Before revision:   In addition, we propose two regularizers, namely NodeDAT and EmotionDL , to improve the robustness of our model against cross-subject EEG variations and noisy labels during recording .
After revision:  Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in our graph neural network where the connection and sparseness of the adjacency matrix are supported by the neurosicience theories of human URLanization. In addition, we propose two regularizers, namely NodeDAT and EmotionDL , to improve the robustness of our model against cross-subject EEG variations and noisy labels during recording .