Abstract
Currently, one of the challenges in a Brain Computer Interface (BCI) technologies is the improvement real-time event-related potential (ERP) detection. Variability and low signal-to-noise ratio (SNR) impair detection methods. We hypothesized that if in a P300-based BCI we find the electrodes with the maximum relative voltage area (the “maximum relative” term refers to the area within each trial, but not between trials) where a P300 can be located, we will improve the performance of a classifier and reduce the number of trials necessary to achieve 100% success. We propose a method that calculates successively the maximum relative voltage areas in the P300 region of the EEG signal for each stimulus. In this way, differences between a target and a non-target stimulus are maximized. This method was tested with a linear classifier (LDA), known for its good performance and low computational cost. We observed that a single electrode with maximum relative voltage area in a P300 region can give more information than the traditional 4 electrode measurement. The preliminary results show that by detecting appropriate characteristics in the EEG signal, we can reduce the error by trial as well as the number of electrodes. The detection of the maximum relative voltage area in the EEG electrodes is a characteristic that can contribute to increase the SNR and decrease the prediction error with the smallest number of trials in the P300-based BCI systems. This type of methods that seek specific characteristics in the signals can also contribute to the management of the variability present in the BCI systems. This method can be used both for an online and offline analysis.
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References
Blankertz, B., Muller, K.R., Curio, G., Vaughan, T.M., Schalk, G., Wolpaw, J.R., Schlogl, A., Neuper, C., Pfurtscheller, G., Hinterberger, T., Schroder, M., Birbaumer, N.: The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans. Biomed. Eng. 51(6), 1044–1051 (2004)
Blankertz, B., Lemm, S., Treder, M., Haufe, S., Müller, K.-R.: Single-trial analysis and classification of ERP components–a tutorial. NeuroImage 56(2), 814–825 (2011)
Boksem, M.A.S., Meijman, T.F., Lorist, M.M.: Effects of mental fatigue on attention: an ERP study. Cogn. Brain. Res. 25(1), 107–116 (2005)
Donchin, E., Spencer, K.M., Wijesinghe, R.: The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans. Rehabil. Eng. 8(2), 174–179 (2000)
Farwell, L.A., Donchin, E.: Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70(6), 510–523 (1988)
Kahneman, D.: Attention and effort. Citeseer (1973)
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 Trans. Biomed. Eng. 51(6), 1073–1076 (2004)
Käthner, I., Wriessnegger, S.C., Müller-Putz, G.R., Kübler, A., Halder, S.: Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain-computer interface. Biol. Psychol. 102, 118–129 (2014)
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(4), 299–305 (2006)
Mak, J.N., Arbel, Y., Minett, J.W., McCane, L.M., Yuksel, B., Ryan, D., Thompson, D., Bianchi, L., Erdogmus, D.: Optimizing the P300-based brain-computer interface: current status, limitations and future directions. J. Neural Eng. 8(2), 025003 (2011)
McCann, M.T., Thompson, D.E., Syed, Z.H., Huggins, J.E.: Electrode subset selection methods for an EEG-based P300 brain-computer interface. Disabil. Rehabil. Assistive Technol. 10(3), 216–220 (2015)
Polich, J.: Updating P300: An integrative theory of P3a and P3b. Clin. Neurophysiol. 118(10), 2128–2148 (2007)
Rakotomamonjy, A., Guigue, V.: BCI competition III: dataset II- ensemble of SVMs for BCI P300 speller. IEEE Trans. Biomed. Eng. 55(3), 1147–1154 (2008)
Salvaris, M., Sepulveda, F.: Visual modifications on the P300 speller BCI paradigm. J. Neural Eng. 6(4), 046011 (2009)
Squires, N.K., Squires, K.C., Hillyard, S.A.: Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli in man. Electroencephalogr. Clin. Neurophysiol. 38(4), 387–401 (1975)
Sutton, S., Braren, M., Joseph, Z., John, E.R.: Evoked-potential correlates of stimulus uncertainty. Science 150(3700), 1187–1188 (1965)
Acknowledgments
This work was funded by Spanish projects of Ministerio de Economía y Competitividad/FEDER TIN2014-54580-R, DPI2015-65833-P (http://www.mineco.gob.es/) and Predoctoral Research Grants 2015-AR2Q9086 of the Government of Ecuador through the Secretaría de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT).
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Changoluisa, V., Varona, P., Rodriguez, F.B. (2017). How to Reduce Classification Error in ERP-Based BCI: Maximum Relative Areas as a Feature for P300 Detection. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_42
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DOI: https://doi.org/10.1007/978-3-319-59147-6_42
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