###### 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. brain-computer interface; MI-EEG; efficient channel attention; spiking neural networks; ANN-to-SNN conversion +
Footnote †: journal: MedicalVector Machines (SVM) [13], Common Spatial Pattern (CSP) [14], and Empirical Mode Decomposition (EMD) [15]. However, individual differences in EEG signals often result in poor generalization ability of traditional methods when applied to unknown datasets from new subjects, which hinders the broader application and adoption of the technology. In recent years, deep learning methods have been employed to automatically extract features, significantly improving the classification performance of MI-EEG. For example, Multi-Layer Perceptron (MLP) [16; 17], Convolutional Neural Networks (CNN) [18; 19; 20; 21], Attention-based CNN [22; 23; 24; 25], and Temporal Convolutional Networks (TCN) [26]. The attention mechanism [27; 28] has had a significant impact on the field of deep learning, with the Transformer architecture [29] and its variants based on the attention mechanism achieving excellent results in fields such as Natural Language Processing (NLP), computer vision, and speech processing. Currently, several deep learning algorithms based on the Transformer architecture have been proposed for EEG signal processing, leading to breakthrough progress in motor imagery (MI) classification tasks [23; 25]. For example, ref. [30] enhances the model's classification performance on EEG datasets combining multi-head self-attention with positional embeddings. MBEGCBAM [31] uses attention mechanisms to capture the spatiotemporal features of EEG signals. The ATCNet [32] model extracts high-level temporal features through Temporal Convolutional Networks (TCN) and multi-head attention mechanisms, focusing on the most valuable information within these features. CSANet [33] improves adaptive feature selection capabilities by employing both local and global multi-head self-attention mechanisms to capture the correlation of EEG signal information across different scales between individuals. However, these methods focus primarily on processing advanced time series and neglect the selection of high-value features across the multi-scale spatiotemporal dimensions of raw EEG data. Although D-ATCNet [34] has made some improvements, its ability to extract high-value features from the multi-scale spatiotemporal dimensions of EEG data remains limited. Moreover, this limitation also affects the inference speed of the model, leaving significant room for further improvement.