Before revision:  In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition , which is biologically supported and captures both local and global inter-channel relations . 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.
After revision:  In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition . RGNN considers the biological topology among different brain regions to capture both local and global inter-channel relations . 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.