用脉冲神经网络提升脑机接口解码精度与能效
Spiking Neural Network for Intra-cortical Brain Signal Decoding
- 结合人工特征与深度学习特征进行融合输入
- 在两只猕猴实验中准确率更高，效率提升数十至百倍
- 适合高精度低功耗的植入式脑机接口场景
精准高效的脑信号解码对皮层内脑机接口至关重要。基于神经活动向量特征的传统方法准确率低，而深度学习方法计算开销大。本文提出一种脉冲神经网络（SNN）用于高效且节能的皮层内脑信号解码，并设计了一种特征融合方法，将人工提取的神经活动特征与深度神经网络自动提取的特征相结合，进一步提升解码准确率。在两只猕猴的运动相关皮层脑信号解码实验中，所提SNN模型准确率优于传统人工神经网络，更重要的是，其计算效率提升了数十到上百倍。该模型非常适合高精度、低功耗的应用场景，如皮层内脑机接口。
原文摘要 · Abstract (English)
Decoding brain signals accurately and efficiently is crucial for intra-cortical brain-computer interfaces. Traditional decoding approaches based on neural activity vector features suffer from low accuracy, whereas deep learning based approaches have high computational cost. To improve both the decoding accuracy and efficiency, this paper proposes a spiking neural network (SNN) for effective and energy-efficient intra-cortical brain signal decoding. We also propose a feature fusion approach, which integrates the manually extracted neural activity vector features with those extracted by a deep neural network, to further improve the decoding accuracy. Experiments in decoding motor-related intra-cortical brain signals of two rhesus macaques demonstrated that our SNN model achieved higher accuracy than traditional artificial neural networks; more importantly, it was tens or hundreds of times more efficient. The SNN model is very suitable for high precision and low power applications like intra-cortical brain-computer interfaces.
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