Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods

Single-trial motor imagery classification is a crucial aspect of brain–computer applications. Therefore, it is necessary to extract and discriminate signal features involving motor imagery movements. Riemannian geometry-based feature extraction methods are effective when designing these types of mot...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 19; číslo 7; s. 1736
Hlavní autori:	Majidov, Ikhtiyor, Whangbo, Taegkeun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 11.04.2019 MDPI
Predmet:	Algorithms BCI Classification CSP EEG electro-oscillography (EOG) Electroencephalography FBCSP (filter bank common spatial pattern) Geometry Methods Neural networks Noise online learning particle swarm optimization (PSO) Riemannian geometry Signal processing tangent space Riemannian geometry CSP EEG FBCSP (filter bank common spatial pattern) BCI electro-oscillography (EOG) particle swarm optimization (PSO) tangent space online learning
ISSN:	1424-8220, 1424-8220
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