###### Abstract
Furthermore, models based on the Transformer architecture typically require high energy consumption, making them unsuitable for portable devices, which greatly limits the development of Brain-Computer Interface (BCI) applications. Spiking Neural Networks (SNNs) [35] use discrete spike sequences (0 or 1) for computation and information transmission. Compared to Artificial Neural Networks (ANNs), SNNs have great prospects in brain-like intelligence due to their low power consumption, event-driven characteristics, and biological plausibility [36]. With technological advancements, SNNs have begun to adopt advanced ANN architectures to improve performance, such as ResNet-like SNNs [37; 38; 39] and Recurrent Neural Networks (RNNs) [40]. Therefore, integrating the Transformer structure into SNNs while retaining their brain-like and low-power advantages is a crucial direction in the current development of SNN algorithms and neuromorphic chips. Through the ANN-to-SNN conversion method, the parameters of pre-trained ANN models can be mapped onto SNN models with the same structure [41; 42]. However, more time steps are usually required to achieve comparable performance [43]. Furthermore, existing methods struggle to simulate the Softmax activation function in the self-attention mechanism with minimal loss using spiking neurons [44], which significantly impacts model performance and the overall feasibility of SNN-Transformer integration. In recent years, several studies have focused on EEG classification tasks based on SNNs. Initially, some researchers treated SNNs as classifiers [45; 46], overlooking the feature extraction capabilities of SNNs. [PERSON] et al. [47] integrated spatial convolution, temporal convolution, and recurrent layers, proposing a convolutional SNN architecture for the four-class classification of EEG-based MI. [PERSON] et al. proposed SCNet [48], a lightweight spiking network inspired by the EEGNet architecture. [PERSON] et al. [49] proposed a novel hybrid response (HR) spiking module, improving the frequency sensitivity of the SNN. However, the SNN studies mentioned above employed direct training methods, and their model performance was significantly below that of advanced ANNs. To improve the feature extraction capability of raw EEG data, this paper proposes an Efficient Channel Attention Convolution (ECA-conv) architecture, namely, the Efficient Channel Attention Temporal Convolution Network (ECA-ATCNet), which incorporates efficient spatial and spectral attention mechanisms for decoding important features across multiple dimensions of raw EEG data. In addition, to address the high energy consumption issue of the model, the Spike Integrated Transformer conversion (SIT-conversion) method based on Spiking-Softmax is proposed to convert self-attention into SNNs, which can achieve nearly lossless ANN-to-SNN conversion with few time steps. Overall, the contributions of this work are summarized as follows:  
* This paper proposes an efficient ECA-ATCNet model, which incorporates ECA-conv, convolution-based sliding windows, and attention-based time convolution modules;
* The use of Efficient Channel Attention Convolution (ECA-conv) enhances the adaptive feature selection capability across the spatial and spectral dimensions of inter-individual EEG signals;
* This paper proposes a SIT-conversion method based on Spiking-Softmax, which converts the self-attention of the Transformer model to SNNs almost lossless;
* This paper proposes the Spiking-Softmax method, which achieves ultra-high precision simulation of the Softmax function in SNNs through the integrated cooperation of a new Spiking Exponential Neuron (SI-exp) and Spiking Collaboration Normalized Neuron (SI-norm).