## 6 Conclusions  
This study proposes an EEG decoding algorithm based on a weighted filter bank regularized common spatial pattern (WFBRCSP) to address the issues of individual differences in EEG signals and noise interference. The limitations of the traditional model are improved through the following methods:  
1. Multi-band filtering and mutual information weighting:  
The EEG signals are divided into nine sub-bands, and the correlation between the target subject and source domain subjects is calculated based on the mutual information maximization criterion. Adaptive weight allocation is performed to reduce the negative impact of low-correlation data;
2. Regularized covariance matrix optimization:  
A regularization parameter is introduced to merge the covariance matrices of the target subject and the source domain subjects, enhancing the feature stability under small sample sizes and non-stationary data; \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Main Contributions** & **Limitations** & **Innovations of WFBRCSP** \\ \hline \multirow{5}{*}{
\begin{tabular}{c} [PERSON] \\ et al. [15] \\ \end{tabular} } & Proposed a KL divergence-based & Only post-extraction feature-level & Introduces mutual \\  & transfer-learning method that & adaptation is performed, without & information-guided weighting \\  & weights features and filters samples & addressing information loss at the & during signal and spatial filtering, \\  & using SVM probabilities to improve & signal or spatial filter stage. & reducing feature loss and enhancing \\  & cross-session performance. & cross-subject generalization. & cross-subject generalization. \\  & Developed a cross-dataset domain & Integrates mutual information \\  & selection framework using MMD & Feature extraction and classification & weighting during feature extraction \\  & for source sample selection, & are independent; reliance on linear & and uses RBF-kernel SVM with \\  & Euclidean alignment, and & separability limits handling of & cross-validation, improving \\  & multi-combination CSP features & complex distributions. & adaptability to complex \\  & with linear SVM. & cross-subject data. & cross-subject generalization. \\  & Proposed a multi-source & Relies on traditional CSP features & Combines weighted filter banks \\  & transfer-learning framework based & without modeling inter-subject & and regularized CSP, introducing \\  & on resting-state alignment and & variability during feature extraction, & weighting to better capture \\  & improved EasyTL. & limiting alignment precision. & inter-subject differences. \\  & Constructs the WFBRCSP & Under multi-subject conditions, the & The method directly operates on \\  & framework by integrating mutual & mutual information calculation & EEG signals and spatial filters, \\  & information-based subject & during the training phase increases & enhancing interpretability and \\  & weighting, filter bank & computational overhead. When & reducing feature loss. Mutual \\  & decomposition, and regularized & extended to multi-class problems, & information-based subject \\  & CSP for adaptive cross-subject & the design of spatial filters and & weighting improves cross-subject \\  & feature alignment. Applies Fisher & classifiers may incur higher & robustness, while Fisher score and \\  & score for feature selection and & complutational complexity. & RBF-SVM ensure a balance between \\  & RBF-kernel SVM for classification. & computational complexity. & accuracy and efficiency. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the main contributions of this paper with existing related work. Efficient feature selection and classification:  
The Fisher score is used to select highly discriminative features, and an SVM classifier is employed to achieve efficient decoding.