Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification

Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most of them cannot extract sufficient significant infor...

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Vydáno v:	Computational intelligence and neuroscience Ročník 2018; číslo 2018; s. 1 - 9
Hlavní autoři:	Nguyen, Thinh, Ma, Yuliang, Chen, Kang, She, Qingshan, Zhang, Yingchun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cairo, Egypt Hindawi Publishing Corporation 01.01.2018 Hindawi John Wiley & Sons, Inc
Témata:	Algorithms Analysis Brain Brain-Computer Interfaces Classification Competition Computer applications Datasets Datasets as Topic Dictionaries EEG Electroencephalography Evoked Potentials, Motor - physiology Human-computer interface Humans Image classification Imagery (Psychotherapy) - methods Implants Information processing Learning - physiology Learning algorithms Machine Learning Mathematical problems Mental task performance Motor skill learning Motors Neural coding Neural networks Neural Networks (Computer) Neurons Pattern recognition Representations Signal processing Spatial filtering
ISSN:	1687-5265, 1687-5273, 1687-5273
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