Cargando…

Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets

The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain–computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise and nonstationarity of electroencephalography...

Descripción completa

Detalles Bibliográficos
Autores principales: Shuqfa, Zaid, Belkacem, Abdelkader Nasreddine, Lakas, Abderrahmane
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255410/
https://www.ncbi.nlm.nih.gov/pubmed/37299779
http://dx.doi.org/10.3390/s23115051
_version_ 1785056865166557184
author Shuqfa, Zaid
Belkacem, Abdelkader Nasreddine
Lakas, Abderrahmane
author_facet Shuqfa, Zaid
Belkacem, Abdelkader Nasreddine
Lakas, Abderrahmane
author_sort Shuqfa, Zaid
collection PubMed
description The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain–computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise and nonstationarity of electroencephalography signals. However, the related literature shows high classification accuracy on only relatively small BCI datasets. The aim of this paper is to provide a study of the performance of a novel implementation of the Riemannian geometry decoding algorithm using large BCI datasets. In this study, we apply several Riemannian geometry decoding algorithms on a large offline dataset using four adaptation strategies: baseline, rebias, supervised, and unsupervised. Each of these adaptation strategies is applied in motor execution and motor imagery for both scenarios 64 electrodes and 29 electrodes. The dataset is composed of four-class bilateral and unilateral motor imagery and motor execution of 109 subjects. We run several classification experiments and the results show that the best classification accuracy is obtained for the scenario where the baseline minimum distance to Riemannian mean has been used. The mean accuracy values up to 81.5% for motor execution, and up to 76.4% for motor imagery. The accurate classification of EEG trials helps to realize successful BCI applications that allow effective control of devices.
format Online
Article
Text
id pubmed-10255410
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-102554102023-06-10 Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets Shuqfa, Zaid Belkacem, Abdelkader Nasreddine Lakas, Abderrahmane Sensors (Basel) Article The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain–computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise and nonstationarity of electroencephalography signals. However, the related literature shows high classification accuracy on only relatively small BCI datasets. The aim of this paper is to provide a study of the performance of a novel implementation of the Riemannian geometry decoding algorithm using large BCI datasets. In this study, we apply several Riemannian geometry decoding algorithms on a large offline dataset using four adaptation strategies: baseline, rebias, supervised, and unsupervised. Each of these adaptation strategies is applied in motor execution and motor imagery for both scenarios 64 electrodes and 29 electrodes. The dataset is composed of four-class bilateral and unilateral motor imagery and motor execution of 109 subjects. We run several classification experiments and the results show that the best classification accuracy is obtained for the scenario where the baseline minimum distance to Riemannian mean has been used. The mean accuracy values up to 81.5% for motor execution, and up to 76.4% for motor imagery. The accurate classification of EEG trials helps to realize successful BCI applications that allow effective control of devices. MDPI 2023-05-25 /pmc/articles/PMC10255410/ /pubmed/37299779 http://dx.doi.org/10.3390/s23115051 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shuqfa, Zaid
Belkacem, Abdelkader Nasreddine
Lakas, Abderrahmane
Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets
title Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets
title_full Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets
title_fullStr Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets
title_full_unstemmed Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets
title_short Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets
title_sort decoding multi-class motor imagery and motor execution tasks using riemannian geometry algorithms on large eeg datasets
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255410/
https://www.ncbi.nlm.nih.gov/pubmed/37299779
http://dx.doi.org/10.3390/s23115051
work_keys_str_mv AT shuqfazaid decodingmulticlassmotorimageryandmotorexecutiontasksusingriemanniangeometryalgorithmsonlargeeegdatasets
AT belkacemabdelkadernasreddine decodingmulticlassmotorimageryandmotorexecutiontasksusingriemanniangeometryalgorithmsonlargeeegdatasets
AT lakasabderrahmane decodingmulticlassmotorimageryandmotorexecutiontasksusingriemanniangeometryalgorithmsonlargeeegdatasets