Spiking Neural Networks for EEG Signal Analysis Using Wavelet Transform

Li YuanJian WeiYing Liu^*

• Academy of Military Sciences, Beijing, China

Brain-computer interfaces (BCIs) leverage EEG signal processing to enable human-machine communication and have broad application potential. \textcolor{blue}{However, existing deep learning-based BCI methods face two critical limitations that hinder their practical deployment: reliance on manual EEG feature extraction, which constrains their ability to adaptively capture complex neural patterns, and high energy consumption characteristics that make them unsuitable for resource-constrained portable BCI devices requiring edge deployment. To address these limitations, this work combines wavelet transform for automatic feature extraction with spiking neural networks for energy-efficient computation. Specifically, we present a novel spiking transformer that integrates a spiking self-attention mechanism with discrete wavelet transform, termed SpikeWavformer. SpikeWavformer enables automatic EEG signal time-frequency decomposition, eliminates manual feature extraction, and provides energy-efficient classification decision-making, thereby enhancing the model's cross-scene generalization while meeting the constraints of portable BCI applications.} Experimental results demonstrate \textcolor{blue}{the effectiveness of SpikeWavformer} in emotion recognition and auditory attention decoding tasks, highlighting its practicality for resource-constrained BCI applications.