Spiking Neural Network for Intra-cortical Brain Signal Decoding
This work addresses the need for accurate and energy-efficient decoding in intra-cortical brain-computer interfaces, representing an incremental improvement by combining existing techniques.
The paper tackled the problem of decoding intra-cortical brain signals for brain-computer interfaces by proposing a spiking neural network (SNN) with a feature fusion approach, achieving higher accuracy and tens to hundreds of times greater efficiency compared to traditional methods.
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.