Multimodal deep learning approach for Joint EEG-EMG Data compression and classification
Abstract
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. 2017 IEEE.
Collections
- Computer Science & Engineering [2382 items ]
Related items
Showing items related by title, author, creator and subject.
-
Adaptive compression and optimization for real-time energy-efficient wireless EEG monitoring systems
Hussein R.; Mohamed A.; Alghoniemy M. ( IEEE , 2013 , Conference Paper)Recent technological advances in wireless body sensor networks (WBSN) have made it possible for the development of innovative medical applications to improve health care and the quality of life. Electroencephalography ... -
An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System
Djelouat, Hamza; Baali, Hamza; Amira, Abbes; Bensaali, Faycal ( Hindawi Limited , 2017 , Article)The last decade has witnessed tremendous efforts to shape the Internet of things (IoT) platforms to be well suited for healthcare applications. These platforms are comprised of a network of wireless sensors to monitor ... -
Performance evaluation for compression-accuracy trade-off using compressive sensing for EEG-based epileptic seizure detection in wireless tele-monitoring
Abualsaud K.; Mahmuddin M.; Hussein R.; Mohamed A. ( IEEE , 2013 , Conference Paper)Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities. EEG signals capture important information pertinent to different ...