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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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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