Multimodal deep learning approach for joint EEG-EMG data compression and classification
This work addresses multimodal biomedical signal processing for applications like emotion recognition, but it appears incremental as it extends existing autoencoder methods to handle multimodal data.
The paper tackled joint compression and classification of EEG and EMG signals using a multimodal deep autoencoder, resulting in reduced signal distortion at high compression levels and improved accuracy in classifying signals based on volunteer sentiments.
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.