Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 7; p. 1736
• Main Authors: Majidov, Ikhtiyor, Whangbo, Taegkeun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.04.2019; MDPI
• Subjects: 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

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...