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arXiv · 2412.20714

Effective and Efficient Intracortical Brain Signal Decoding with Spiking Neural Networks

Haotian Fu·Peng Zhang·Song Yang·Herui Zhang·Ziwei Wang·Dongrui Wu
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Abstract

A brain-computer interface (BCI) facilitates direct interaction between the brain and external devices. To concurrently achieve high decoding accuracy and low energy consumption in invasive BCIs, we propose a novel spiking neural network (SNN) framework incorporating local synaptic stabilization (LSS) and channel-wise attention (CA), termed LSS-CA-SNN. LSS optimizes neuronal membrane potential dynamics, boosting classification performance, while CA refines neuronal activation, effectively reducing energy consumption. Furthermore, we introduce SpikeDrop, a data augmentation strategy designed to expand the training dataset thus enhancing model generalizability. Experiments on invasive spiking datasets recorded from two rhesus macaques demonstrated that LSS-CA-SNN surpassed state-of-the-art artificial neural networks (ANNs) in both decoding accuracy and energy efficiency, achieving 0.80-3.87% performance gains and 14.78-43.86 times energy saving. This study highlights the potential of LSS-CA-SNN and SpikeDrop in advancing invasive BCI applications.

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Haotian Fu, Peng Zhang, Song Yang, Herui Zhang, Ziwei Wang, Dongrui Wu. 2024-12-30. Effective and Efficient Intracortical Brain Signal Decoding with Spiking Neural Networks. https://arxiv.org/abs/2412.20714

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