title: Identification of motor imagery tasks through CC-LR algorithm in brain computer interface.
abstract: 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.
authors: Department of Mathematics and Computing, University of Southern Queensland, Toowoomba, Australia.
authors: Siuly
authors: Yan
authors: Y
authors: Li
authors: Peng
authors: P
authors: Wen
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D016000:Cluster Analysis; D016208:Databases, Factual; D007092:Imagination; D016018:Least-Squares Analysis; D016015:Logistic Models; D009048:Motor Skills; D010363:Pattern Recognition, Automated; D060388:Support Vector Machine