A depression detection approach leveraging transfer learning with single-channel EEG

• Published in: Journal of neural engineering Vol. 22; no. 3; pp. 36001 - 36018
• Main Authors: Sun, Chengyuan, Guan, Mingjuan, Duan, Keyu, Gao, Shang, Chen, Zhao
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.06.2025
• Subjects: Adult; Deep Learning; depression detection; Depressive Disorder, Major - diagnosis; Depressive Disorder, Major - physiopathology; Electroencephalography - methods; Female; Humans; Machine Learning; Male; Neural Networks, Computer; ResNet152V2; single-channel EEG; transfer learning; single-channel EEG; transfer learning; ResNet152V2; depression detection
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/adcfc8

Objective. Major depressive disorder (MDD) is a widespread mental disorder that affects health. Many methods combining electroencephalography (EEG) with machine learning or deep learning have been proposed to objectively distinguish between MDD and healthy individuals. However, most current methods detect depression based on multichannel EEG signals, which constrains its application in daily life. The context in which EEG is obtained can vary in terms of study designs and EEG equipment settings, and the available depression EEG data is limited, which could also potentially lessen the efficacy of the model in differentiating between MDD and healthy subjects. To solve the above challenges, a depression detection model leveraging transfer learning with the single-channel EEG is advanced. Approach. We utilized a pretrained ResNet152V2 network to which a flattening layer and dense layer were appended. The method of feature extraction was applied, meaning that all layers within ResNet152V2 were frozen and only the parameters of the newly added layers were adjustable during training. Given the superiority of deep neural networks in image processing, the temporal sequences of EEG signals are first converted into images, transforming the problem of EEG signal categorization into an image classification task. Subsequently, a cross-subject experimental strategy was adopted for model training and performance evaluation. Main results. The model was capable of precisely (approaching 100% accuracy) identifying depression in other individuals by employing single-channel EEG samples obtained from a limited number of subjects. Furthermore, the model exhibited superior performance across four publicly available depression EEG datasets, thereby demonstrating good adaptability in response to variations in EEG caused by the context. Significance. This research not only highlights the impressive potential of deep transfer learning techniques in EEG signal analysis but also paves the way for innovative technical approaches to facilitate early diagnosis of associated mental disorders in the future.