Blankertz, B., Mller, K.R., Krusienski, D.J., Schalk, G., Wolpaw, J.R., Schlgl, A., Pfurtscheller, G., delRMilln, J., Schrder, M. and Birbaumer, N. (2006) The BCI competetion III: Validating alternative approaches to actual BCI problems. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14, 153-159.
http://dx.doi.org/10.1109/TNSRE.2006.875642

has been cited by the following article:

• TITLE: Optimizing feature vectors and removal unnecessary channels in BCI speller application

  AUTHORS: Bahram Perseh, Majid Kiamini

  KEYWORDS: Brain Computer Interface (BCI); Speller Application; Event Related Potential (ERP); ERP Components; Channel Selection; Feature Extraction

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.6 No.10, October 22, 2013

  ABSTRACT: In this paper we will discuss novel algorithms to develop the brain-computer interface (BCI) system in speller application based on single-trial classification of electroencephalogram (EEG) signal. The idea is to employ proper methods for reducing the number of channels and optimizing feature vectors. Removal unnecessary channels and reducing feature dimension result in cost decrement, time saving and improve the BCI implementation eventually. Optimal channels will be gotten after two stages sifting. In the first stage, the channels reduced up to 30% based on channels of the important event related potential (ERP) components and in the next stage, optimal channels were extracted by backward forward selection (BFS) algorithm. Also we will show that suitable single-trial analysis requires applying proper feature vector that was constructed by recognizing important ERP components, so as to propose an algorithm to distinguish less important features in feature vectors. F-Score criteria used to recognize effective features which created more discrimination between different classes and feature vectors were reconstructed based on effective features. Our algorithm has tested on dataset II of BCI competition III. The results show that we achieve accuracy up to 31% in single-trial, which is better than the performance of winner who is in this competition (about 25.5%). Also we use simple classifier and few channels to compute output performances while more complicated classifier and all channels are used by them.