Attention-based multi-scale convolution and conformer for EEG-based depression detection

Attention-based multi-scale convolution and conformer for EEG-based depression detection

Depression is a common mental health issue, and early detection is crucial for timely intervention. This study proposes an end-to-end EEG-based depression recognition model, AMCCBDep, which combines Attention-based Multi-scale Parallel Convolution (AMPC), Conformer, and Bidirectional Gated Recurrent...

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Bibliographic Details
Main Authors: Ze Yan, Yumei Wan, Xin Pu, Xiaolin Han, Mingming Zhao, Haiyan Wu, Wentao Li, Xueying He, Yunshao Zheng
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-07-01
Series:Frontiers in Psychiatry
Subjects:
depression detection
electroencephalography (EEG)
attention
deep learning (DL)
AMCCBDep
Online Access:https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1584474/full
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https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1584474/full

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