前学習済み特徴量と反復極座標注意機構を備えたグラフ畳み込みネットワークによる被験者間EEGベースの精神神経診断

Electroencephalography (EEG)-based diagnosis of neuropsychiatric disorders offers a non-invasive and cost-effective solution for early detection. However, robust cross-subject generalization remains a major challenge due to substantial inter-individual variability in EEG signals. To address this, we propose PreIPCA-GCN, a novel graph convolutional network that integrates pretrained temporal features and Iterative Polar Coordinate Attention (IPCA)-based brain connectivity modeling. Specifically, we utilize a modified version of LaBraM, a large-scale pretrained EEG model, to extract subject-invariant node representations. Functional brain connectivity is then characterized using Pearson correlation and cosine similarity in polar space, capturing both connectivity strength (radius) and phase synchronization (angle). To fuse these complementary cues, we introduce a dual-path IPCA mechanism, refining the adjacency matrix across iterations. PreIPCA-GCN is evaluated on six public EEG datasets covering five neuropsychiatric disorders (e.g., attention-deficit/hyperactivity disorder, Alzheimer's disease, schizophrenia), consistently demonstrating strong cross-subject accuracies under both hold-out (86.77%-95.69%) and leave-one-subject-out cross-validation (88.81%-97.49%). Comprehensive comparative results show that PreIPCA-GCN outperforms several state-of-the-art methods. Ablation studies further confirm the effectiveness of both the pretrained node features and IPCA-based fused adjacency matrix in improving cross-subject generalization. These findings suggest PreIPCA-GCN as a robust and generalizable framework for cross-subject EEG-based neuropsychiatric diagnosis, offering strong potential for future clinical applications.