## 5 Conclusions  
In this paper, we first propose the Efficient Channel Attention Temporal Convolutional Network (ECA-ATCNet) for EEG-based motor imagery classification. The Efficient Channel Attention in the ECA-Conv module, which operates in parallel across both spatial and spectral dimensions, enhances the model's ability to decode raw EEG signals, thereby improving classification performance, as demonstrated by ablation experiments. The model achieves an average accuracy of 87.89% in the within-subject classification task on the BCI-2a dataset and 71.88% accuracy in the between-subject classification task on the PhysioNet MI-EEG dataset, outperforming existing state-of-the-art (SOTA) models. Despite our model's superior performance compared to existing state-of-the-art methods, the sample size of currently available public MI-EEG datasets remains limited. The scale of these datasets is insufficient to fully capture the diverse EEG characteristics across different subjects and environmental conditions, thus limiting the model's generalization capacity in more varied settings. In future work, we intend to collaborate with medical institutions to validate the model in the context of various neurological disease diagnosis tasks, evaluating its generalization ability across different clinical scenarios. Furthermore, we compare our method with SOTA models on different tasks to validate its superiority. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Model** & **Method** & **Architecture** & \begin{tabular}{c} **Time** \\ **Steps** \\ \end{tabular} & \
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Conversion** \\ **Loss (\%)** \\ \end{tabular} \\ \hline \multirow{7}{*}{CIFAR10} & Spikformer [75] & Direct Training & Spikformer-4-384 & 4 & 95.19 & / \\  & Spikingformer [76] & Direct Training & Spikingformer-4-384 & 4 & 95.61 & / \\ \cline{2-6}  & SNM [73] & ANN-to-SNN & ResNet-18 & 128 & 95.19 & \(-\)0.02 \\  & QCFS [74] & ANN-to-SNN & ResNet-18 & 32 & 96.08 & \(+\)0.04 \\  & MST [42] & ANN-to-SNN & Swin-T (BN) & 256 & 97.27 & \(-\)1.01 \\ \cline{2-6}  & SIT-EdgeNeXt (ours) & ANN-to-SNN & EdgeNeXt & 12 & **97.69**1  & 0.00 \\  & SIT-Next-ViT (ours) & ANN-to-SNN & Next-ViT & 12 & 97.61 & \(-\)0.01 \\  & SIT-UniFormer (ours) & ANN-to-SNN & UniFormer & 12 & 97.59 & \(\mathbf{+0.13}\)1  \\ \hline \multirow{7}{*}{CIFAR100} & Spikformer [75] & Direct Training & Spikformer-4-384 & 4 & 77.86 & / \\  & Spikingformer [76] & Direct Training & Spikingformer-4-384 & 4 & 79.09 & / \\ \cline{2-6}  & SNM [73] & ANN-to-SNN & ResNet-18 & 128 & 77.97 & \(-\)0.29 \\  & QCFS [74] & ANN-to-SNN & ResNet-18 & 512 & 79.61 & \(\mathbf{+0.81}\)1  \\  & MST [42] & ANN-to-SNN & Swin-T (BN) & 256 & \(\mathbf{86.91}\)1  & \(-\)1.81 \\ \cline{2-6}  & SIT-EdgeNeXt (ours) & ANN-to-SNN & EdgeNeXt & 12 & 85.37 & \(-\)0.03 \\  & SIT-Next-ViT (ours) & ANN-to-SNN & Next-ViT & 12 & 86.55 & \(+\)0.01 \\  & SIT-UniFormer (ours) & ANN-to-SNN & UniFormer & 12 & 85.77 & \(+\)0.07 \\ \hline \multirow{7}{*}{ImageNet} & Spikformer [75] & Direct Training & Spikformer-8-768 & 4 & 74.81 & / \\  & Spikingformer [76] & Direct Training & Spikingformer-8-786 & 4 & 75.85 & / \\ \cline{2-6}  & SNM [73] & ANN-to-SNN & VGG16 & 1024 & 73.16 & \(-\)0.02 \\ \cline{1-1}  & QCFS [74] & ANN-to-SNN & ResNet-18 & 1024 & 74.32 & \(\mathbf{+0.03}\)1  \\ \cline{1-1}  & MST [42] & ANN-to-SNN & Swin-T (BN) & 512 & 78.51 & \(-\)2 \\ \cline{1-1} \cline{2-6}  & SIT-EdgeNeXt (ours) & ANN-to-SNN & EdgeNeXt & 12 & 79.18 & \(-\)0.24 \\ \cline{1-1}  & SIT-Next-ViT (\({}^{1}\) ours) & ANN-to-SNN & Next-ViT & 12 & \(\mathbf{81.71}\)1  & 0.00 \\ \cline{1-1}  & SIT-UniFormer (ours) & ANN-to-SNN & UniFormer & 12 & 77.11 & \(-\)0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of the SNNs generated by SIT-conversion of EdgeNeXt, Next-ViT, and UniFormer models with SOTA SNNs on CIFAR10, CIFAR100, and ImageNet-1K datasets.