Major depressive disorder detection via temporal-frequency-spatial transformer with sub-domain knowledge alignment using EEG

doi:10.1016/j.neunet.2025.107965

	Major depressive disorder detection via temporal-frequency-spatial transformer with sub-domain knowledge alignment using EEG
	Xu, Chen-Yang 1; Fan, Fei-Yi 2; Zhao, Li-Xuan 1; Jin, Li-Cheng 1; Zhang, Yong-Hui 3; Meng, Qing-Hao 1
	2026
发表期刊	NEURAL NETWORKS
ISSN	0893-6080
卷号	193 页码:12
摘要	Major Depressive Disorder (MDD) is a common mental illness that seriously jeopardizes the physical and mental health of patients. Accurate detection of MDD is crucial for treatment. Currently, there are significant differences in the EEG signals of each MDD patient, leading to lower accuracy of cross-subject MDD detection. Transformer-based methods have been used by scholars to detect MDD using electroencephalogram (EEG) data, but these methods often neglect the frequency features, focusing primarily on global domain adaptation (DA) while ignoring sub-domain alignment, resulting in loss of fine-grained discriminative information. To address this, we incorporate fine-grained frequency features to improve sub-domain alignment in DA rather than relying solely on global feature alignment. Building on the above analysis, we propose the TFST-SDKA model, a temporal-frequency-spatial Transformer (TFST) integrated with a sub-domain knowledge alignment (SDKA) method for MDD detection. The SDKA module classifies subjects into distinct sub-domains based on their labels by extracting fine-grained discriminative information from each subject. This process helps bridge the gap between source and target domains, enhancing the model's generalization. In addition, we propose a frequency attention (FA) mechanism, which uses discrete cosine transform (DCT) to convert EEG feature maps into the frequency domain. The FA extracts multiple frequency information of EEG signals associated with MDD and combines these frequency data to enhance the model's representational capability. As a result, the TFST-SDKA model improves EEG feature representation and aligns source and target domain features. Extensive experiments conducted on the MODMA and PRED+CT datasets demonstrate that our proposed TFST-SDKA model outperforms state-of-the-art (SOTA) methods in MDD detection tasks. Specifically, our method exceeds the SOTA methods by 1.42 % on the MODMA dataset and 1.16% on the PRED+CT dataset in terms of accuracy.
关键词	MDD detection Sub-domain knowledge alignment (SDKA) Temporal-frequency-spatial transformer (TFST) Frequency attention (FA)
DOI	10.1016/j.neunet.2025.107965
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62203321] ; China Postdoctoral Science Foundation[2021M692390]
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:001561923800004
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41733
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Meng, Qing-Hao
作者单位	1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Tianjin Anding Hosp, Dept Psychiat, Tianjin 300222, Peoples R China
推荐引用方式 GB/T 7714	Xu, Chen-Yang,Fan, Fei-Yi,Zhao, Li-Xuan,et al. Major depressive disorder detection via temporal-frequency-spatial transformer with sub-domain knowledge alignment using EEG[J]. NEURAL NETWORKS,2026,193:12.
APA	Xu, Chen-Yang,Fan, Fei-Yi,Zhao, Li-Xuan,Jin, Li-Cheng,Zhang, Yong-Hui,&Meng, Qing-Hao.(2026).Major depressive disorder detection via temporal-frequency-spatial transformer with sub-domain knowledge alignment using EEG.NEURAL NETWORKS,193,12.
MLA	Xu, Chen-Yang,et al."Major depressive disorder detection via temporal-frequency-spatial transformer with sub-domain knowledge alignment using EEG".NEURAL NETWORKS 193(2026):12.