Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition

TL;DR

This paper introduces a novel multimodal physiological signals representation learning framework using multiscale contrasting, which improves depression recognition accuracy by leveraging the complementarity and semantic consistency of fNIRS and EEG signals.

Contribution

The paper proposes a multiscale contrasting framework with semantic consistency modules for better multimodal physiological signal representation learning in depression detection.

Findings

Outperforms state-of-the-art models on multiple datasets

Effective in capturing spatio-temporal features of signals

Capable of transferring to other multimodal time series tasks

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.