Abstract

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.

References

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• H. Cecotti, A. Graser, "Convolutional neural network for P300 detection with application to brain- computer interfaces ", IEEE Transaction on Pattern Analysis and Machine Intelligence, 2011, Vol. 33, No. 3.
• H. Zhang, C. Guan and C. Wang, "Asynchronous P300-based brain computer interfaces: a computational approach with statistical models", IEEE Transaction on Biomedical Engineering, 2008, Vol. 55, No. 6.
• E. W. Sellers, D. J. Krusienski, D. J. McFarland, T. M. Vaughan, J. R. Wolpawa, 2006: A P300 event-related potential brain–computer interface (BCI):The effects of matrix size and inter stimulus interval on performance," Biological Psychology 73, Elsevier, pp. 242–252.
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• J. U. Duncombe, 1959: Infrared navigation—Part I: An assessment of feasibility," IEEE Trans. Electron Devices, Vol. ED-11, pp. 34-39.
• A. Rodrigo, 2009 Feature extraction and classification for brain computer interfaces, Doctoral Thesis. ISBN (Electronic) 978-87-7094-034-4, University of Aalborg.
• H. Cecotti, A. Graser, "Convolutional neural network for P300 detection with application to brain- computer interfaces ", IEEE Transaction on Pattern Analysis and Machine Intelligence, 2011, Vol. 33, No. 3.
• H. Zhang, C. Guan and C. Wang, "Asynchronous P300-based brain computer interfaces: a computational approach with statistical models", IEEE Transaction on Biomedical Engineering, 2008, Vol. 55, No. 6.
• E. W. Sellers, D. J. Krusienski, D. J. McFarland, T. M. Vaughan, J. R. Wolpawa, 2006: A P300 event-related potential brain–computer interface (BCI):The effects of matrix size and inter stimulus interval on performance," Biological Psychology 73, Elsevier, pp. 242–252.
• U. Hoffmann, J. Vesin, K. Diserens, and T. Ebrahimi, "An efficient P300-based brain-compuer interface for disabled subjects", Journal of Neuroscience Methods, 2007.
• K. Hornik, M. Stinchcombe, and H. White, 1989: Multilayer feedforward networks are universal approximators, Neural Networks, vol. 2, no. 5, pp. 359–366.
• K. Hornik, M. Stinchcombe, and H. White, 1990: Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks, Neural Networks, vol. 3, no. 5, pp. 551–560.
• Z. Cashero, 2011 Comparison of EEG preprocessing methods to improve the classification of P300 trials. Master of Science Thesis, Colorado State University.
• Senthilmurugan. M, Latha. M and Malmurugan. N, "Classification in EEG-based brain computer interfaces using inverse model", International Journal of Computer Theory and Engineering, 2011, Vol. 3, No. 2.