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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Computational intelligence and neuroscience Jg. 2018; H. 2018; S. 1 - 9
Hauptverfasser:	Nguyen, Thinh, Ma, Yuliang, Chen, Kang, She, Qingshan, Zhang, Yingchun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cairo, Egypt Hindawi Publishing Corporation 01.01.2018 Hindawi John Wiley & Sons, Inc
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!