Abstract
One of the workhorses of Brain Computer Interfaces (BCI) is the P300 speller, which allows a person to spell text by looking at the corresponding letters that are laid out on a flashing grid. The device functions by detecting the Event Related Potentials (ERP), which can be measured in an electroencephalogram (EEG), that occur when the letter that the subject is looking at flashes (unexpectedly). In this work, after a careful analysis of the EEG signals involved, we propose a preprocessing method that allows us to improve on the state-of-the-art results for this kind of applications. Our results are comparable, and sometimes better, than the best results published, and do not require a feature (channel) selection step, which is extremely costly, and which must be adapted to each user of the P300 speller separately.
This work was supported by the CSIC 2012 I+D project 519, and the CSIC “Iniciación a la Investigación” program.
Chapter PDF
Similar content being viewed by others
Keywords
References
Rakotomamonjy, A., Guigue, V.: BCI Competition III: Dataset II - ensemble of SVMs for BCI P300 speller. IEEE Trans. Biomed. Eng. (2007)
American Electroencephalographic Society: Guidelines for standard electrode positionnomenclature. J. Clin. Neurophysiol. 8, 200–202 (1991)
Vidal, J.: Toward direct brain-computer communication. Annual Review of Biophysics and Bioengineering 2, 157–180 (1973)
Wolpaw, J., Birbaumer, N., Heetderks, W., McFarland, D., Peckham, P., Schalk, G., Donchin, E., a Quatrano, L., Robinson, C., Vaughan, T.: Brain-computer interface technology: a review of the first international meeting. IEEE Trans. on Rehabilitation Eng. 8, 164–173 (2000)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI1995 Proceedings of the 14th International Joint Conference on Artificial Intelligence, Vol. 2, pp. 1137–1143. Morgan Kaufmann (1995)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)
Smola, A., Schölkopf, B.: A tutorial on support vector regression. Statistics and Computing 14, 199–222 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Patrone, M., Lecumberry, F., Martín, Á., Ramirez, I., Seroussi, G. (2015). EEG Signal Pre-Processing for the P300 Speller. In: Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science(), vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_67
Download citation
DOI: https://doi.org/10.1007/978-3-319-25751-8_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25750-1
Online ISBN: 978-3-319-25751-8
eBook Packages: Computer ScienceComputer Science (R0)