# Deep learning-based classification of fine hand movements from low frequency EEG

arXiv: https://arxiv.org/abs/2011.06791 · HTML: https://mlsanity.com/paper/2011.06791/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet](https://mlsanity.com/paper/2105.07917/) (2021)
- [vEEGNet: learning latent representations to reconstruct EEG raw data via variational autoencoders](https://mlsanity.com/paper/2312.09449/) (2023)
- [Motor imagery classification using EEG spectrograms](https://mlsanity.com/paper/2211.08350/) (2022)
- [ACTA: A Mobile-Health Solution for Integrated Nudge-Neurofeedback Training for Senior Citizens](https://mlsanity.com/paper/2102.08692/) (2021)
- [hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications](https://mlsanity.com/paper/2312.00799/) (2023)

## Abstract (arXiv)

The classification of different fine hand movements from EEG signals represents a relevant research challenge, e.g., in brain-computer interface applications for motor rehabilitation. Here, we analyzed two different datasets where fine hand movements (touch, grasp, palmar and lateral grasp) were performed in a self-paced modality. We trained and tested a newly proposed convolutional neural network (CNN), and we compared its classification performance into respect to two well-established machine learning models, namely, a shrinked-LDA and a Random Forest. Compared to previous literature, we took advantage of the knowledge of the neuroscience field, and we trained our CNN model on the so-called Movement Related Cortical Potentials (MRCPs)s. They are EEG amplitude modulations at low frequencies, i.e., (0.3, 3) Hz, that have been proved to encode several properties of the movements, e.g., type of grasp, force level and speed. We showed that CNN achieved good performance in both datasets and they were similar or superior to the baseline models. Also, compared to the baseline, our CNN requires a lighter and faster pre-processing procedure, paving the way for its possible use in an online modality, e.g., for many brain-computer interface applications.