In the field of brain–computer interfaces (BCI), the decoding of motor imagery EEG signals is significantly hindered by individual differences in EEG signals, which limits the generalization ability of decoding models. To address this challenge, this study proposes a mutual information weighted filter bank regularized common spatial pattern (WFBRCSP) algorithm. The algorithm divides the signal into multiple frequency bands, adaptively assigns subject weights based on the mutual information maximization criterion, and optimizes the covariance matrix with a regularization strategy, significantly improving the robustness of feature extraction. The results on the public BCI competition datasets BCICIII IVa and BCICIV IIb exhibit that the WFBRCSP outperforms traditional CSP, RCSP, FBCSP, FBRCSP, and OFBRCSP methods in terms of classification accuracy (87.87% and 85.92%). In addition, through the mutual information-weighted and regularized spatial filtering of data from different subjects, WFBRCSP demonstrates excellent real-time performance in cross-subject scenarios, validating its practical value in brain–computer interface systems. This study provides a new approach to addressing the issues of individual differences and noise interference in EEG signals.