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Multimodal deep learning approach for joint EEG-EMG data compression and classification
[article]
2017
arXiv
pre-print
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
arXiv:1703.08970v1
fatcat:sxfjktupxbdy5cj5azzwfoapxa