Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition

• URL: http://arxiv.org/abs/2406.16968v2
• Date: Wed, 26 Jun 2024 01:54:51 GMT
• Title: Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition
• Authors: Kai Shao, Rui Wang, Yixue Hao, Long Hu, Min Chen, Hans Arno Jacobsen,
• Abstract summary: Depression based on physiological signals such as functional near-infrared spectroscopy (NIRS) and electroencephalogram (EEG) has made considerable progress. In this paper, we introduce a multimodal physiological signals representation learning framework using architecture via multiscale contrasting for depression recognition (MRLM) To enhance the learning of semantic representation associated with stimulation tasks, a semantic contrast module is proposed.
• Score: 18.65975882665568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depression recognition based on physiological signals such as functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG) has made considerable progress. However, most existing studies ignore the complementarity and semantic consistency of multimodal physiological signals under the same stimulation task in complex spatio-temporal patterns. In this paper, we introduce a multimodal physiological signals representation learning framework using Siamese architecture via multiscale contrasting for depression recognition (MRLMC). First, fNIRS and EEG are transformed into different but correlated data based on a time-domain data augmentation strategy. Then, we design a spatio-temporal contrasting module to learn the representation of fNIRS and EEG through weight-sharing multiscale spatio-temporal convolution. Furthermore, to enhance the learning of semantic representation associated with stimulation tasks, a semantic consistency contrast module is proposed, aiming to maximize the semantic similarity of fNIRS and EEG. Extensive experiments on publicly available and self-collected multimodal physiological signals datasets indicate that MRLMC outperforms the state-of-the-art models. Moreover, our proposed framework is capable of transferring to multimodal time series downstream tasks.

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