Multimodal Deep Learning Approach for Joint EEG-EMG Data Compression and Classification
Open accessAbstract
In this paper, we present a joint compression and classification approach of EEG and EMG signals using a deep learning approach. Specifically, we build our system based on the deep autoencoder architecture which is designed not only to extract discriminant features in the multimodal data representation but also to reconstruct the data from the latent representation using encoder-decoder layers. Since autoencoder can be seen as a compression approach, we extend it to handle multimodal data at the encoder layer, reconstructed and retrieved at the decoder layer. We show through experimental results, that exploiting both multimodal data intercorellation and intracorellation 1) Significantly reduces signal distortion particularly for high compression levels 2) Achieves better accuracy in classifying EEG and EMG signals recorded and labeled according to the sentiments of the volunteer.
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Stacked-autoencoder-based multimodal systems compress each modality separately in the encoder and build a shared latent representation in the decoder without supervision.
“the authors of [210–212] designed multimodal systems based on SAEs, where the encoder side of the architecture represents and compresses each unimodal feature separately, and the decoder side constructs the latent (shared) representation of the inputs in a unsupervised manner.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [212]
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Tasé, R. O. R., Delisle-Rodríguez, D., Samuel, O. W., & López-Delis, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89691-1_12
A Photoplethysmography Signal Compression Method of Extreme Learning Machine
Zhou, Y., An, A., Liu, J., & Chou, Y. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10239907
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Studying Attention in Virtual Reality with Electroencephalography and Eye-Tracking
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Gaussian-Enhanced CNN Autoencoder for Efficient EEG Signal Compression
Ali, ِ., & Ismael, M. R. (2026) · International Conference on Networks, Computers and Communications (ICNCCom) · doi:10.1109/icnccom69117.2026.11736250
- Authors
- Ben Said, A., Mohamed, A., Elfouly, T., Harras, K., & Wang, Z. J.
- Affiliations
- Qatar University (QA)
- Carnegie Mellon University Qatar (QA)
- University of British Columbia (CA)
- Venue
- 2017 IEEE Wireless Communications and Networking Conference (WCNC)
- Publisher
- IEEE
- Published
- 2017-03
- Pages
- 1-6
- ISSN
- 1525-3511 (ISSN-L)
- OpenAlex
- W2604843307
- Cited by
- 86
- References
- 23
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 6 · 2023: 12 · 2022: 13 · 2021: 13 · 2020: 11 · 2019: 9 · 2018: 14 · 2017: 1
- FWCI
- 10.98
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Wireless Communications and Networking Conference (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Qatar Foundation
- Fonds National de la Recherche Luxembourg
- Qatar National Research Fund (7-684-1-127)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- EEG, electromyography, joint classification, deep autoencoder, latent representations, signal reconstruction, signal distortion, sentiment classification, multimodal fusion
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