## 6 Conclusions
The experimental results exhibit that WFBRCSP achieves average classification accuracies of 87.87% and 85.92% on two public datasets (BCICIII IVa and BCICIV IIb), which is an improvement of approximately 7-13 percentage points compared to the traditional CSP. In addition, the WFBRCSP achieves an AUC value of 0.96 (for subject \"aa\") in cross-subject scenarios, and the steeper ROC curve further validates its strong adaptability to individual differences. Although the training time of the WFBRCSP is relatively long, its testing time is short, making it suitable for real-time BCI systems with offline training and online testing. This method has some areas for improvement. In the future, it can be extended to multi-class motor imagery tasks, such as hand, foot, and tongue movements, while maintaining high decoding performance. However, the computational complexity of multi-class tasks will increase, particularly in the design of spatial filters and classifiers. Specifically, spatial filters need to be computed for each task, and classifiers such as SVM will require more support vectors and training samples to accurately define decision boundaries, thereby increasing the computational cost of the model. Future research could evaluate the performance of the WFBRCSP algorithm across a broader range of EEG tasks, assessing its adaptability to brain activity associated with different tasks, such as evoked potentials and cognitive tasks. By expanding the scope of evaluation, the potential for broader applications of the algorithm in the field of brain-computer interfaces (BCI) can be explored, thereby enhancing its practical applicability and robustness. This study integrates transfer-learning with adaptive regularization strategies, providing a theoretical reference for the design of EEG-decoding algorithms and laying a technical foundation for their application in medical rehabilitation, neuroengineering, and other fields. [PERSON] supervised the research, analyzed the results, and provided feedback. [PERSON] implemented the research, executed the experimental work, analyzed the results, and revised the final article. [PERSON] revised the draft and approved the final version of the article. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China (Grant No. 62466006) and grants from the Natural Science Foundation of Guangxi Province of China (Grant No. 2025 GXNSFAA069626), as well as the Guangxi Science and Technology Base and Talent Program (Approval No. 2022 AC21054). Not applicable. The data presented in this study are openly available in the BCI Competition III IVa, [[https://www.bbci.de/competition/iii/](https://www.bbci.de/competition/iii/)]([https://www.bbci.de/competition/iii/](https://www.bbci.de/competition/iii/)), accessed on 6 April 2025 [25], and BCI Competition IV IIb, [[https://www.bbci.de/competition/iv/](https://www.bbci.de/competition/iv/)]([https://www.bbci.de/competition/iv/](https://www.bbci.de/competition/iv/)), accessed on 6 April 2024 [26]. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
WFBRCSPWeighted Filter Bank and Regularization Common Spatial Pattern BCIBrain-Computer Interface MIMotor Imagery EEGElectroencephalogram ERDEvent-Related Desynchronization