JBiSE  Vol.6 No.10 , October 2013
Optimizing feature vectors and removal unnecessary channels in BCI speller application
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.
Cite this paper: Perseh, B. and Kiamini, M. (2013) Optimizing feature vectors and removal unnecessary channels in BCI speller application. Journal of Biomedical Science and Engineering, 6, 973-981. doi: 10.4236/jbise.2013.610121.

[1]   Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., McFarland, D.J., Peckham, P.H., Schalk, G., Donchin, E., Quatrano, L.A., Robinson, C.J. and Vaughan, T.M. (2000) Brain-computer interface technology: A review of the first international meeting. IEEE Transactions on Rehabilitation Engineering, 8, 164-173.

[2]   Wolpaw, J.R. (2004) Brain-computer interfaces (BCIs) for communication and control: Current status. Proceedings of the 2nd International Brain-Computer Interface Workshop and Training Course, Graz, 17-18 September 2004, 29-32.

[3]   Henning, G. and Husar, P. (1995) Statistical detection of visually evoked potentials. IEEE Engineering in Medicine and Biology, 14, 386-390.

[4]   Liu, Y., Zhou, Z., Hu, D. and Dong, D. (2005) T-weighted approach for neural information processing in P300 based brain-computer interface. IEEE Conference on Neural Networks and Brain, Beijing, 13-15 October 2005, 1535-1539.

[5]   Farwell, L.A. and Donchin, E. (1988) Talking off the top of your head: Toward a mental prosthesis utilizing eventrelated brain potentials. Electroencephalography and Clinical Neurophysiology, 70, 510-523.

[6]   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.

[7]   Schalk, G., McFarland, D.G., Hinterberger, T., Birbaumer, N. and Wolpaw, J. (2004) BCI2000: A general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engineering, 51, 10341043.

[8]   Chen, Y.W. and Lin, C.J. (2003) Combining SVMs with various feature selection strategies. Studies in Fuzziness and Soft Computing, 207, 315-324.

[9]   Fatourechi, M., Birch, G.E. and Ward, R.K. (2007) Application of a hybrid wavelet feature selection method in the design of a self-paced brain interface system. Journal of Neuro Engineering and Rehabilitation, 4, 11.

[10]   Yu, S.N. and Chen, Y.H. (2007) Electrocardiogram beat classication based on wavelet transformation and probabilistic neural net work. Pattern Recognition Letters, 28, pp. 1142-1150.

[11]   Fisher, R.A. (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179-188.

[12]   Hofmann, H., Vesin, J.M., Ebrahimi, E. and Diserens, K. (2008) An efficient P300-based brain-computer interface for disabled subjects. Journal of Neuradcience Methods, 167, 115-125.

[13]   Rakotomamonjy, A. and Guigue, V. (2008) BCI competition III: Dataset II-ensemble of SVMs for BCI P300 speller. IEEE Transactions on Biomedical Engineering, 55, 1147-1154.