Deep Learning-Based Classification of Fine Hand Movements from Low Frequency EEG

Giulia Bressan,Gernot R. Müller-Putz,Giulia Cisotto,Selina Christin Wriessnegger

doi:10.3390/fi13050103

Abstract

The classification of different fine hand movements from electroencephalogram (EEG) signals represents a relevant research challenge, e.g., in BCI 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 CNN, and we compared its classification performance with two well-established machine learning models, namely, shrinkage-linear discriminant analysis (LDA) and Random Forest (RF). Compared to previous literature, we included neuroscientific evidence, and we trained our Convolutional Neural Network (CNN) model on the so-called movement-related cortical potentials (MRCPs). 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 (accuracy of 0.70±0.11 and 0.64±0.10, for the two datasets, respectively), and they were similar or superior to the baseline models (accuracy of 0.68±0.10 and 0.62±0.07 with sLDA; accuracy of 0.70±0.15 and 0.61±0.07 with RF, with comparable performance in precision and recall). In addition, compared to the baseline, our CNN requires a faster pre-processing procedure, paving the way for its possible use in online BCI applications.

Highlights

Published: 21 April 2021Several BCI systems for motor rehabilitation or motor control [1,2,3,4,5,6] and other basic neuroscience studies strongly rely on the ability to precisely and effectively distinguish different fine hand movements
We observed that a difference in the Movement-related cortical potentials (MRCPs) peak amplitude was especially noticeable at the EEG electrodes located in the contralateral side of the movement and that this spatial pattern is consistent across several participants, in line with other literature [43]
For Shrinkage linear discriminant analysis (sLDA) and Random Forest (RF), we considered all possible choices of the sliding window length, with the best window time location, for each participant

Summary

Introduction

Published: 21 April 2021Several BCI systems for motor rehabilitation or motor control [1,2,3,4,5,6] and other basic neuroscience studies strongly rely on the ability to precisely and effectively distinguish different fine hand movements. Reports that the components of the MRCPs can be influenced by several factors, such as the preparatory state (self-paced or cue-based), the level of intention [12], the type of movement, the praxis, and the previous experience of the same movement. It has been found that MRCPs can encode several properties of the movements, such as the type of grasp [13], the force level [14], and the speed of the task [15]. MRCPs have been previously employed to discriminate hand movements in patients with severe manual impairments [5]. For this reason, MRCPs are considered valid signals to Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations

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