Multimodal depression detection based on an attention graph convolution and transformer

Multimodal depression detection based on an attention graph convolution and transformer

Traditional depression detection methods typically rely on single-modal data, but these approaches are limited by individual differences, noise interference, and emotional fluctuations. To address the low accuracy in single-modal depression detection and the poor fusion of multimodal features from e...

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Bibliographic Details
Main Authors: Xiaowen Jia, Jingxia Chen, Kexin Liu, Qian Wang, Jialing He
Format: Article
Language:English
Published: AIMS Press 2025-02-01
Series:Mathematical Biosciences and Engineering
Subjects:
eeg signals
speech signals
graph convolutional network
decision-level fusion
multimodal depression
multi-head attention
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2025024
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