Cited by 86Emotion and Mood RecognitionAdvanced Data Compression TechniquesEEG and Brain-Computer Interfaces

Multimodal Deep Learning Approach for Joint EEG-EMG Data Compression and Classification

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Ben Said, Ahmed, Mohamed, Amr, Elfouly, Tarek, Harras, Khaled, Wang, Z. Jane·2017·2017 IEEE Wireless Communications and Networking Conference (WCNC)

Abstract

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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Crossref · 10.1109/wcnc.2017.7925709

Cites

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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.”

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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
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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