Evidence map›Paper›PMID 41987849›Full record

ReviewMedComm2026

Emerging Neural Recording and Neurostimulation Technologies Based on Brain-Computer Interface: A Promising Approach for Neuropsychiatric Disorders.

Yeguang Xu, Danyang Chen, Qing Ye, Peng Zhang, Jian Shi, Shengjie Li, Yuhao Sun, Zhixian Zhao, Yingxin Tang, Ping Zhang and 1 more

Abstract readReview
In one paragraph

Review in MedComm, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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

11 authors.

Yeguang XuDepartment of Neurology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China.
Danyang ChenDepartment of Neurology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China.
Qing YeBrain-Computer Interface Research Institute Tongji Hospital Tongji Medical College of Huazhong University of Science and Technology Wuhan Hubei China.
Peng ZhangHubei Bioinformatics and Molecular Imaging Key Laboratory Department of Biomedical Engineering College of Life Science and Technology Huazhong University of Science and Technology Wuhan Hubei China.
Jian ShiBrain-Computer Interface Research Institute Tongji Hospital Tongji Medical College of Huazhong University of Science and Technology Wuhan Hubei China.
Shengjie LiDepartment of Neurology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China.
Yuhao SunDepartment of Neurology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China.
Zhixian ZhaoDepartment of Neurology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China.
Yingxin TangDepartment of Neurology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China.
Ping ZhangDepartment of Neurology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China.
Zhouping TangDepartment of Neurology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurological and psychiatric disorders, arising from disruptions in neural circuitry, pose a major and growing challenge to global healthcare systems. Brain-computer interface (BCI) technology has emerged as a promising approach, enabling direct communication between the brain and external devices. By facilitating bidirectional interaction with the nervous system, BCIs open new avenues for both diagnosis and treatment. In this review, we examine recent advances in recording and stimulation technologies within the BCI framework and evaluate their therapeutic potential across major neuropsychiatric disorders. We focus particularly on post-stroke motor rehabilitation as a representative paradigm, providing detailed analysis of the mechanisms, clinical evidence, and future prospects of endovascular BCI, BCI-integrated epidural spinal cord stimulation, and BCI-driven deep brain stimulation. We further extend the discussion to movement disorders such as Parkinson's disease and epilepsy, as well as cognitive and psychiatric conditions including Alzheimer's disease and depression, highlighting how BCI-based approaches enable symptom detection and closed-loop neuromodulation. Additionally, we address ethical and societal considerations accompanying clinical translation of these advanced neurotechnologies. By integrating current evidence, this review highlights a paradigm shift toward more active, precise, and personalized neural rehabilitation enabled by BCI systems, while outlining key challenges and future directions for research and clinical application.

Indexed as

brain–computer interfaceclosed‐loop stimulationdeep brain stimulationendovascular electrode arraysepidural spinal cord stimulationneuropsychiatric disorders

Identifiers

PMID41987849
PMCPMC13077214

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.