Multimodal mild depression recognition based on EEG-EM synchronization acquisition network
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In this paper, we used electroencephalography (EEG)-eye movement (EM) synchronization acquisition network to simultaneously record both EEG and EM physiological signals of the mild depression and normal controls during free viewing. Then, we consider a multimodal feature fusion method that can best discriminate between mild depression and normal control subjects as a step toward achieving our long-term aim of developing an objective and effective multimodal system that assists doctors during diagnosis and monitoring of mild depression. Based on the multimodal denoising autoencoder, we use two feature fusion strategies (feature fusion and hidden layer fusion) for fusion of the EEG and EM signals to improve the recognition performance of classifiers for mild depression. Our experimental results indicate that the EEG-EM synchronization acquisition network ensures that the recorded EM and EEG data require that both the data streams are synchronized with millisecond precision, and both fusion methods can improve the mild depression recognition accuracy, thus demonstrating the complementary nature of the modalities. Compared with the unimodal classification approach that uses only EEG or EM, the feature fusion method slightly improved the recognition accuracy by 1.88%, while the hidden layer fusion method significantly improved the classification rate by up to 7.36%. In particular, the highest classification accuracy achieved in this paper was 83.42%. These results indicate that the multimodal deep learning approaches with input data using a combination of EEG and EM signals are promising in achieving real-time monitoring and identification of mild depression.
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Multi-modal fusion of EEG and eye movement signals in hidden layers yields improved recognition accuracy over single-feature methods for mild depression.
“The application of deep learning to fuse these multi-modal data sets enables real-time monitoring and detection of mild depression, with the fusion approach in the hidden layers yielding improved recognition accuracy over single-feature methods, and showcasing the benefits of combining diverse physiological signals.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [47]
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- XIPING HU received the Ph.D. degree from The University of British Columbia, Vancouver, BC, Canada. He was the Co-Founder and the CTO of Bravolol, Ltd., Hong Kong, a leading language learning mobile application company with over 100 million users and listed as the top-two language education platform globally. He is cur- rently a Professor with the School of Informa- tion Science and Engineering, Lanzhou University, Lanzhou, China. He has authored or co-authored around 60 papers published and presented in prestigious conferences and journals, such as the IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING, IEEE INTERNET OF THINGS JOURNAL, ACM Transactions on Multimedia Computing, Communications, and Applications, IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, IEEE Com- munications Magazine, IEEE NETWORK, ACM MobiCom, and WWW. His current research interests include mobile cyber-physical systems, crowdsens- ing, social networks, and cloud computing.
- Dr. Hu has been serving as an Associate Editor for the IEEE ACCESS and a Lead Guest Editor for the IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING and Wireless Communications and Mobile Computing.
Cited by
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A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works
Multi-modality approaches for medical support systems: A systematic review of the last decade
Salvi, M., Loh, H. W., Seoni, S., Barua, P. D., García, S., Molinari, F., & Acharya, U. R. (2024) · Information Fusion · doi:10.1016/j.inffus.2023.102134 · Cited by 144 · see citing works
- Authors
- Zhu, J., Wang, Y., La, R., Zhan, J., Niu, J., Zeng, S., & Hu, X.
- Affiliations
- Lanzhou University (CN)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 28196-28210
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2919418213
- Cited by
- 56
- References
- 70
- Citations by year
- 2026: 3 · 2025: 15 · 2024: 11 · 2023: 13 · 2022: 8 · 2021: 5 · 2020: 1
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- Lanzhou University Institutional Repository (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61632014, 61210010, 61402211)
- National Basic Research Program of China (2014CB744600)
- Fundamental Research Funds for the Central Universities (lzujbky-2017-it74, lzujbky-2017-it75)
- International Cooperation Project of Ministry of Science and Technology (2013DFA11140)
- Beijing Municipal Science and Technology Commission (Z171100000117005)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- mild depression, depression detection, EEG, eye movements, multimodal fusion, denoising autoencoder, free viewing, depression diagnosis, real-time monitoring
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