Identification of motor imagery tasks through CC-LR algorithm in brain computer interface

This study focuses on the identification of Motor Imagery (MI) tasks for the development of Brain Computer Interface (BCI) technologies combining Cross-Correlation and Logistic Regression (CC-LR) techniques. The proposed method is tested on two benchmark data sets, IVa and IVb of BCI Competition III...

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Bibliographic Details
Published in:	International journal of bioinformatics research and applications Vol. 9; no. 2; p. 156
Main Authors:	Siuly, Li, Yan, Wen, Peng
Format:	Journal Article
Language:	English
Published:	Switzerland 2013
Subjects:	Algorithms Brain-Computer Interfaces Cluster Analysis Databases, Factual Imagination Least-Squares Analysis Logistic Models Motor Skills Pattern Recognition, Automated Support Vector Machine
ISSN:	1744-5485
Online Access:	Get more information
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Summary:	This study focuses on the identification of Motor Imagery (MI) tasks for the development of Brain Computer Interface (BCI) technologies combining Cross-Correlation and Logistic Regression (CC-LR) techniques. The proposed method is tested on two benchmark data sets, IVa and IVb of BCI Competition III, and the performance is evaluated through a 3-fold cross-validation procedure. The experimental outcomes are compared with two recently reported algorithms, R-Common Spatial Pattern (CSP) with aggregation and Clustering Technique (CT)-based Least Square Support Vector Machine (LS-SVM) and also other four algorithms using data set IVa. The results demonstrate that our proposed method results in an improvement of at least 3.47% compared with the existing methods tested.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1744-5485
DOI:	10.1504/IJBRA.2013.052447