The development of Brain-Computer-Interface (BCI) technology requires accurate decoding of brain activities measured by EEG. However, due to the non-stationary characteristics of EEG signals and intra- and inter-individual variability, it is not easy to construct a reliable and universal evaluation model for different subjects. In practical applications, most of the target domains are invisible, yet the current transfer learning models based on EEG signals mostly are target-domains-visable. To address this problem, this paper proposes a deep migration learning framework that reduces individual differences through intra-subject alignment and inter-subject alignment, extracts stable features after superposition averaging using a multi-scale spatio-temporal graph neural network, and employs a multi-source domain distribution normalization method, which enables the model to be effectively generalized to the target domain. The adaptive subject normalization layer introduced in the model gradually realizes the alignment of different source domain distributions during the training process, and executes the Test-Time-Adaptation (TTA) strategy in the testing phase to achieve dynamic adaptation to the target domain data. The experimental results show that the model outperforms traditional deep learning models on TMS-EEG data, while the ablation experiments verify the effectiveness of the subject-level normalization module in improving the model generalization ability.