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MetaNIRS: A general decoding framework for fNIRS based motor execution/imagery.

Yu Li1, Yu Sun2, Feng Wan3

  • 1School of Electronics and Information Engineering at Wuyi University, Jiangmen, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 19, 2025
PubMed
Summary
This summary is machine-generated.

The novel MetaNIRS framework improves brain-computer interface (BCI) performance for motor execution (ME) and motor imagery (MI) decoding using functional near-infrared spectroscopy (fNIRS). This approach enhances BCI accuracy in rehabilitation applications.

Keywords:
Brain-computer interface (BCI)Motor execution (ME)Motor imagery (MI)PoolFormerfNIRS

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Functional near-infrared spectroscopy (fNIRS) is a key technology for monitoring brain activity, with significant potential in brain-computer interfaces (BCI) for rehabilitation.
  • Current fNIRS-based BCI systems exhibit suboptimal performance in decoding motor execution (ME) and motor imagery (MI).
  • The PoolFormer framework has shown success in visual tasks, suggesting its potential adaptability to other domains like neuroimaging.

Purpose of the Study:

  • To introduce a novel framework, MetaNIRS, integrating a Long-range Dilation Multilayer Perceptron (LongDilMLP) with the PoolFormer architecture for enhanced fNIRS signal processing.
  • To evaluate the effectiveness of MetaNIRS for both motor execution (ME) and motor imagery (MI) classification tasks.
  • To assess the practical applicability and robustness of the proposed MetaNIRS framework in real-world BCI scenarios.

Main Methods:

  • Development of a novel Long-range Dilation Multilayer Perceptron (LongDilMLP) to capture hemodynamic characteristics specific to fNIRS signals.
  • Integration of LongDilMLP with the PoolFormer framework to create the MetaNIRS system.
  • Validation of MetaNIRS using two public ME datasets and one self-collected MI dataset, including cross-subject accuracy assessments and ablation studies.

Main Results:

  • MetaNIRS achieved average accuracies of 76.00% (ME), 57.45% (MI), and 84.14% (combined/other) with cross-subject accuracies of 77.24%, 58.55%, and 85.52%, respectively.
  • Sensitivity experiments confirmed the robustness of MetaNIRS to parameter variations.
  • Ablation studies demonstrated the importance of each component within MetaNIRS and the superiority of LongDilMLP over traditional MLPs, with visualization highlighting the utility of the initial signal segments.

Conclusions:

  • The MetaNIRS framework, incorporating LongDilMLP and PoolFormer, offers a significant advancement in decoding motor execution and motor imagery from fNIRS data.
  • The proposed model provides an efficient and generalizable decoding framework, improving the performance of fNIRS-based BCIs for applications in neurorehabilitation.
  • The findings underscore the potential of MetaNIRS to overcome current limitations in fNIRS-BCI performance, paving the way for more effective assistive technologies.