from concept verification to practical implementation,1 and clinical trials on BCIs are currently underway across the globe.2 The wireless minimally invasive implantable BCI, termed the Neural Electronic Opportunity (NEO), was developed by the team at Tsinghua University and successfully completed its inaugural clinical trial at Xuanwu Hospital in China in 2023. The next year, Beijing Tiantan Hospital in China reported a successful case where the NEO was used to assist a patient with high-level paraplegia in achieving mind control of cursor movement. Another successful case in China occurred in November 2024 in Huashan Hospital. The 38-year-old patient had been unable to grip and stand due to cervical spinal cord injury following a car accident. The patient recovered from the implantation procedure and was able to rise from bed and use a wheelchair on the third postoperative day. Clinical trials on BCIs are now in progress at many hospitals in China. Meanwhile, Elon Musk announced on X that Neuralink would implant its first chip into a human brain, marking the commencement of Neuralink's first human clinical trial on January 30, 2024. Neuralink received FDA approval for human trials in May 2023. As of September 2025, 12 individuals have received Neuralink's BCI device implants.3 Neuralink's first submission of research findings to the New England Journal of Medicine indicates that BCI technology is advancing to a new stage of development.4 Health Canada approved Neuralink's clinical trials to comprehensively evaluate the self-developed fully implantable wireless BCI system in 2024. The trials in China differ from the Neuralink trials of a fully implantable BCI in that the Chinese trials involve minimally invasive procedures which do not require opening the dura, resulting in a more stable electrode status, a lower risk of infection, and greater safety for patients. The advantage of BCI technology could be advantageous to patients living with disabilities due to spinal cord injury,5, 6 stroke,7-9 treatment-resistant depression,10 and other conditions,10 enhancing their ability to communicate,11-13 control and recover limb function,14 and improving quality of life. Although BCI technology has demonstrated clear clinical value for patients with disabilities, there remain bottlenecks in core algorithm breakthroughs.15-18 Additionally, the clinical application of BCIs is limited not only by technical challenges but also by medical ethics—which mandates minimizing potential harm—and by the significant cost of treatment. BCIs remain distant from widespread clinical implementation, primarily due to two prerequisites: first, demonstrable clinical efficacy must be established and substantiated to confirm sustained effectiveness; second, robust evidence-based validation is essential for its inclusion in clinical practice guidelines, which is a necessary condition for standardized adoption in routine medical care. Eventual large-scale clinical applications should use BCI medical products which are accessible, easy to use, effective and affordable. These are equally important considerations in the industrialization of BCIs. Shugeng Chen: Writing—review and editing; writing—original draft; funding acquisition; conceptualization; methodology; investigation; visualization; data curation; formal analysis. Lei Jiang: Writing—original draft; writing—review and editing; conceptualization. Jie Jia: Writing—review and editing; funding acquisition; conceptualization; methodology; investigation; supervision. This work was supported by the National Natural Science Foundation of China (82202798), the Project of Shanghai Science and Technology Commission (24YL1900202) and the Shanghai Sailing Program (22YF1404200). The authors declare no conflicts of interest. The National Natural Science Foundation of China, 82202798; Project of Shanghai Science and Technology Commission, 24YL1900202; Shanghai Sailing Program, 22YF1404200. The ethics approval is not needed in this study. Data sharing is not applicable to this article as no new data was created or analyzed in this study.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.624
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.361
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it