Abstract
Brain Computer Interface is a new technology aimed to communicate the user’s intentions without using nerves or muscles. To obtain this objective, BCI devices make use of classifiers which translate inputs from the user’s brain signals into commands for external devices. This paper describes an adaptive bi-stage classifier based on RBF neural networks and Hidden Markov Models. The first stage analyses the user’s electroencephalografic input signal and provides sequences of pre-assignations to the second stage. The segment of EEG signal is assigned to the HMM with the highest probability of generating the pre-assignation sequence.
The algorithm is tested with real samples of electroencephalografic signal, from five healthy volunteers using the cross-validation method. The results allow to conclude that it is possible to implement this algorithm in an on-line BCI device, but a huge dependency in the percentage of the correct classification from the user and the setup parameters has been detected.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Vidal, J.J.: Toward direct brain-computer communication (1973)
Wolpaw, J.R., et al.: Brain-Computer Interface Technology: A Review of the First International Meeting. IEEE Trans. on Reh. Eng. 8(2), 164–171 (2000)
Birbaumer, N., et al.: The thought translation device (TTD) for completely paralyzed patients. IEEE Trans. on Reh. Eng. 8(2), 190–193 (2000)
Wolpaw, J.R., et al.: Brain-Computer interface for communication and control. Clinical Neurophysiology 113, 767–791 (2002)
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), 174–179 (2000)
Kostov, A., Polak, M.: Parallel man-machine training in development of EEG-based cursor control. IEEE Trans. on Reh. Eng. 8(2), 203–205 (2000)
Pfurtscheller, et al.: Current trends in Graz brain-computer interface (BCI) research. IEEE Trans. on Reh. Eng. 8(2), 216–219 (2000)
Pfurtscheller, et al.: Brain oscillations control hand orthosis in a tetraplegic. Neuroscience Letters 1(292), 211–214 (2000)
Pineda, J.A., et al.: Learning to Control Brain Rhythms: Making a Brain-Computer Interface Possible. IEEE Trans. on Reh. Eng. 11(2), 181–184 (2003)
Ripley, B.: Pattern Recognition and Neural Networks, 2nd edn. Cambridge University Press, London (2000)
Penny, W.D., et al.: EEG-based communication: A pattern recognition approach. IEEE Trans. on Reh. Eng. 8(2), 214–215 (2000)
Neuper, C., et al.: Motor Imagery and Direct Brain-Computer Communication. Proceedings of the IEEE 89(7), 1123–1134 (2001)
Proakis, J.G., Manolakis, D.G.: Tratamiento digital de señales: [principios, algoritmos y aplicaciones]. Prentice-Hall, Madrid (1997)
Martinez, J.L., Barrientos, A.: The windowing Effect in Cerebral Pattern Classification. In: An Application to BCI Technology. IASTED Biomedical Engineering BioMED 2006, pp. 1186–1191 (February 2006)
Martinez, J.L., Barrientos, A.: Brain Computer Interface. Comparison of Neural Networks Classifiers. In: Proceedings of the BIODEVICES International Conference on Biomedical Electronics and Devices, vol. 1(1), pp. 3–10 (January 2008)
Martinez, J.L., Barrientos, A.: Linear Discriminant Analysis on Brain Computer Interface. In: IEEE International Symposium on Intelligent Signal Processing, Conference Proceedings Book, pp. 859–864 (October 2007)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition (February 1989)
Rabiner, L.R., Juang, B.H.: An Introduction to Hidden Markov Models (January 1986)
Martinez, J.L., Barrientos, A.: Brain Computer Interface. In: Application of an adaptive bi-stage classifier based on RBF-HMM, Proceedings of the BIODEVICES International Conference on Biomedical Electronics and Devices, vol. 1(1), pp. 13–20 (January 2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pérez, J.L.M., Cruz, A.B. (2011). Adaptive RBF-HMM Bi-Stage Classifier Applied to Brain Computer Interface. In: Fred, A., Filipe, J., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2010. Communications in Computer and Information Science, vol 127. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18472-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-18472-7_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18471-0
Online ISBN: 978-3-642-18472-7
eBook Packages: Computer ScienceComputer Science (R0)