Evidence map›Paper›PMID 42382696›Full record

ReviewExploration (Beijing, China)2026

Brain-Computer Interfaces: The Dawn of a New Era in Disease Treatment.

Yuqi Feng, Wangzheqi Zhang, Jun Chen, Huang Wu, Lei Wu, Yanhao Qiu, Xiaoming Deng, Chenglong Zhu, Yisheng Chen, Zhijie Zhao and 2 more

Abstract readReview
In one paragraph

Review in Exploration (Beijing, China), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yuqi FengFaculty of Anesthesiology Changhai Hospital Naval Medical University Shanghai China.
Wangzheqi ZhangFaculty of Anesthesiology Changhai Hospital Naval Medical University Shanghai China.
Jun ChenFaculty of Anesthesiology Changhai Hospital Naval Medical University Shanghai China.
Huang WuFaculty of Anesthesiology Changhai Hospital Naval Medical University Shanghai China.
Lei WuFaculty of Anesthesiology Changhai Hospital Naval Medical University Shanghai China.
Yanhao QiuFaculty of Anesthesiology Changhai Hospital Naval Medical University Shanghai China.
Xiaoming DengFaculty of Anesthesiology Changhai Hospital Naval Medical University Shanghai China.
Chenglong ZhuFaculty of Anesthesiology Changhai Hospital Naval Medical University Shanghai China.
Yisheng ChenFujian Key Laboratory of Toxicant and Drug Toxicology Medical College Ningde Normal University Ningde China.
Zhijie ZhaoDepartment of Plastic and Reconstructive Surgery Shanghai Ninth People's Hospital Shanghai JiaoTong University School of Medicine Shanghai China.
Changli WangFaculty of Anesthesiology Changhai Hospital Naval Medical University Shanghai China.ORCID https://orcid.org/0000-0001-5718-2667
Xiaomin ZhangDepartment of Otolaryngology Naval Medical Center of PLA Navy Medical University Shanghai China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain-computer interface (BCI) technology has emerged as a crucial interdisciplinary advancement in the field of neuropsychiatric disease treatment. With the global rise in the prevalence of neurological and psychiatric disorders, which impose a substantial burden on society, BCI offers a novel approach. Since the discovery of bioelectric phenomena in the 19th century, various classification frameworks have been developed based on signal paradigms, invasiveness, and feedback mechanisms. BCI applications span multiple disease areas. In movement disorders, it aids in restoring motor function through prosthetic control, functional electrical stimulation, and brain stimulation-based therapies. For patients with communication barriers, it enables alternative communication methods and speech-related neural signal decoding. In psychiatric conditions, BCI shows growing potential in both diagnosis and treatment, particularly in conditions like autism and depression. Despite significant progress, BCI faces challenges. The long-term biocompatibility of electrodes and the resolution of neural signals remain to be improved. To address these limitations, research on new electrode materials, such as carbon nanomaterials and composites, is ongoing. Emerging BCI technologies, including endovascular BCI and optogenetics BCI, present new possibilities. The integration of multimodal technologies and artificial intelligence in BCI systems is expected to enhance performance and enable more personalized treatment. Overall, BCI technology holds great promise for improving the quality of life of patients with neuropsychiatric disorders and driving innovation in the medical and neuroscience fields.

Indexed as

brain–computer interfacecommunication barrierselectrode materialsmovement disordersneuropsychiatric disorderspsychiatric disorders

Identifiers

PMID42382696
PMCPMC13317701

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.