Comparison of linear, nonlinear, and feature selection methods for EEG signal classification

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Volumn 11, Issue 2, 2003, Pages 141-144

  Garrett, Deon ^c   Peterson, David A ^a   Anderson, Charles W ^a   Thaut, Michael H ^b  

^a Colorado State University   (United States)

^b Department of Environmental and Radiological Health Sciences   (United States)

^c NONE

Author keywords

Brain computer interface (BCI); Electroencephalogram (EEG); Feature selection; Genetic algorithms (GA); Neural networks; Pattern classification; Support vector machines (SVM)

Indexed keywords

ELECTROENCEPHALOGRAPHY; GENETIC ALGORITHMS; HUMAN COMPUTER INTERACTION; NEURAL NETWORKS;

BRAIN-COMPUTER INTERFACES (BCI);

INTERFACES (COMPUTER);

ALGORITHM; ANALYTIC METHOD; ARTICLE; COMPARATIVE STUDY; DISCRIMINANT ANALYSIS; ELECTROENCEPHALOGRAPHY; LINEAR SYSTEM; MULTICHANNEL RECORDER; NERVE CELL NETWORK; NONLINEAR SYSTEM; PATTERN RECOGNITION; PRIORITY JOURNAL; SENSORIMOTOR CORTEX; TASK PERFORMANCE;

ALGORITHMS; BRAIN; COMPUTER SIMULATION; DISCRIMINANT ANALYSIS; ELECTROENCEPHALOGRAPHY; EVOKED POTENTIALS; FINGERS; HUMANS; LINEAR MODELS; MODELS, NEUROLOGICAL; MOVEMENT; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; PATTERN RECOGNITION, AUTOMATED; SIGNAL PROCESSING, COMPUTER-ASSISTED; THINKING; USER-COMPUTER INTERFACE;

EID: 0042354786     PISSN: 15344320     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNSRE.2003.814441     Document Type: Article

References (25)

1. C. W. Anderson, S. V. Devulapalli, and E. A. Stolz, "Determining mental state from EEG signals using neural networks," Sci. Program., vol. 4, no. 3, pp. 171-183, 1995.
2. C. Anderson and M. Kirby, "EEG subspace representations and feature selection for brain-computer interfaces," in Proc. 1st IEEE Workshop on Computer Vision and Pattern Recognition for Human-Computer Interaction (CVPRHCI), Madison, WI, 2003.
3. B. Blankertz, G. Curio, and K. R. Müller, "Classifying single trial EEG: Toward brain computer interfacing," in Advances in Neural Information Processing Systems, T. G. Dietterich, S. Becker, and Z. Ghahramani, Eds. Cambridge, MA: MIT Press, 2002, vol. 14.
4. B. E. Boser, I. M. Guyon, and V. Vapnik, "A training algorithm for optimal margin classifiers," in Proc. 5th Annu. Workshop on Computational Learning Theory, Pittsburgh, PA, 1992, pp. 144-152.
5. G. C. Cawley, MATLAB Support Vector Machine Toolbox. Norwich, Norfolk, U.K.: School of Inform. Syst., Univ. East Anglia, 2000.
6. R. Courant and D. Hilbert, Methods of Mathematical Physics. New York: Wiley Interscience, 1970, vol. I and II.
7. T. M. Cover, "Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition," IEEE Trans. Electron. Comput., vol. EC- 14, pp. 326-334, 1965.
8. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Berlin, Germany: Springer-Verlag, 2001.
9. H. Jasper, "The ten twenty electrode system of the international federation," Electroencephalogr. Clin. Neurophysiol., vol. 10, pp. 371-375, 1958.
10. T.-P. Jung, S. Makeig, C. Humphries, T.-W. Lee, M. J. McKeown, V. Iragui, and T. J. Sejnowski, "Removing electroencephalographic artifacts by blind source separation," Psychophysiol., vol. 37, pp. 168-178, 2000.
11. Z. A. Keirn, "Alternative modes of communication between man and machine," M.S. thesis, Purdue Univ., West Lafayette, IN, 1988.
12. Z. A. Keirn and J. I. Aunon, "A new mode of communication between man and his surroundings," IEEE Trans. Biomed. Eng., vol. 37, pp. 1209-1214, Dec. 1990.
13. M. Kirby and C. W. Anderson, "Geometric analysis for the characterization of nonstationary time-series," in Springer Applied Mathematical Sciences Series Celebratory Volume for the Occasion of the 70th Birthday of Larry Sirovich, E. Kaplan, J. Marsden, and K. R. Sreenivasan, Eds. Berlin, Germany: Springer-Verlag, 2003, ch. 8, pp. 263-292.
14. CynapSys LLC, Flexga, www.cynapsys.com (2002). [Online]
15. J. Ma, Y. Zhao, and S. Ahalt. (2002) OSU SVM Classifier Matlab Toolbox [Online] http://eewww.eng.ohio-state.edu/-maj/osu_svm/
16. J. Mercer, "Functions of positive and negative type, and their connection with the theory of integral equations," Trans. London Philosoph. Soc., vol. A 209, pp. 415-446, 1909.
17. G. Pfurtscheller, C. Neuper, C. Guger, W. Harkam, H. Ramoser, A. Schlogl, B. Obermaier, and M. Pregenzer, "Current trends in Graz brain-computer interface (BCI) research," IEEE Trans. Rehab. Eng., vol. 8, pp. 216-219, June 2000.
18. J. A. Pineda, B. Z. Allison, and A. Vankov, "The effects of self-movement, observation, and imagination on mu rhythms and readiness potentials: toward a brain-computer interface," IEEE Trans. Rehab. Eng., vol. 8, pp. 219-222, June 2000.
19. J. C. Platt, "Fast training of support vector machines using sequential minimal optimization," in Advances in Kernel Methods - Support Vector Learning, B. Scholkopf, C. Burges, and A. Smola, Eds. Cambridge, MA: MIT Press, 1998, pp. 185-208.
20. -, "Using analytic QP and sparseness to speed training of support vector machines," in Advances in Neural Information Processing Systems 11, M. S. Kearns, S.A. Solla, and D. A. Cohn, Eds. Cambridge, MA: MIT Press, 1999.
21. J. C. Platt, N. Nello Cristianini, and J. Shawe-Taylor, "Large margin DAG's for multiclass classification," in Advances in Neural Information Processing Systems 12, S. A. Solla, T. K. Leen, and K.-R. Müller, Eds. Cambridge, MA: MIT Press, 2000, pp. 547-553.
22. D. Whitley, R. Beveridge, C. Guerra, and C. Graves, "Messy genetic algorithms for subset feature selection," in Proc. Int. Conf. on Genetic Algorithms, T. Baeck, Ed., Boston, MA, 1997, pp. 568-575.
23. D. Whitley, "A genetic algorithm tutorial," Statist. Comput., vol. 4, pp. 65-85, 1994.
24. J. R. Wolpaw, D. J. McFarland, and T. M. Vaughan, "Brain-computer interface research at the wadsworth center," IEEE Trans. Rehab. Eng., vol. 8, pp. 222-226, June 2000.
25. J. Yang and V. Honavar, "Feature subset selection using a genetic algorithm," in Feature Extraction, Construction and Subset Selection: A Data Mining Perspective, H. Liu and H. Motoda, Eds. Boston, MA: Kluwer Academic, 1998, pp. 117-136.