Electroencephalography (EEG) motor imagery (MI) classification is fundamental to understanding the neural mechanisms underlying human movement and advancing brain-computer interfaces (BCI) applications. Deep learning based approaches have demonstrated exceptional proficiency in classifying EEG signals. However, their applications are often restricted by the large variation of signals between individuals, i.e., inter-subject variability. To mitigate this issue, some studies have employed task-specific (TS) EEG signals recorded from the target subject, thereby improving classification performance. Despite this progress, collecting TS EEG data remains a major limitation due to its time-consuming and labor-intensive process. Conversely, resting state (RS) EEG signals present a promising alternative, as they can be acquired more easily and contain rich subject information. In this paper, we propose a subject-adaptive learning approach using RS EEG signals within a meta-learning framework. The model learns to adapt to each subject using only their RS EEG signals for personalized EEG MI classification. Our learning framework consists of two iterative phases. In the subject-specific training phase, we fuse RS EEG signals with TS information while retaining individual subject characteristics and use the fused signals to adapt the model to the target subject. In the meta-training phase, the model predicts the MI class corresponding to the given TS EEG signals and computes the loss to update the meta-parameters for rapid target adaptation. Our method achieves an average accuracy improvement of 10.05% across two encoders and three benchmark datasets. Furthermore, visualization results show that the fused RS EEG signals combined with TS information exhibit characteristics similar to real TS EEG signals. These findings highlight the potential of leveraging RS EEG signals to advance practical BCI systems.
