P300 Oddball Task and Classification Based on Support Vector Machine for BCI System

Gao Chao Cui; Jian Ting Cao

doi:10.4028/www.scientific.net/AMM.397-400.2187

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 397-400P300 Oddball Task and Classification Based on...

P300 Oddball Task and Classification Based on Support Vector Machine for BCI System

Abstract:

The P300 oddball task is the most popular paradigm in the existing BCI systems. Recently using auditory stimuli in P300 oddball task arises since it gives much freedom to the BCI user. In this paper, we present a novel BCI paradigm using P300 and P100 responses. Since P300 and P100 responses occur in the frontal lobe and the temporal lobe respectively, so that we can use these responses stimulated by an audio in a single task. The main advantage of our designed paradigm is that we can obtain two different kinds of responses in a single trial EEG task. In the EEG data analysis, we first employ the multivariate empirical mode decomposition (MEMD) algorithm to extract P300 and P100 components. And then, we employ a support vector machine (SVM) technique for the feature classification.

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Periodical:

Applied Mechanics and Materials (Volumes 397-400)

Pages:

2187-2190

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.397-400.2187

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Online since:

September 2013

Authors:

Gao Chao Cui, Jian Ting Cao

Keywords:

Brain Computer Interface (BCI), Electroencephalographm (EEG), P300 and P100 Tasks, Support Vector Machine (SVM)

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References

[1] Wolpaw, J.R., Birbaumer, N., Mcfarland, D.J., Pfurtscheller, G., Vaughan, T. M.: Brain-Computer Interfaces for Communication and Control. Clinical Neurophysiology, vol. 113, no. 6, pp.767-791 (2002).

DOI: 10.1016/s1388-2457(02)00057-3

Google Scholar

[2] Rehman, N., Mandic, D. P.: Empirical Mode Decomposition for Trivariate Signals. IEEE Transactions on Signal Processing, vol. 58, no. 3, pp.1059-1068 (2010).

DOI: 10.1109/tsp.2009.2033730

Google Scholar

[3] Rehman, N., Mandic, D.P.: MultivariateEmpiricalModeDecomposition. Proceedings of the Royal Society A, vol. 466, no. 2117, pp.1291-1302 (2010).

Google Scholar

[4] Huang, N.E., Wu, M., Long, S., Shen, S., Qu, W., Gloersen, P., Fan, K.: A Confidence Limit for The Empirical Mode Decomposition and Hilbert Spectral Analysis. Proc. R. Soc. Lond. A 459, 2317-2345 (2003).

DOI: 10.1098/rspa.2003.1123

Google Scholar

[5] Amari, S., Wu, S.: Improving Support Vector Machine Classers by Modifying Kernel Functions. Neural Networks, vol. 12, no. 6, pp.783-789 (1999).

DOI: 10.1016/s0893-6080(99)00032-5

Google Scholar

[6] Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing Multiple Parameters for Support Vector Machines. Machine Learning, vol. 46, no. 1, pp.131-159 (2002).

DOI: 10.1023/a:1012450327387

Google Scholar

[7] Sharbrough, F., Chatrian, C.E., Lesser, R.P., Luders, H., Nuwer, M. Picton, T.W.: American Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature. Journal of Clinical Neuro- physiology, 8, pp.200-202 (1991).

DOI: 10.1097/00004691-199104000-00007

Google Scholar