## 5 Conclusions
Furthermore, in this study, we observed that although our proposed model demonstrated performance improvements over SOTA methods, the statistical significance test did not indicate a significant difference (\(p>0.05\)). This result is consistent with previous EEG studies, where high inter-subject variability, limited dataset size, and the inherent noise in EEG signals often constrain the statistical power of significance tests. In future work, our objective is to incorporate larger and more diverse EEG datasets (through collaboration with hospitals) to enhance statistical power and further validate the robustness of our model. Then, we will use attention maps and saliency analysis to reveal the features and signal regions that the model prioritizes during the decision-making process, exploring the model's focus on EEG channels and frequency bands in different tasks. This will enhance the transparency and interpretability of the model, providing stronger support for its application in the medical field. Next, we propose an SIT-conversion method based on Spiking-Softmax, which requires only 12 time steps to convert the self-attention mechanism in Transformers into SNNs. By applying SIT-conversion to ECA-ATCNet, the performance of the converted model decreases by only 0.6% to 0.73%, while energy consumption is reduced by 52.84% to 53.52%. Due to the limited work on converting motor imagery classification models to SNNs through ANN-to-SNN conversion, a detailed comparative analysis of its performance in image classification tasks is conducted through SIT-conversion. The SIT-conversion is applied to the EdgeNeXt, Next-ViT, and UniFormer models on image datasets CIFAR10, CIFAR100, and ImageNet-1K. The conversion loss produced by SIT-conversion ranges from \(-0.05\%\) to \(+0.13\%\), with the EdgeNeXt model achieving a conversion loss of \(-0.24\%\) on the ImageNet-1K dataset, demonstrating near-lossless ANN-to-SNN conversion and proving the effectiveness and generalization ability of this method. Furthermore, the energy consumption of the converted model is reduced by 18.18% to 45.13%, significantly lowering the energy required during model inference. This will drive the development of SNN algorithms, bridge the gap between SNN and ANN models in practical applications, and play a crucial role in advancing the application of brain-machine interfaces in portable devices. While our SIT-Conversion method has significant advantages in reducing inference energy consumption and time steps (achieving a 12-step conversion with minimal accuracy loss), we acknowledge that the computational feasibility of SNN deployment ultimately depends on neuromorphic hardware support. Due to the limited availability of neuromorphic chips, current mainstream research on SNNs [42; 74; 75; 77] primarily relies on theoretical validation, and our report is consistent with these works. In future work, we plan to conduct experimental tests in conjunction with the advancements in neuromorphic chips, further bridging the gap between theoretical progress and practical deployment, and supplementing such deployment data. Conceptualization, X.L., Q.S. and Z.W. ; methodology, X.L., Q.S., H.W. and Z.W. ; software, H.W. ; validation, X.L., Q.S. and H.W. ; formal analysis, X.L. and Q.S. ; investigation, X.L., Q.S. and H.W. ; resources, Z.W. ; data curation, X.L. and H.W. ; writing--original draft preparation, X.L. and Q.S. ; writing--review and editing, X.L., Q.S. and Z.W. ; visualization, X.L., Q.S. and H.W. ; supervision, Z.W. ; project administration, Z.W. ; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript. This research is supported by National Natural Science Foundation of China (No. 62302090, 62477006, 62272097), Shanghai Sailing Program (No.23 YF1401100). Not applicable.