Summary of Multimodal Physiological Signals Representation Learning Via Multiscale Contrasting For Depression Recognition, by Kai Shao et al.
Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition
by Kai Shao, Rui Wang, Yixue Hao, Long Hu, Min Chen, Hans Arno Jacobsen
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel multimodal physiological signals representation learning framework, dubbed MRLMC, has been developed for depression recognition using functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG). By leveraging Siamese architecture via multiscale contrasting, the proposed approach learns a robust representation of fNIRS and EEG signals. This is achieved through a time-domain data augmentation strategy, spatio-temporal contrasting module, and semantic consistency contrast module. Experimental results on publicly available and self-collected datasets demonstrate that MRLMC outperforms existing state-of-the-art models, with potential applications in multimodal time series downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Depression recognition from brain signals is a growing field. A new way to do this uses multiple types of brain signal data together. The method, called MRLMC, is good at recognizing depression because it learns from the patterns in the data. It does this by looking at how the different brain signals change over time and space. This helps the model learn what’s important for recognizing depression. The results show that MRLMC is better than other methods at doing this. |
Keywords
* Artificial intelligence * Data augmentation * Representation learning * Time series
