PMID- 23467061 OWN - NLM STAT- MEDLINE DCOM- 20140310 LR - 20190805 IS - 1744-5485 (Print) IS - 1744-5485 (Linking) VI - 9 IP - 2 DP - 2013 TI - Identification of motor imagery tasks through CC-LR algorithm in brain computer interface. PG - 156-72 LID - 10.1504/IJBRA.2013.052447 [doi] AB - 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. FAU - Siuly AU - Siuly AD - Department of Mathematics and Computing, University of Southern Queensland, Toowoomba, Australia. FAU - Li, Yan AU - Li Y FAU - Wen, Peng AU - Wen P LA - eng PT - Journal Article PL - Switzerland TA - Int J Bioinform Res Appl JT - International journal of bioinformatics research and applications JID - 101253758 SB - IM MH - *Algorithms MH - *Brain-Computer Interfaces MH - Cluster Analysis MH - Databases, Factual MH - *Imagination MH - Least-Squares Analysis MH - Logistic Models MH - *Motor Skills MH - Pattern Recognition, Automated MH - Support Vector Machine EDAT- 2013/03/08 06:00 MHDA- 2014/03/13 06:00 CRDT- 2013/03/08 06:00 PHST- 2013/03/08 06:00 [entrez] PHST- 2013/03/08 06:00 [pubmed] PHST- 2014/03/13 06:00 [medline] AID - 7285282673H72332 [pii] AID - 10.1504/IJBRA.2013.052447 [doi] PST - ppublish SO - Int J Bioinform Res Appl. 2013;9(2):156-72. doi: 10.1504/IJBRA.2013.052447.