Abstract
Spiking neural networks (SNN) has the advantages of low power consumption and high efficiency in processing temporal information. However, due to the difficulty of network training, there exist few studies about the applications of SNN in brain-computer interface (BCI), especially in the four-classification task of motor imagery (MI). In this study, we develop a four-layer SNN structure to solve the MI four-classification problem. Firstly, an improved optimization algorithm for Ben’s spiker algorithm (BSA) is presented to convert EEG signals into spike signals, which obtains about 50 times higher efficiency than the commonly used optimizing algorithms. Secondly, a SNN combined with spike long-short-time-memory (LSTM) module is proposed to perform four-classification tasks in MI. Finally, we introduce the channel-wise normalization strategy to facilitate the training of deeper layers. Our experiment on the publicly released dataset achieves the accuracy that is comparable to the previous work of one-Dimension convolution neural network (1D-CNN). Meanwhile, the number of parameters of proposed network is about 1/10 of that in 1D-CNN. This study reveals the great potential of the SNN in developing a low-power and wearable BCI system.
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Li, Y., Shen, H., Hu, D. (2023). A Spiking Neural Network for Brain-Computer Interface of Four Classes Motor Imagery. In: Ying, X. (eds) Human Brain and Artificial Intelligence. HBAI 2022. Communications in Computer and Information Science, vol 1692. Springer, Singapore. https://doi.org/10.1007/978-981-19-8222-4_13
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DOI: https://doi.org/10.1007/978-981-19-8222-4_13
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