Classification of EEG Signals Produced by RGB Colour Stimuli

  • Saim Rasheed Department of Information Technology, Faculty of Computing and IT King Abdulaziz University
  • Daniele Marini Dipartimento di Informatica, Università degli Studi di Milano, Italia
Keywords: EEG, Support Vector Machinces, Event related Spectral Perturbation, Visual Evoked Potentials


In this paper we have presented results for classification of electroencephalograph (EEG) signals produced by the random visual exposure of primary colours i.e. red, green and blue to the subject while sitting in a dark room. Event-related spectral perturbations (ERSP) are used as features for support vector machine (SVM). Our objective was to classify the EEG signals as Red, Green and Blue classes and we have successfully classified the three visual conditions having accuracy of 84%, 89% and 98% with linear, polynomial and radial basis function kernels respectively with in all the groups of data among all the subjects.


(1) Abe, S., 2005. Support Vector Machines for Pattern Classification S. Singh, Japan: Springer.

(2) Benjamin, B., Gabriel, C. & Müller, K., 2002. Classifying Single Trial EEG : Towards Brain Computer Interfacing. In Advances in Neural Inf. Proc. Systems. MIT Press, pp. 157--164.

(3) Chang, C. & Lin, C., 2010. LIBSVM : a Library for Support Vector Machines. Department of Computer Science, National Taiwan University, Taiwan, 1-30.

(4) Chiappa, S. & Barber, D., 2006. EEG classification using generative independent component analysis. Neurocomputing, 69, 769-777.

(5) Delorme, A. & Makeig, S., 2004. EEGLAB : an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134, 9-21.

(6) Dobelle, W.H., 2000. Artificial Vision for the Blind by Connecting a Television Camera to the Visual Cortex. ASAIO, 46, 3-9.

(7) Fan, R., Chen, P. & Lin, C., 2005. Working Set Selection Using Second Order Information for Training Support Vector Machines. Journal of Machine Learning Research, 6, 1889-1918.

(8) Farwell, L. & Donchin, E., 1988. Talking off the top of your head - Toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70(6), 510-523.

(9) Foresta, F.L., Mammone, N. & Morabito, F.C., 2009. PCA – ICA for automatic identification of critical events in continuous coma-EEG monitoring. Biomedical Signal Processing And Control, 4(3), 229-235.

(10) Garrett, D. et al., 2003. Comparison of Linear, Nonlinear, and Feature Selection Methods for EEG Signal Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11(2), 141-144.

(11) Guan, J. & Chen, Y., 2008. Single-trial EEG classification using in-phase average for brain-computer interface. Frontiers of Electrical and Electronic Engineering in China, 3(2), 194-197.

(12) Hallez, H. et al., 2009. Removing muscle and eye artifacts using blind source separation techniques in ictal EEG source imaging. Clinical Neurophysiology, 120(7), 1262-1272.

(13) Hsu, C., Chang, C. & Lin, C., 2003. A Practical Guide to Support Vector Classification, National Taiwan University.

(14) Huang, R., Jung, T. & Makeig, S., 2005. Analyzing Event-Related

Brain Dynamics in Continuous Compensatory Tracking Tasks. In IEEE-EMBS 27th Annual International Conference of Engineering in Medicine and Biology. Shanghai, pp. 5750-5753.

(15) James, C.J. & Hesse, C.W., 2005. Independent component analysis for biomedical signals. Physiological Measurement, 26, R15-R39.

(16) Jung, T. et al., 2000. Independent Component Analysis of Biomedical Signals. In Proc. Int. Workshop on Independent Component Analysis and Signal Separation. pp. 633-644.

(17) Jung, T. et al., 2000. Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clinical Neurophysiology, 111(10), 1745-1758.

(18) Kaper, M. et al., 2004. BCI Competition 2003 — Data Set IIb : Support Vector Machines for the P300 Speller Paradigm. IEEE Transactions on Biomedical Engineering, 51(6), 1073-1076.

(19) Krusienski, D.J. et al., 2008. Toward Enhanced P300 Speller Performance. Journal of Neuroscience Methods, 167(1), 15-21.

(20) Leeb, R. et al., 2006. Walking by Thinking : The Brainwaves Are Crucial , Not the Muscles ! PRESENCE, 15(5), 500 -514.

(21) Lotte, F. et al., 2007. A review of classification algorithms for EEG-based brain – computer interfaces. Journal of Neural Engineering, 4(2), R1-R13.

(22) Makeig, S. et al., 2004. Mining event-related brain dynamics. Trends in Cognitive Sciences, 8(5), 204-210.

(23) Makeig, S., 1993. Auditory Event-Related Dynamics of the EEG Spectrum and Effects of Exposure to Tones. Electroencephalography and Clinical Neurophysiology, 86(4), 283-293.

(24) Peterson, D.A. et al., 2005. Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface. EURASIP Journal on Applied Signal Processing, 19, 3128-3140.

(25) Pfurtscheller, G. & Neuper, C., 2001. Motor Imagery and Direct Brain – Computer Communication. In Proceedings of the IEEE. pp. 1123-1134.

(26) Pires, G., Castelo-branco, M. & Nunes, U., 2008. Visual P300-based BCI to steer a Wheelchair: a Bayesian Approach. In 30th Annual International IEEE EMBS conference. Vancouver, pp. 658-661.

(27) Rakotomamonjy, A. et al., 2005. Ensemble of SVMs for Improving Brain Computer Interface P300 Speller Performances. In Artificial Neural Networks: Biological Inspirations – ICANN. Berlin: Springer, pp. 45-50.

(28) Schalk, G. et al., 2004. BCI 2000 : A General-Purpose Brain-Computer Interface ( BCI ) System. IEEE transactions on Biomedical Engineering, 51(6), 1034-1043.

(29) Steinwart, I. & Christmann, A., 2008. Support Vector Machines M.Jordan, J. Kleinberg, & B. Scholkopf, New York: Springer.

(30) Stone, J.V., 2004. Independent Component Analysis: A Tutorial Introduction, MIT Press.

(31) Vapnik, V.N., 2000. The Nature of Statistical Learning Theory M. Jordan et al., Springer.

(32) Wang, L., 2005. Support Vector Machines: Theory and Applications J. Kacprzyk, Springer.

(33) Wolpaw, J.R. et al., 2002. Brain – computer interfaces for communication and control. Clinical Neurophysiology, 113, 767-791.

(34) Yoto, A. et al., 2007. Effects of Object Color Stimuli on Human Brain Activities in Perception and Attention Referred to EEG Alpha Band Response. Journal of Physiological Anthropology, 26(3), 373-379.

How to Cite
Rasheed, S., & Marini, D. (2015). Classification of EEG Signals Produced by RGB Colour Stimuli. Journal of Biomedical Engineering and Medical Imaging, 2(5), 56.