Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.

ABSTRACT: Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. However, it ignores the three-dimensional spatial characteristics of EEG signals and cannot dynamically learn the interactions between nodes. To address this issue, a schizophrenia classification model based on a three-dimensional adaptive graph convolutional neural network (3D-AGCN) is proposed. Each subject's EEG data is divided into various segment lengths and frequency bands for the experiment. The attention mechanism is then used to integrate the node features in the spatial, feature, and frequency band dimensions. The resulting adaptive brain functional network features are then constructed and fed into the GAT + GCN model. This adaptive approach el

SUBMITTER: Yin G 

PROVIDER: S-EPMC11790975 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature