## 5 Discussion  
Based on the experimental results of the WFBRCSP, the following section will provide an in-depth discussion of these results. As exhibited in Tables 1 and 2, the CSP performed the worst in classification performance on both datasets, with average classification accuracies of 80.41% and 72.87%, respectively. The RCSP algorithm overcomes the limitations of the CSP algorithm in terms of overfitting, noise sensitivity, small sample sizes, and non-stationary data by introducing a regularization term, thus achieving better classification results than CSP. Due to the subject-specific nature of EEG signals, when using fixed frequency bands to filter EEG signals, the classification results of the CSP vary significantly between subjects. The FBCSP extracts CSP features from EEG signals across multiple sub-bands, addressing the dependency on frequency ranges, and therefore, its classification performance is superior to that of CSP. The FBRCSP algorithm demonstrates the advantages of the RCSP while also incorporating the benefits of FBCSP through the multi-filter bank structure, further improving its classification performance. The OFBRCSP uses a low Laplacian spatial filter, which calculates the weighted potential difference between the current electrode and neighboring electrodes to enhance local activity. It achieved a good average classification accuracy on BCICIII IVa, but performed poorly on BCICIV IIb, a dataset with fewer electrodes (C3, CZ, and C4). The effectiveness of the filter highly depends on the number and distribution of neighboring electrodes, and too few neighboring electrodes can lead to unbalanced weight distribution. As mentioned earlier, the subject-specific nature of EEG signals means that, in the RCSP algorithm, each source domain subject adjusts the covariance matrix with the same weight, without considering the correlation between subjects. The WFBRCSP introduces a regularization term based on subject data weights, using mutual information between subjects, to reconstruct the sample covariance matrix and adaptively balance the contribution of different subject data. This results in the highest average classification accuracies, with 87.87% and 85.92%, respectively. The average Kappa values of the WFBRCSP method for the two datasets are 0.76 and 0.72, respectively, the highest among all compared methods. This indicates that the proposed method excels in handling individual differences across  
Figure 7: Subject “aa” of BCICIII IVa ROC curves and AUC values for various method. subjects and ensuring stability in cross-subject tasks, further validating the effectiveness of WFBRCSP for motor imagery EEG signal decoding tasks. Figure 5 provides an intuitive visual reference, illustrating the classification performance of subject \"aa\" under different regularization parameter settings. The experimental results show that subject \"aa\" is significantly influenced by parameter selection, with substantial variations in classification accuracy across different parameter configurations. When \(\gamma\) = 0.2, subject \"aa\" achieved a relatively high classification accuracy. However, as the \(\gamma\) increased to 0.3, the classification accuracy began to decline, indicating that subject \"aa\" performs better in suppressing variance than in suppressing bias. This further emphasizes the importance of parameter selection. In the WFBRCSP method, the use of SVM with ten-fold cross-validation provides a more reliable performance assessment. The parameter set selected by subject \"aa\" with a feature selection size \(N\) = 2 yielded the highest classification accuracy, further validating the effectiveness of the SVM for this task. As exhibited in Figure 6, although the CSP algorithm demonstrates limited classification performance, it maintains high computational efficiency, primarily dominated by the eigendecomposition of the covariance matrices \(O(T^{3})\), with its space complexity determined by the storage requirements for covariance matrices [28].