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

TL;DR

This study introduces a CNN model trained on low-frequency EEG signals to classify fine hand movements, demonstrating comparable or superior accuracy to traditional models and enabling faster, online BCI applications.

Contribution

A novel CNN approach trained on low-frequency EEG data for fine hand movement classification, with improved efficiency and potential for real-time BCI use.

Findings

CNN achieved high classification accuracy on two datasets.

Compared to baseline models, CNN required less pre-processing.

CNN's performance was comparable or better than traditional machine learning models.

Abstract

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.