International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 46 - Number 19 |
Year of Publication: 2012 |
Authors: Ali Bakhshi, Alireza Ahmadifard |
10.5120/7048-9498 |
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
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.