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...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 23; no. 11; p. 5051
Main Authors:	Shuqfa, Zaid, Belkacem, Abdelkader Nasreddine, Lakas, Abderrahmane
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 25.05.2023 MDPI
Subjects:	Algorithms Brain research brain–computer interface (BCI) Classification Datasets Electrodes Electroencephalography electroencephalography/electroencephalogram (EEG) Geometry motor execution (ME) motor imagery (MI) multiclass classification Riemannian geometry decoding algorithm (RGDA) Wavelet transforms New Jersey brain–computer interface (BCI) motor execution (ME) Riemannian geometry decoding algorithm (RGDA) multiclass classification motor imagery (MI) electroencephalography/electroencephalogram (EEG)
ISSN:	1424-8220, 1424-8220
Online Access:	Get full text
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