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Title
Japanese:脳波から抑うつ状態を推定する機械学習モデルに関する研究 
English:A Study on Machine Learning Models for Estimating Depressive States from EEG 
Author
Japanese: 森重遥斗, 胡元寧, 中谷裕教, 八木透.  
English: Haruto Morishige, Yuanning Hu, Hironori Nakatani, Tohru Yagi.  
Language Japanese 
Journal/Book name
Japanese: 
English: 
Volume, Number, Page        
Published date Mar. 19, 2026 
Publisher
Japanese: 
English: 
Conference name
Japanese:医用・生体工学研究会 
English: 
Conference site
Japanese: 
English: 
Abstract Depression exists on a continuum, necessitating objective EEG-based markers for early detection. This study classified 43 subjects into three depression levels (BDI-II) using SVM and LSTM models trained on EEG data during IAPS stimulus presentation. While SVM accuracy hovered near the 33% chance level, Z-score normalization in the 100–500 ms post-stimulus window improved performance. LSTM achieved 36–39% accuracy, regardless of layer or unit adjustments. These findings suggest that specific time domains are critical for classification. Future research will optimize electrode selection and model architectures to enhance predictive accuracy for objective depression assessment.

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