A novel method of motor imagery classification using eeg signal.
BCI
ELM
Electroencephalogram
Fisher’s linear discriminant
Principal component analysis
Journal
Artificial intelligence in medicine
ISSN: 1873-2860
Titre abrégé: Artif Intell Med
Pays: Netherlands
ID NLM: 8915031
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
received:
18
06
2019
revised:
05
11
2019
accepted:
30
12
2019
entrez:
8
3
2020
pubmed:
8
3
2020
medline:
26
1
2021
Statut:
ppublish
Résumé
A subject of extensive research interest in the Brain Computer Interfaces (BCIs) niche is motor imagery (MI), where users imagine limb movements to control the system. This interest is owed to the immense potential for its applicability in gaming, neuro-prosthetics and neuro-rehabilitation, where the user's thoughts of imagined movements need to be decoded. Electroencephalography (EEG) equipment is commonly used for keeping track of cerebrum movement in BCI systems. The EEG signals are recognized by feature extraction and classification. The current research proposes a Hybrid-KELM (Kernel Extreme Learning Machine) method based on PCA (Principal Component Analysis) and FLD (Fisher's Linear Discriminant) for MI BCI classification of EEG data. The performance and results of the method are demonstrated using BCI competition dataset III, and compared with those of contemporary methods. The proposed method generated an accuracy of 96.54%.
Identifiants
pubmed: 32143794
pii: S0933-3657(19)30481-6
doi: 10.1016/j.artmed.2019.101787
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
101787Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.