 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.
<clarity>  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.
 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 relations among different EEG channels . 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.