The aim of this study was to design and compare methods for …

• Abstract
• Background
• Methods
• Results and Discussion
• Discussion
• Conclusion
• References
• Figures
• Tables

Biology Articles » Bioengineering » Evolutionary optimization of classifiers and features for single-trial EEG Discrimination » References

References

- Evolutionary optimization of classifiers and features for single-trial EEG Discrimination

1. Fisch BJ: Fisch & Spehlmann's EEG Primer; Basic Principles of Digital and Analog EEG. third edition. The Netherlands: Elsevier Science; 1999.

2. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM: Brain-computer interfaces for communication and control.

   Clinical Neurophysiology 2002, 113(6):767-791.

3. Birbaumer N: Breaking the silence: Brain-computer interfaces (BCI) for communication and motor control.

   Psychophysiology 2006, 43(6):517-32.

4. Müller-Putz GR, Scherer R, Pfurtscheller G, Rupp R: EEG-based neuroprosthesis control: A step towards clinical practice.

   Neuroscience Letters 2005, 382:169-174.

5. Li Y, Gao X, Liu H, Gao S: Classification of single-trial electroencephalogram during finger movement.

   IEEE Transactions on biomedical engineering 2004, 51(6):1019-1025.

6. Obermaier B, Guger C, Neuper C, Pfurtscheller G: Hidden Markov models for online classification of single trial EEG data.

   Pattern Recognition Letters 2001, 22(12):1299-1309.

7. Costa EJX, Cabral EF Jr: EEG-based discrimination between imagination of left and right hand movements using adaptive gaussian representation.

   Medical Engineering & Physics 2000, 22:345-348.

8. Beisteiner R, Hollinger G, Lindinger WL, Berthoz A: Mental representations of movements. Brain potentials associated with imagination of hand movements.

   Electroencephalography and clinical Neurophysiology 1995, 96:183-193.

9. Rothwell J: Control of Human Voluntary Movement. second edition. London: Chapman and Hill; 1994.

10. Millán J, Franze M, Mouriño J, Cincotti F, Babiloni F: Relevant EEG features for the classification of spontaneous motor-related tasks.

    Biological Cybernetics 2002, 86:89-95.

11. Graimann B, Huggins JE, Levine SP, Pfurtscheller G: Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis.

    IEEE Transactions on Biomedical Engineering 2004, 51(6):954-62.

12. Yom-Tov E, Inbar GF: Feature selection for the classification of movements from single movement-related potentials.

    IEEE Transactions on Neural Systems and Rehabilitation Engineering 2002, 10(3):170-177.

13. Bellman RE: Adaptive control processes. Princeton, NJ: Princeton University Press; 1961.

14. Blum A, Langley P: Selection of relevant features and examples in machine learning.

    Artificial Intelligence 1997, 97(1–2):245-271.

15. Kohavi R, John GH: Wrappers for feature subset selection.

    Artificial Intelligence 1997, 97(1–2):273-324.

16. Yang J, Honavar V: Feature subset selection using a genetic algorithm.

    Intelligent Systems and Their Applications 1998, 13(2):44-49.

17. Dijck GV, Hulle MMV, Wevers M: Genetic algorithm for feature subset selection with exploitation of feature correlations from continuous wavelet transform: a real-case application.

    International Journal of Computational Intelligence 2004, 1(4):308-312.

18. Yao X: Evolving artificial neural networks.

    Proceedings of the IEEE 1999, 87:1423-1447.

19. Stanley KO, Miikkulainen R: Evolving neural networks through augmenting topologies.

    Evol Comput 2002, 10(2):99-127.

20. Haykin S: Neural networks, a comprehensive foundation. New Jersey, NY, USA: Prentice Hall; 1999.

21. Wahde M: Improving the prediction of the clinical outcome of breast cancer using evolutionary algorithms.

    Soft Computing 2006, 10:338-345.

22. Åberg M, Wessberg J: Single-trial EEG discrimination using wavelets, artificial neural networks and evolutionary algorithms.

    Society for Neuroscience 35th Annual Meeting, Washington DC, USA, No. 520.4 2005.

23. Trejo L, Shensa M: Feature extraction of event-related potentials using wavelets: An application to human performance monitoring.

    Brain and Language 1999, 66:89-107(19).

24. Walnut DF: An introduction to wavelet analysis. Boston, MA, USA: Birkhäuser; 2002.

25. Laubach M: Wavelet-based processing of neuronal spike trains prior to discriminant analysis.

    Journal of Neuroscience Methods 2003, 134(2):159-168.

26. Reeves CR, Rowe JE: Genetic algorithms: Principles and perspectives: A guide to GA theory. Norwell, MA, USA: Kluwer Academic Publishers; 2002.

27. EEGLAB [http://www.sccn.ucsd.edu/eeglab/]

28. Siegel S, Castellan JN Jr: Nonparametric Statistics for the Behavioral Sciences. second edition. New York: McGraw-Hill; 1988.

29. Müller K, Anderson CW, Birch GE: Linear and nonlinear methods for brain-computer interfaces.

    IEEE Trans Neural Syst Rehabil Eng 2003, 11(2):165-9.

30. Garrett D, Peterson D, Anderson C, Thaut M: Comparison of linear, nonlinear, and feature selection methods for EEG signal classification.

    IEEE Trans Neural Syst Rehabil Eng 2003, 11(2):141-4.

31. Brodal P: The central nervous system: structure and function. third edition. Oxford University Press Inc; 2003.