GNMixer: A High-Density EEG Signal Processing Method Using P 2GNN and MLP-Mixer for Depression Detection

Electroencephalogram (EEG), which uses electrodes as highly sensitive sensors to capture the brain’s electrical activity, is used to study depression. With the development of graph neural networks (GNNs), topological analysis based on high-density electrodes has become an important research direction in depression detection. However, effective capture of complex spatial relationships between electrodes, particularly long-range dependencies across brain regions, remains challenging. In this work, we propose a method called GNMixer, which enhances the capability of topology representation learning through a local and global information interaction strategy. Specifically, we divide the entire cerebral cortex into multiple subbrain regions, which are considered distinct patches. Inside each patch, we develop a brain patch encoder based on ${P}^{\,{2}}$ GNN to enhance the attention of local features. During the encoding process, we employ a precomputed approach to extract multichannel topological features. Outside the patches, we use the multilayer perceptron (MLP)-Mixer to capture interactions between brain regions and explore macroscopic connectivity patterns. In the mental-disorder analysis (MODMA) dataset, the accuracy of GNMixer reaches 93.12%. Our method promotes the advancement of high-density EEG sensor technology in depression detection.