## 4 Experimental Results
### Performance Comparison of Other Method and WFBRCSP  
The description of similar competing methods compared to the WFBRCSP method proposed in this study is as follows. CSP: The most classic algorithm in the field of motor imagery EEG signal processing, which uses spatial filters to extract variance features and combines them with SVM for feature classification. RCSP: An improved version of the CSP algorithm, which introduces a regularization term and performs a linear combination of the covariance matrices of the target subject and source domain subjects. FBCSP: The EEG signal frequency band is divided into multiple sub-bands, and the CSP algorithm is used to extract features from these sub-bands, achieving first place on the BCICIV IIb dataset. This study uses the Fisher score for feature selection. FBRCSP: Combining the ideas of sub-band division and regularized CSP, parameter optimization is performed using 10-fold cross-validation. In this study, Fisher score feature selection and mutual information maximization feature selection are used for comparison of the results. OBRCSP: The transfer-learning-based optimized filter bank regularized common spatial pattern method [12] uses a low Laplacian spatial filter to enhance local brain region activity and improve the signal-to-noise ratio. After extracting features using a seventh-order Butterworth filter bank and regularized CSP algorithm, the best features are selected using the minimum redundancy maximum relevance (mRmR) algorithm. 1. Comparison of Accuracy Between WFBRCSP and Various Methods  
The classification accuracy of the WFBRCSP was compared with that of CSP, RCSP, FBCSP, FBRCSP-FSS, FBRCSP-MIFS, and OFBRCSP, as exhibited in Tables 1 and 2. Bold values indicate the highest accuracy and mean kappa value for each subject across methods. Figures 3 and 4 exhibit the confusion matrices for the two datasets on the test set. The experimental results demonstrate that the proposed WFBRCSP method achieved the highest average classification accuracy on both Dataset I and Dataset II, with accuracies of 87.87% and 85.92%, respectively. And WFBRCSP achieved the highest classification accuracy for each subject in Dataset II. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Subject** & **aa** & **al** & **av** & **aw** & **ay** & **Mean** & **Mean Kappa** \\ \hline CSP & 76.79 & **100** & 61.73 & 79.02 & 84.52 & 80.41 & 0.61 \\ RCSP & 76.79 & **100** & 66.84 & 77.23 & 87.70 & 81.71 & 0.63 \\ FRCSP & 87.50 & 98.21 & 68.37 & 75.89 & 80.16 & 82.03 & 0.64 \\ FBRCSP-FSS & 91.96 & 96.43 & 70.92 & 81.25 & **89.68** & 86.05 & 0.72 \\ FBRCSP-MIFS & 91.07 & 98.21 & 69.39 & 82.59 & 88.89 & 86.03 & 0.72 \\ OFBRCSP & 90.18 & 96.43 & **75.00** & 81.25 & 88.10 & 86.19 & 0.70 \\ WFBRCSP & **94.64** & **100** & 72.45 & **86.16** & 86.11 & **87.87** & **0.76** \\ (\(\beta\), \(\gamma\), \(N\)) & (0.5, 0.2, 2) & (0.9, 0, 20) & (0.6, 0.5, 20) & (0.2, 0, 18) & (0.1, 0, 4) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification accuracy of BCICIII IVa (%). The average Kappa values for each subject across different methods are provided in Tables 1 and 2. The Kappa value is used to measure the model's performance in task classification [27] and can effectively capture the performance differences of the model across subjects. Particularly in cross-subject tasks, where individual differences are significant, Kappa values provide a more accurate reflection of the model's stability. 2 Hyperparameter Settings  
Figure 5 exhibits the classification accuracy of subject \"aa\" in Dataset I with a feature selection size of \(N=2\) under different parameter sets. In the figure, the X-axis represents the \(\gamma\) value, and the Y-axis represents the \(\beta\) value. The brighter the color of the block, the higher the classification accuracy. The block marked with a star corresponds to the parameter set that yields the highest classification accuracy, with the regularization parameters \(\beta\) and \(\gamma\) and the feature selection size \(N\) set to (0.5, 0.2, 2).