## 1 Introduction  
Motor imagery (MI)-based brain-computer interface (BCI) technology is an advanced research field in neurocognition and clinical rehabilitation medicine [1]. Its core goal is to decode motor imagery EEG signals (electroencephalogram, EEG) and directly translate the user's intention into mechanical responses, providing assistive-control solutions for patients with motor dysfunction [2]. The event-related desynchronization (ERD)/event-related synchronization (ERS) induced by motor imagery tasks in EEG are often considered as features for BCI systems to recognize users' movement intentions [3]. However, these features exhibit significant individual differences: their signal amplitude, frequency band range, and cortical spatial distribution characteristics are all influenced by the subject's physiological state and experimental conditions [4, 5]. The common spatial pattern (CSP) algorithm is widely used for EEG feature extraction [6] and can effectively extract spatiotemporal features related to ERD/ERS. Its computational complexity is relatively low, making it especially suitable for online BCI systems. The filter bank common spatial pattern (FBCSP) algorithm extends CSP by adding frequency band division [7], addressing the issue that CSP's pre-filtering strategy may not adapt to the subject's individualized frequency domain features, thereby improving feature robustness. The regularized common spatial pattern (RCSP) introduces a regularization term [8] to address the problem of noise sensitivity in CSP, which can lead to model overfitting, thus enhancing performance on small sample datasets. Although these methods are characterized by their suitability for binary classification tasks and high computational efficiency, they still suffer from insufficient generalization ability. In recent years, transfer-learning methods [9], through cross-subject or cross-task knowledge transfer, have become an effective way to enhance model generalization, receiving widespread attention. For motor imagery EEG signal decoding, some representative transfer-learning strategies include (1) a feature domain adaptation strategy, which aligns the EEG feature distributions of different subjects or tasks to reduce domain differences [10]; (2) model-based transfer-learning, which adapts to new subjects by fine-tuning pre-trained models or sharing model parameters [11]; and (3) a data augmentation strategy, which improves model generalization by synthesizing or transforming data [12]. Based on the FBCSP and RCSP methods, this study proposes a transfer strategy for cross-subject feature space alignment, named weighted filter bank and regularization common spatial pattern (WFBRCSP). WFBRCSP divides the EEG signals of the target subject and each source domain subject into sub-bands using a filter bank. It utilizes mutual information to quantify the similarity of EEG signal distributions within each frequency band between the target subject and the source domain subjects. Then, based on the total mutual information between the source domain subjects and the target subject across all frequency bands, it allocates weights to the sub-band EEG signals of each source domain subject. The weights are determined according to the proportion of their total mutual information. This approach achieves cross-subject feature space alignment. On this basis, the RCSP algorithm is used to extract spatiotemporal features from the weighted sub-bands for both the target subject and each source domain subject. Compared to traditional FBCSP and RCSP, WFBRCSP introduces a regularization term through the weighted filter bank and feature extraction process. This makes full use of the data from source domain subjects, which is similar to the target subject's distribution. This approach addresses the issue of adapting to the individualized frequency domain features while being sensitive to noise, which could lead to model overfitting.