## 2 Related Works  
Due to the individual differences in EEG signals and the scarcity of data, the application of transfer-learning in EEG signal processing has gradually become an important research direction. [PERSON] et al. [13] proposed a transfer-learning-based method for motor intention recognition, targeting the challenge of limited data availability in exoskeleton-assisted rehabilitation training. The method involves recording EEG signals from healthy subjects performing bilateral synchronous movements under an exoskeleton mirror mode to train a classifier, which is then transferred to recognize unilateral limb motor intentions, thereby eliminating the need for additional calibration sessions during rehabilitation. To accommodate the small-sample condition, the authors employed a support vector machine (SVM) to classify extracted temporal features between movement-related potentials and resting states, enhancing the feasibility of the system under data-constrained environments. [PERSON] et al. [14] proposed a transfer-learning domain adaptation algorithm based on an improved Kullback-Leibler divergence to address the issue of feature distribution drift caused by the non-stationarity of EEG signals in motor imagery BCI. In this method, KL divergence is employed as a metric to measure the similarity between the feature distributions of the training and testing datasets. Additionally, the classification probabilities output by an SVM are utilized to both weight the covariance matrices of the training samples and to identify and remove the samples with poor classification performance, thereby effectively enhancing the model's classification accuracy on cross-session testing datasets. [PERSON] et al. [15] proposed a novel cross-dataset adaptive domain selection transfer-learning framework. Based on the maximum mean discrepancy criterion, source samples most similar to the target subject's distribution were selected from a large-scale dataset comprising 109 subjects, after excluding BCI-illiterate individuals (subjects with classification accuracies below 70%). Subsequently, Euclidean alignment and a multi-combination common spatial pattern (MCCSP) method were employed for discriminative feature extraction, followed by classification using a linear kernel SVM. The experimental results demonstrated that the proposed method outperformed advanced approaches such as EA-CSP, FBCSP, and EA-IISCSP in cross-dataset motor imagery BCI tasks, with statistically significant improvements (\(p<0.05\)). [PERSON] et al. [16] addressed the challenges of inter-subject variability and calibration burden in motor imagery EEG-based BCIs by proposing a multi-source domain transfer-learning fusion framework. The framework aligns source domains using resting-state data, integrates feature extraction via CSP, and employs an improved EasyTL classifier with optimal transport and density peak selection strategies to enhance cross-subject classification performance. Considering the limitations of deep-learning methods in terms of data requirements, computational cost, and interpretability, the authors adopted a lightweight design that combines traditional machine learning with optimal transport, achieving a balance between classification performance, resource consumption, and system deployability. [PERSON] et al. [17] proposed a novel transfer-learning strategy named MDTL, which not only enables transfer from a single source domain to a target domain but also facilitates data transmission among multiple source domains, thereby enhancing data selectivity and mitigating noise effects. As a model-agnostic approach, MDTL can be rapidly integrated into existing deep-learning models without requiring modifications to the network architecture. The experimental results demonstrated that MDTL improved the average classification accuracy on two datasets and reduced the training time by 93.4%.