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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Veröffentlicht in:International journal of bioinformatics research and applications Jg. 9; H. 2; S. 156
Hauptverfasser: Siuly, Li, Yan, Wen, Peng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland 2013
Schlagworte:
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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1744-5485
DOI:10.1504/IJBRA.2013.052447