Multimodal deep learning approach for joint EEG-EMG data compression and classification

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... (read more)

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METHOD TYPE
AutoEncoder
Generative Models