Evidence map›Paper›PMID 42934124›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2026

[Theoretical system of brain-computer interface for basic research].

Yunfa Fu, Liu Yan, Fan Wang, Tianwen Li, Lei Zhao, Xue Yang, Jiaping Xu, Rongzhang Luo

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 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

8 authors.

Yunfa FuFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.
Liu YanFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.
Fan WangFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.
Tianwen LiBrain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P. R. China.
Lei ZhaoBrain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P. R. China.
Xue YangNeurorehabilitation Team, Kunming Medical University, Kunming 650500, P. R. China.
Jiaping XuFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.
Rongzhang LuoFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain-computer interface (BCI) has achieved remarkable progress in brain signal acquisition, decoding algorithms, and related fields. Nevertheless, its underlying theoretical system remains largely fragmented, lacking a systematic characterization of system decodability, performance boundaries, and dynamic constraint mechanisms. To address this gap, this paper formalizes BCI systems as a closed-loop probabilistic information processing model from the perspectives of information theory and statistical decision-making. This paper constructs a foundational theoretical system consisting of scientific hypotheses, neuroscience principles, fundamental theorems, and basic laws, derives four results capable of guiding experimental design, and illustrates the application boundaries with reference to motor imagery BCI paradigms. This theoretical system is expected to provide a unified analytical benchmark for basic BCI research and drive the paradigm shift of BCI from engineering experience-driven algorithm optimization to theory-driven system analysis and design.

Indexed as

BrainBrain-Computer InterfacesSignal Processing, Computer-AssistedAlgorithmsElectroencephalographyHumansInformation TheoryBrain–computer interfaceInformation-theoretic constraintNeuroscientific principleStatistical decision theoryTheoretical system

Identifiers

PMID42934124
PMCPMC13713244

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Registered trials

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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.