Classification of imagery movement tasks for brain-computer interfaces using regression tree

UM > Faculty of Science and Technology > DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING

Residential College	false
Status	已發表Published
	Classification of imagery movement tasks for brain-computer interfaces using regression tree
	Wong C.; Wan F.
	2009
Conference Name	The Sixth International Symposium on Neural Networks (ISNN 2009)
Source Publication	Advances in Intelligent and Soft Computing
Volume	56
Pages	461-468
Conference Date	26th-29th May 2009
Conference Place	in Wuhan, China
Abstract	Classification of EEG (electroencephalographic) signals recorded during right and left motor imagery tasks is a technique for designing BCI (Braincomputer interfaces). In this paper, the regression tree is used to separate the right/left patterns that are extracted by ERD time courses. The regression tree is a statistical method to identify complex patterns without rigorous theoretical and distributional assumptions. The simulation result shows that the proposed BCI can provide satisfactory offline classification error rate and mutual information.
Keyword	Brain-computer Interface (Bci) Decision Tree Event-related Desynchronization (Erd) Motor Imagery
URL	View the original
Indexed By	SCIE
Language	英語English
Fulltext Access	View Full-Text via Scopus
Document Type	Conference paper
Collection	DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Corresponding Author	Wan F.
Affiliation	Universidade de Macau
First Author Affilication	University of Macau
Corresponding Author Affilication	University of Macau
Recommended Citation GB/T 7714	Wong C.,Wan F.. Classification of imagery movement tasks for brain-computer interfaces using regression tree[C], 2009, 461-468.
APA	Wong C.., & Wan F. (2009). Classification of imagery movement tasks for brain-computer interfaces using regression tree. Advances in Intelligent and Soft Computing, 56, 461-468.

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