Classification of Single-Trial EEG Based on Support Vector Clustering during Finger Movement

1. Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-Computer Interface for Communication and Control. Journal of Clinical Neurophysiology 113, 767–791 (2002)

   Article  Google Scholar 

2. Blankertz, B., et al.: The BCI Competition III: Validating Alternative Approaches to Actual BCI Problems. IEEE Transaction on Neural System and Rehabilitation Engineering 14, 153–159 (2006)

   Article  Google Scholar 

3. Lehtonen, J., Jylänki, P., Kauhanen, L., Sams, M.: Online Classification of the Single EEG Trials During Finger Movements. IEEE Transactions on Biomedical Engineering 55(2), 713–720 (2008)

   Article  Google Scholar 

4. Wang, Y., et al.: BCI Competition 2003-Data Set IV: An algorithm based on CSSD and FDA for Classifying Single-Trial EEG. IEEE Transactions on Biomedical Engineering 51(6), 1081–1086 (2004)

   Article  Google Scholar 

5. Li, Y., Gao, X., Liu, H., Gao, S.: Classification of Single-Trial Electroencephalogram During Finger Movement. IEEE Transactions on Biomedical Engineering 51(6), 1019–1025 (2004)

   Article  Google Scholar 

6. Li, Y., et al.: Single Trial EEG Classification During Finger Movement Task by Using Hidden Markov Models. In: 2nd International IEEE EMBS Conference on Neural Engineering, Arlington, Virginia, pp. 625–628 (2005)

   Google Scholar 

7. Ranaweera, R.D., Talavage, T.M., Krishnan, A.: Time-frequency Features Differentiate Direction of Finger Movement in Cued and Self-paced Tasks. In: 2nd International IEEE EMBS Conference on Neural Engineering, Arlington, Virginia, pp. 551–554 (2005)

   Google Scholar 

8. Pfurtscheller, G., Neuper, C., Flotzinger, D., Pregenzer, M.: EEG-Based Discrimination Between Imagination of Right and Left Hand Movement. Journal of Electro-encephalography and Clinical Neurophysiology 103, 642–651 (1997)

   Article  Google Scholar 

9. Blankertz, B., et al.: Boosting Bit Rates and Error Detection for the Classification of Fast-Paced Motor Commands Based on Single-Trial EEG Analysis. IEEE Transactions on Neural System and Rehabilitation Engineering 11(2), 127–131 (2003)

   Article  Google Scholar 

10. Wang, Y., Berg, P., Scherg, M.: Common Spatial Subspace Decomposition Applied to Analysis of Brain Responses Under Multiple Task Conditions: A Simulation Study. Journal of Clinical Neurophysiology 110, 604–614 (1999)

    Article  Google Scholar 

11. Bashashati, A., Fatourechi, M., Ward, R.K.: A Survey of Signal Processing Algorithms in Brain-Computer Interfaces Based on Electrical Brain Signals. Journal of Neural Engineering 4(2), R32–R57 (2007)

    Article  Google Scholar 

12. Shibasaki, H., Hallett, M.: What is the Bereitschaftspotential? Journal of Clinical Neurophysiology 117, 2341–2356 (2006)

    Article  Google Scholar 

13. Müller-Gerking, J., Pfurtscheller, G., Flyvbjerg, H.: Designing Optimal Spatial Filters for Single-Trial EEG Classification in a Movement Task. Journal of Clinical Neuro-physiology 110, 787–798 (1999)

    Google Scholar 

14. Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, San Diego (1990)

    MATH  Google Scholar 

15. Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: Support Vector Clustering. Journal of Machine Learning Research 2, 125–137 (2001)

    MATH  Google Scholar 

16. Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: A Support Vector Clustering Method. In: 15th International Conference on Pattern Recognition, Barcelona, pp. 724–727 (2000)

    Google Scholar 

17. Xu, R., Wunsch II, D.: Survey of Clustering Algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)

    Article  Google Scholar 

18. Blankertz, B., Curio, G., Müller, K.-R.: Classifying Single Trial EEG: Towards Brain Computer Interfacing. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems (NIPS 2001), vol. 14, pp. 157–164 (2001)

    Google Scholar 

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