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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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A Neuromorphic Approach for Brain-Machine Interface Using Spiking Neural Networks.

Guanting Liu, Ying Yan, Sizhen He

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary

    This study introduces Spiking Neural Networks (SNNs) for brain-machine interfaces (BMIs) to decode neural signals more naturally. This novel approach enhances prosthetic control for individuals with paralysis.

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    Area of Science:

    • Neuroscience
    • Neuromorphic Computing
    • Biomedical Engineering

    Background:

    • Brain-machine interfaces (BMIs) offer potential for motor function restoration in paralysis.
    • Conventional decoding algorithms often neglect biological neural processing properties.

    Purpose of the Study:

    • To present a novel Spiking Neural Network (SNN) approach for brain-machine interface (BMI) decoding.
    • To leverage SNNs' biological plausibility for improved neural control.

    Main Methods:

    • Implemented a SNN-based decoder for offline analysis.
    • Utilized intracortical neural recordings from the primary motor cortex (M1) and dorsal premotor cortex (PMd).
    • Decoded neural activity into continuous 2D cursor movements in a macaque model.

    Main Results:

    • Demonstrated the feasibility of using SNNs for decoding neural signals.
    • Showcased SNNs' ability to capture complex, time-varying neural representations.
    • Indicated potential for more naturalistic and adaptive BMI control.

    Conclusions:

    • Spiking Neural Networks offer a promising, biologically plausible alternative for BMI decoding.
    • This approach may lead to more intuitive and responsive prosthetic device control.
    • Further research into SNNs could advance neuroprosthetic technology for paralysis recovery.