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

Giulia Bressan, Giulia Cisotto, Gernot R. Müller-Putz and Selina Christin Wriessnegger
Additional contact information
Giulia Bressan: Department of Information Engineering, University of Padova, 35122 Padova, Italy
Giulia Cisotto: Department of Information Engineering, University of Padova, 35122 Padova, Italy
Gernot R. Müller-Putz: Institute of Neural Engineering, Graz University of Technology, 8010 Graz, Austria
Selina Christin Wriessnegger: Institute of Neural Engineering, Graz University of Technology, 8010 Graz, Austria

Future Internet, 2021, vol. 13, issue 5, 1-14

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.

Keywords: EEG; MRCP; CNN; RF; LDA; BCI; hand; grasping; palmar grasp; lateral grasp (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1999-5903/13/5/103/pdf (application/pdf)
https://www.mdpi.com/1999-5903/13/5/103/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:13:y:2021:i:5:p:103-:d:540267

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().