Abstract
The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires an accurate classification and recognition of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. This paper proposes a Genetic algorithm (GA) and Support Vector Machine (SVM) hybrid approach to accomplish this EEG classification task for potential BCI applications. An Oddball stimulus program and evoked event-related coherence program were designed to evaluate our method. The present study systematically evaluates the performance of the one channel pair event-related coherence feature set for EEG signal classification of auditory task. A GA approach for feature selection is presented which used to reduce the dimension of event-related coherence feature parameters. With the base classifiers of SVM, classification experiments are carried out upon real EEG recordings. Experimental results suggest the feasibilities of the new feature set, and we also derive some valuable conclusions on the performance of the EEG signal classification methods. The high recognition rates and the method’s procedural and computational simplicity make it a particularly promising method for achieving real-time BCI system based on evoked potential event-related coherence in the future.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Šťastný, J., Sovka, P., Stančák, A.: EEG Signal Classification: Introduction to the Problem. Radioengineering 12, 51–55 (2003)
Curran, E.A., Stokes, M.J.: Learning to control brain activity: a review of the production and control of EEG components for driving brain–computer interface (BCI) systems. Brain Cogn. 51, 326–336 (2003)
Jahankhani, P., Kodogiannis, V., Revett, K.: EEG signal classification using wavelet feature extraction and neural networks. In: International Symposium on Modern Computing, pp. 52–57 (2006)
Garrett, D., Peterson, D.A., Anderson, C.W., Thaut, M.H.: Comparison of Linear and Nonlinear Methods for EEG Signal Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11, 141–144 (2003)
Kaper, M., Meinicke, P., Grossekathoefer, U., Lingner, T., Ritter, H.: Bci competition data set iib: support vector machines for the p300 speller paradigm. IEEE Transactions on Biomedical Engeneering 51(6), 1073–1076 (2004)
Pfurtscheller, G., Neuper, C., Flotzinger, D., Pregenzer, M.: Eeg-based discrimination between imagination of right and left hand movement. Electroencephalography and Clinical Neurophysiology 103, 642–651 (1997)
Chiappa, S., Bengio, S.: Hmm and iohmm modeling of eeg rhythms for asynchronous bci systems. In: European Symposium on Artificial Neural Networks, ESANN (2004)
Millan, J.R., Mourino, J.: Asynchronous BCI and local neural classifiers: An overview of the Adaptive Brain Interface project. IEEE Transactions on Neural Systems and Rehabilitation Engineering, Special Issue on Brain-Computer Interface Technology (2003)
Penny, W.D., Roberts, S.J., Curran, E.A., Stokes, M.J.: Eeg-based communication: a pattern recognition approach. IEEE Transactions on Rehabilitation Engeneering 8(2), 214–215 (2000)
Pfurtscheller, G., Neuper, C., Schlogl, A., Lugger, K.: Separability of eeg signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Transactions on Rehabilitation Engineering 6(3) (1998)
Wang, T., Deng, J., He, B.: Classifying eeg-based motor imagery tasks by means of time-frequency synthesized spatial patterns. Clinical Neurophysiology 115(12), 2744–2753 (2004)
Qin, L., Ding, L., He, B.: Motor imagery classification by means of source analysis for brain computer interface applications. Journal of Neural Engineering, 135–141 (2004)
Kamousi, B., Liu, Z., He, B.: Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering 13, 166–171 (2005)
Congedo, M., Lotte, F., Lecuyer, A.: Classification of movement intention by spatially filtered electromagnetic inverse solutions. Physics in Medicine and Biology 51(8), 1971–1989 (2006)
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces. Journal of Neural Engineering 4 (2007)
Sun, S., Zha, C.: An optimal kernel feature extractor and its application to EEG signal classification. Neurocomputing 69, 1743–1748 (2006)
Inouye, T., Shinosaki, K., Iyama, A., Matsumoto, Y.: Localization of activated areas and directional EEG patterns during mental arithmetic. Electroencephalography and Clinical Neurophysiology 86(4), 224–230 (1993)
Anderson, C.W., Devulapalli, S.V., Stolz, E.A.: Determining mental state from EEG signals using neural networks. Scientific Programming 4(3), 171–183 (1995)
Anderson, C.W., Devulapalli, S.V., Stolz, E.A.: EEG signal classification with different signal representations. In: Girosi, F., Makhoul, J., Manolakos, E., Wilson, E. (eds.) Neural Networks for Signal Processing V, pp. 475–483. IEEE Service Center, Piscataway (1995)
Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific, Singapore (2002)
Neuroscan Edit4.5.American
Yang, J., Honavar, V.: Feature subset selection using a genetic algorithm. Intelligent Systems and their Applications, 44–49 (1998); Iowa State Univ.
Ren, J., Qiu, Z., Fan, W., Cheng, H., Yu, P.S.: Forward Semi-Supervised Feature Selection. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 970–976. Springer, Heidelberg (2008)
Bu, H., Zheng, S., Xia, J.: Genetic algorithm based Semi-feature selection method. In: 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, pp. 521–524 (2009)
Oh, I.S., Lee, J.S., Moon, B.R.: Hybrid genetic algorithms for feature selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(11), 1424–1437 (2004)
Pfurtscheller, G., Neuper, C., Flotzinger, D., Pregenzer, M.: EEG based discrimination between imagination of right and left hand movement. Electroenc. Clin. Neurophys 103(5), 1–10 (1997)
Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: Bci2000: a generalpurpose brain-computer interface (bci) system. IEEE Transactions on Biomedical Engeneering 51(6), 1034–1043 (2004)
Thulasidas, M., Guan, C., Wu, J.: Robust Classification of EEG Signal for Brain–Computer Interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(1) (2006)
Sun, S., Zhang, C., Zhang, D.: An experimental evaluation of ensemble methods for EEG signal classification. Pattern Recognition Letters 28, 2157–2163 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fang, C., Li, H., Ma, L. (2013). EEG Signal Classification Using the Event-Related Coherence and Genetic Algorithm. In: Liu, D., Alippi, C., Zhao, D., Hussain, A. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2013. Lecture Notes in Computer Science(), vol 7888. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38786-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-38786-9_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38785-2
Online ISBN: 978-3-642-38786-9
eBook Packages: Computer ScienceComputer Science (R0)