Paper Info
Title
A self-paced brain interface system that uses movement related potentials and changes in the power of brain rhythms.
Abstract
Movement execution results in the simultaneous generation of movement-related potentials (MRP) as well as changes in the power of Mu and Beta rhythms. This paper proposes a new self-paced multi-channel BI that combines features extracted from MRPs and from changes in the power of Mu and Beta rhythms. We developed a new algorithm to classify the high-dimensional feature space. It uses a two-stage multiple-classifier system (MCS). First, an MCS classifies each neurological phenomenon separately using the information extracted from specific EEG channels (EEG channels are selected by a genetic algorithm). In the second stage, another MCS combines the outputs of MCSs developed in the first stage. Analysis of the data of four able-bodied subjects shows the superior performance of the proposed algorithm compared with a scheme where the features were all combined in a single feature vector and then classified.
Year
DOI
Venue
2007
10.1007/s10827-006-0017-3
Journal of Computational Neuroscience
Keywords
Field
DocType
Self-paced brain interface systems,Multiple neurological phenomena,Movement-related potentials,Mu rhythms,Beta rhythms
Computer science,Movement-related potentials,Speech recognition,Beta Rhythm,Rhythm
Journal
Volume
Issue
ISSN
23
1
0929-5313
Citations 
PageRank 
References 
5
0.56
22
Authors
3
Name
Order
Citations
PageRank
Mehrdad Fatourechi116911.96
Gary E. Birch28211.36
Rabab K Ward31440135.88