From Electroencephalogram Data to Brain Networks: Graph-Learning-Based Brain Disease Diagnosis
Ke Sun, Ciyuan Peng, Shuo Yu, Zhuoyang Han, Feng Xia
Abstract
Brain networks are built according to the structures or neural activities of different brain regions, which can be modeled as complex networks. Many studies exploit brains from the perspective of graph learning to diagnose the nerve diseases of brains. However, many of these algorithms are unable to automatically construct brain function topology based on electroencephalogram (EEG) and fail to capture the global features of multi-channel EEG signals for whole-graph embedding. To address these challenging issues, we propose an attention-based whole-graph learning model for the diagnosis of brain diseases, namely MAINS, which can adaptively construct brain functional topology from EEG signals and effectively embed multiple node features and the global structural features of brain networks into the whole-graph representations. We validated the model by conducting classification (diagnosis) experiments on real EEG datasets. Comprehensive experimental results demonstrate the superiority of the proposed approach over state-of-the-art methods.