ECA-ATCNet: Efficient EEG Decoding with Spike Integrated Transformer Conversion for BCI Applications

Abstract

The Brain–Computer Interface (BCI) has applications in smart homes and healthcare by converting EEG signals into control commands. However, traditional EEG signal decoding methods are affected by individual differences, and although deep learning techniques have made significant breakthroughs, challenges such as high energy consumption and the processing of raw EEG data remain. This paper introduces the Efficient Channel Attention Temporal Convolutional Network (ECA-ATCNet) to enhance feature learning by applying Efficient Channel Attention Convolution (ECA-conv) across spatial and spectral dimensions. The model outperforms state-of-the-art methods in both within-subject and between-subject classification tasks on MI-EEG datasets (BCI-2a and PhysioNet), achieving accuracies of 87.89% and 71.88%, respectively. Additionally, the proposed Spike Integrated Transformer Conversion (SIT-conversion) method, based on Spiking–Softmax, converts the Transformer’s self-attention mechanism into Spiking Neural Networks (SNNs) in just 12 time steps. The accuracy loss of the converted ECA-ATCNet model is only 0.6% to 0.73%, while its energy consumption is reduced by 52.84% to 53.52%. SIT-conversion enables ultra-low-latency, near-lossless ANN-to-SNN conversion, with SNNs achieving similar accuracy to their ANN counterparts on image datasets. Inference energy consumption is reduced by 18.18% to 45.13%. This method offers a novel approach for low-power, portable BCI applications and contributes to the advancement of energy-efficient SNN algorithms.