Abstract
A Brain Computer Interface (BCI) is a device that allows the user to communicate with the world without utilizing voluntary muscle activity (i.e., using only the electrical activity of the brain). It makes use of the well-studied observation that the brain reacts differently to different stimuli, as a function of the level of attention allotted to the stimulus stream and the specific processing triggered by the stimulus. In this article we present a single trial independent component analysis (ICA) method that is working with a BCI system proposed by Farwell and Donchin. It can dramatically reduce the signal processing time and improve the data communicating rate. This ICA method achieved 76.67% accuracy on single trial P300 response identification.
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Keywords
- Independent Component Analysis
- Independent Component Analysis
- Brain Computer Interface
- P300 Response
- Independent Component Analysis Algorithm
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References
Hoffmann, U., Vesin, J.M., Ebrahimi, T.: Recent Advances in Brain-Computer interfaces. In: IEEE International Workshop on Multimedia Signal Processing (MMSP 2007) (2007)
Kalcher, J., Flotzinger, D., Neuper, C., Gölly, S., Pfurtscheller, G.: Graz brain-computer interface II: towards communication between humans and computers based on online classification of three different EEG patterns. Med. & Biol. Eng. & Comput. 34, 382–388 (1996)
Wolpaw, J.R., McFarland, D.J., Neat, G.W., Forneris, C.A.: An EEG-based brain-computer interface for cursor control. Electroencephalography and Clinical Neurophysiology 78, 252–259 (1991)
McFarland, D.J., Neat, G.W., Read, R.F., Wolpaw, J.R.: An EEG-based method for graded cursor control. Psychobiology 21(1), 77–81 (1993)
Pfurtscheller, G., Flotzinger, D., Kalcher, J.: Brain-Computer Interface – a new communication device for handicapped persons. Journal of Microcomputer Applications 16, 293–299 (1993)
Farwell, L.A., Donchin, E.: Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology 70, 510–523 (1988)
Rebsamen, B., Burdet, E., Guan, C.T., Zhang, H.H., Teo, C.L., Zeng, Q., Ang, M., Laugier, C.: A Brain-Controlled Wheelchair Based on P300 and Path Guidance. In: Proceedings of IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (2006)
Fabiani, M., Gratton, G., Karis, D., Donchin, E.: Definition, identification, and reliability of measurement of the P300 component of the event-related brain potential. Adv. Psychophysiol. 2, 1–78 (1987)
Krusienski, D.J., Sellers, E.W., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R.: Toward enhanced P300 speller performance. J. Neurosci. Methods 167(1), 15–21 (2008)
Kaper, M., Meinicke, P., Grossekathoefer, U., Lingner, T., Ritter, H.: BCI Competition 2003—Data Set IIb: Support Vector Machines for the P300 Speller Paradigm. IEEE Transactions on Biomedical Engineering 51(6) (June 2004)
Serby, H., Yom-Tov, E., Inbar, G.F.: An Improved P300-Based Brain-Computer Interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering 13(1) (March 2005)
Bostanov, V.: BCI Competition 2003–Data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram. IEEE Trans Biomed. Eng. 51, 1057–1061 (2004)
Krusienski, D.J., Sellers, E.W., Cabestaing, F., Bayoudh, S., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R.: A comparison of classification techniques for the P300 speller. J. Neural Eng. 3, 299–305 (2006)
Hyvärinen, A.: Fast ICA by a fixed-point algorithm that maximizes non-Gaussianity. Independent Component Analysis Principles and Practice. Cambridge University Press, Cambridge (2001)
Bell, A.J., Sejnowski, T.J.: An information-maximisation approach to blind separation and blind deconvolution. Neural Computation 7, 1129–1159 (1995)
Cardoso, J.F., Comon, P.: Equivariant adaptive source separation. IEEE Transactions on Signal Processing 45(2), 434–444 (1996)
Hyvärinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)
Hyvärinen, A., Oja, E.: Independent Component Analysis: Algorithms and Applications. Neural Networks 13(4-5), 411–430 (2000)
Donchin, E., Spencer, K.M., Wijesinghe, R.: The Mental Prosthesis: Assessing the Speed of a P300-Based Brain–Computer Interface. IEEE Transactions on Rehabilitation Engineering 8(2) (June 2000)
Penny, W.D., Roberts, S.J., Curran, E.A., Stokes, M.J.: EEG-based communication: A pattern recognition approach. IEEE Trans. Rehab. Eng. 8, 214–215 (2000)
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Li, K., Sankar, R., Arbel, Y., Donchin, E. (2009). P300 Based Single Trial Independent Component Analysis on EEG Signal. In: Schmorrow, D.D., Estabrooke, I.V., Grootjen, M. (eds) Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience. FAC 2009. Lecture Notes in Computer Science(), vol 5638. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02812-0_48
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DOI: https://doi.org/10.1007/978-3-642-02812-0_48
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