Sun Chengyuan, Guan Mingjuan, Duan Keyu, Gao Shang, Chen Zhao
Institute of Artificial Intelligence, Anhui University of Science and Technology, No. 168, Taifeng Street, Huainan 232001, Anhui, People's Republic of China.
J Neural Eng. 2025 May 2;22(3). doi: 10.1088/1741-2552/adcfc8.
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.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.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.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.
重度抑郁症（MDD）是一种影响健康的广泛存在的精神障碍。许多将脑电图（EEG）与机器学习或深度学习相结合的方法已被提出，用于客观地区分MDD患者和健康个体。然而，目前大多数方法基于多通道EEG信号来检测抑郁症，这限制了其在日常生活中的应用。EEG获取的背景在研究设计和EEG设备设置方面可能会有所不同，并且可用的抑郁症EEG数据有限，这也可能会降低模型区分MDD和健康受试者的效能。为了解决上述挑战，我们提出了一种利用单通道EEG进行迁移学习的抑郁症检测模型。我们使用了一个预训练的ResNet152V2网络，并附加了一个展平层和一个全连接层。应用了特征提取方法，这意味着在训练过程中ResNet152V2中的所有层都被冻结，只有新添加层的参数是可调整的。鉴于深度神经网络在图像处理方面的优势，首先将EEG信号的时间序列转换为图像，将EEG信号分类问题转化为图像分类任务。随后，采用跨受试者实验策略进行模型训练和性能评估。该模型能够通过使用从有限数量受试者获得的单通道EEG样本精确地（准确率接近100%）识别其他个体的抑郁症。此外，该模型在四个公开可用的抑郁症EEG数据集上表现出卓越的性能，从而证明了其在应对由背景引起的EEG变化方面具有良好的适应性。这项研究不仅突出了深度迁移学习技术在EEG信号分析中令人印象深刻的潜力，还为未来促进相关精神障碍早期诊断的创新技术方法铺平了道路。
