International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
Volume 46 - Issue 19 |
Published: May 2012 |
Authors: Ali Bakhshi, Alireza Ahmadifard |
![]() |
Ali Bakhshi, Alireza Ahmadifard . A Comparison among Classification Accuracy of Neural Network, FLDA and BLDA in P300-based BCI System. International Journal of Computer Applications. 46, 19 (May 2012), 11-15. DOI=10.5120/7048-9498
@article{ 10.5120/7048-9498, author = { Ali Bakhshi,Alireza Ahmadifard }, title = { A Comparison among Classification Accuracy of Neural Network, FLDA and BLDA in P300-based BCI System }, journal = { International Journal of Computer Applications }, year = { 2012 }, volume = { 46 }, number = { 19 }, pages = { 11-15 }, doi = { 10.5120/7048-9498 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2012 %A Ali Bakhshi %A Alireza Ahmadifard %T A Comparison among Classification Accuracy of Neural Network, FLDA and BLDA in P300-based BCI System%T %J International Journal of Computer Applications %V 46 %N 19 %P 11-15 %R 10.5120/7048-9498 %I Foundation of Computer Science (FCS), NY, USA
In the past decade, many studies focused on communication systems that translate brain activities into commands for a computer or other devices that called brain computer interface (BCI). In this study, we present a BCI system that achieves high classification accuracy with Neural Network (NN), Fisher Linear Discriminant Analysis (FLDA) and Bayesian Linear Discriminant Analysis (BLDA) for both disabled and able-bodies subjects. The system is based on the P300 evoked potential and is tested with four able-bodied and five severely disabled subjects. The effect of different electrode configurations on accuracy of machine learning Algorithms is tested and effect of other factors on classification accuracy in P300-based systems are discussed.