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Related Experiment Video

Updated: Jun 27, 2026

Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke
09:42

Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke

Published on: September 1, 2023

Self-Rectifying Integrate-and-Fire Neuron and Collaborative Trim Training Framework for SNN-Based EEG Motor Imagery

Yifan Chen1, Weihao Sun1, Ming Meng1

  • 1School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.

Brain Sciences
|June 26, 2026
PubMed
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This study introduces a novel training method for Spiking Neural Networks (SNNs), enhancing their performance in brain-computer interfaces. The approach achieves Artificial Neural Network (ANN) comparable results with SNNs' energy efficiency.

Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Spiking Neural Networks (SNNs) offer biological plausibility and energy efficiency for brain-computer interfaces.
  • Training SNNs is challenging due to the discrete nature of spikes, hindering gradient-based methods.
  • Weight transfer from Artificial Neural Networks (ANNs) to SNNs introduces conversion errors.

Purpose of the Study:

  • To develop a novel training methodology for Spiking Neural Networks (SNNs).
  • To overcome the limitations of existing SNN training and conversion methods.
  • To achieve competitive performance in brain-computer interfaces while retaining SNN advantages.

Main Methods:

  • Introduction of the self-rectifying integrate-and-fire (SRIF) neuron to mitigate asynchronism and clipping errors using negative and rectification spikes.
Keywords:
brain–computer interfaceelectroencephalographymotor imageryspiking neural network

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Last Updated: Jun 27, 2026

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  • Proposal of a collaborative trim (CT) training framework with a quantized network to refine SNN weights and outputs.
  • Application of the methodology to EEG-based motor imagery classification.

    • Main Results:

    • The proposed training methodology enables SNNs to achieve performance metrics comparable to ANNs.
    • EEG-based motor imagery classification accuracy is significantly improved.
    • The method successfully reduces conversion errors inherent in ANN-to-SNN weight transfer.

    Conclusions:

    • The developed method allows SNNs to match ANN classification performance.
    • The approach leverages the inherent energy efficiency and computational simplicity of SNNs.
    • This work enhances the practical applicability of SNNs in brain-computer interfaces.