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Updated: Jan 30, 2026

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A principal brain-region analysis framework based on evolutionary decomposition for fNIRS brain-computer interfaces.

Jiewei Lu1,2,3, Yinuo Liu1, Xinyuan Zhang1

  • 1College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China.

Journal of Neural Engineering
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

Functional near-infrared spectroscopy (fNIRS) brain-computer interfaces (BCIs) benefit from a new Principal Brain-Region Analysis (PBA) framework. This method enhances accuracy and explainability by focusing on task-specific brain regions and their interactions.

Keywords:
brain–computer interfacesevolutionary decompositionfNIRSprincipal brain-region analysis

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Area of Science:

  • Neuroscience
  • Biomedical Engineering

Background:

  • Functional near-infrared spectroscopy (fNIRS) is a developing brain-computer interface (BCI) technology.
  • Current BCI methods often overlook the specific roles and interactions of task-related brain regions.

Purpose of the Study:

  • To introduce a Principal Brain-Region Analysis (PBA) framework to improve BCI performance.
  • To identify and leverage task-specific brain regions (TSBRs) and their collaborative functions.

Main Methods:

  • Formulating TSBR identification as an optimization problem with spatial constraints.
  • Employing an evolutionary decomposition algorithm combining spatial nondominated operators and genetic iterative computation.
  • Training classifiers using neuroimaging features from identified TSBRs and stacking for final predictions.

Main Results:

  • The PBA method significantly improved classification accuracy on two public fNIRS-BCI datasets.
  • Accuracy enhancements were observed for both sliding slope-based and sliding mean concentration change methods.
  • Specific accuracy gains ranged from 6.03% to 13.62% depending on the method and dataset.

Conclusions:

  • Principal Brain-Region Analysis (PBA) offers a novel framework for advancing BCI accuracy.
  • The PBA approach enhances the explainability of BCIs by focusing on critical brain regions.
  • This method represents a significant step forward in optimizing fNIRS-based brain-computer interfaces.