Year: 2026
Type: article
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.
Source: PubMed Central
Language: Chinese
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7223868236