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

arXiv: https://arxiv.org/abs/1703.08970 · HTML: https://mlsanity.com/paper/1703.08970/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2017.

## Built on by (most cited first, 7 of 7)

- [Imbalanced Big Data Oversampling: Taxonomy, Algorithms, Software, Guidelines and Future Directions](https://mlsanity.com/paper/2107.11508/) (2021)
- [Can Brain Signals Reveal Inner Alignment with Human Languages?](https://mlsanity.com/paper/2208.06348/) (2022)
- [G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification](https://mlsanity.com/paper/2101.11656/) (2021)
- [CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion](https://mlsanity.com/paper/2311.07788/) (2023)
- [SWIM: Short-Window CNN Integrated with Mamba for EEG-Based Auditory Spatial Attention Decoding](https://mlsanity.com/paper/2409.19884/) (2024)
- [DistilCLIP-EEG: Enhancing Epileptic Seizure Detection Through Multi-modal Learning and Knowledge Distillation](https://mlsanity.com/paper/2510.13497/) (2025)
- [Multimodal Classification: Current Landscape, Taxonomy and Future Directions](https://mlsanity.com/paper/2109.09020/) (2021)

## Abstract (arXiv)

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.