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Dimensionality Reduction of MI-EEG Data via Convolutional Autoencoders with a Low Size Dataset

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Business Intelligence (CBI 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 449))

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Abstract

Electroencephalography has a poor spatial resolution, as experimental setups demand many electrodes around the motor cortex to reach the best results. Yet, it increases the data to be stored or transmitted in real-time for later uses. Thus, researchers have suggested autoencoders (AE) that transmit the compressed latent variable instead of the data itself. In this paper, we propose an AE and a Supervised Autoencoder (SupAE) designed for mobile applications treating Motor Imagery (MI). The introduced Encoder and Decoder derive from the previously published AMSI-EEGNet, a fast-to-train and lightweight architecture. The results found that the proposed methods perform better than baselines, especially for a high compression ratio (CR). Also, SupAE is a better option when the transmitted data needs classification. Further, we studied the evolution of the AE training and found that it learns similar features to previous studies.

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Authors and Affiliations

  1. LMCSA Laboratory, Faculty of Sciences and Technology, Hassan II University of Casablanca, Mohammedia, Morocco

    Mouad Riyad, Mohammed Khalil & Abdellah Adib

Authors
  1. Mouad Riyad
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  2. Mohammed Khalil
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  3. Abdellah Adib
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Correspondence to Mouad Riyad .

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Editors and Affiliations

  1. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Mohamed Fakir

  2. Sultan Moulay Slimane University, Beni Mellal, Morocco

    Mohamed Baslam

  3. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Rachid El Ayachi

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Riyad, M., Khalil, M., Adib, A. (2022). Dimensionality Reduction of MI-EEG Data via Convolutional Autoencoders with a Low Size Dataset. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2022. Lecture Notes in Business Information Processing, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-031-06458-6_22

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  • DOI: https://doi.org/10.1007/978-3-031-06458-6_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06457-9

  • Online ISBN: 978-3-031-06458-6

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