Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods

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 ty...

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Bibliographic Details
Main Authors: Ikhtiyor Majidov, Taegkeun Whangbo
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Sensors
Subjects:
tangent space
Riemannian geometry
particle swarm optimization (PSO)
BCI
EEG
electro-oscillography (EOG)
CSP
FBCSP (filter bank common spatial pattern)
online learning
Online Access:https://www.mdpi.com/1424-8220/19/7/1736

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https://www.mdpi.com/1424-8220/19/7/1736

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