On-line EEG classification for brain-computer interface based on CSP and SVM

Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010

Volumn 9, Issue , 2010, Pages 4105-4108

  Sun, Hongyu ^a,b   Xiang, Yang ^a   Sun, Yaoru ^a   Zhu, Huaping ^c   Zeng, Jinhua ^c  

^a TONGJI UNIVERSITY   (China)

^b SHANDONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^c NONE

Author keywords

CSP; Motor imaginary; On line classification; SVM

Indexed keywords

CLASSIFICATION RATES; CLASSIFICATION RESULTS; COMMON SPATIAL PATTERNS; COMPUTER GAME; CONTROL SIGNAL; CSP; EEG CLASSIFICATION; INPUT SIGNAL; MENTAL TASKS; ON-LINE CLASSIFICATION; SVM;

ELECTROENCEPHALOGRAPHY; FEATURE EXTRACTION; INTERFACES (COMPUTER); OPTICAL SENSORS; SIGNAL PROCESSING;

BRAIN COMPUTER INTERFACE;

EID: 78650527037     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CISP.2010.5648081     Document Type: Conference Paper

References (14)

1. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, Brain-computer interfaces for communication and control, Clinical Neurophysiology, vol. 113, pp. 767-791, 2002.
2. J. R. Wolpaw, Brain-Computer Interfaces (BCIs) for Communication and Control: Current Status, in 2nd Int. BCI Workshop & Training Course, pp. 29-32 , 2004.
3. J. R. Wolpaw and D. J. McFarland, Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans, Proceedings of the National Academy of Sciences (PNAS), vol. 101,no. 51, pp. 17849-17854, 2004.
4. G. Pfurtscheller and C. Neuper, Motor imaginary and direct brain computer communication, Proceedings of the IEEE, vol. 89, no.7, pp. 1123-1134, 2001.
5. Bunce S.C., Izzetoglu M., Izzetoglu, K., Onaral, B., and Pourrezaei, K., Functional near-infrared spectroscopy, IEEE Eng. Medicine and Biology Magazine vol.25, no.4, pp. 54-62,2006.
6. E. Abbas, G. Mahdi, EEG signals can be used to detect the voluntary hand movements by using an enhanced resource-allocating neural network engineering in medicine and biology society, Proc. IEEE Int. Conf. Ann. vol.1, pp.721-724, 2001.
7. D. Garrett, A.P. David, C.W. Anderson, M.H. Thaut, Comparison of linear, nonlinear, and feature selection methods for EEG signal classification, IEEE Trans. Rehab. Eng. vol.11, no.2, pp.141-144,2003.
8. G. Pfurtscheller, J. Kalcher, C. Neuper, D. Flotzinger, M. Pregenzer, On-line EEG classification during externally-placed hand movements using a neural network-based classifier, Electroen. Clin. Neurophysiol. vol. 99, pp. 416-425, 1996.
9. Alvaro Fuentes Cabrera, Dario Farina, Kim Dremstrup, Comparison of feature selection and classification methods for a brain-computer interface driven by non-motor imaginary. Med Biol Eng Comput vol. 48, pp.123-132,2010.
10. G. Blanchard and B. Blankertz, BCI competition 2003 - Data set IIa: Spatial patterns of self-controlled brain rhythm modulations, IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1062-1066, 2004.
11. H. Ramoser, J. Müller-Gerking, and G. Pfurtscheller, Optimal spatial filtering of single trial EEG during imagined hand movement, IEEE Trans. Rehabil. Eng., vol. 8, no. 4, pp. 441-446, 2000.
12. G. Dornhege, B. Blankertz, G. Curio, and K.-R. Müller, Boosting bit rates in non-invasive EEG single-trial classifications by feature combination and multi-class paradigms. IEEE Transactions on Biomedical Engineering, vol.51, no.6, pp.993-1002, 2004.
13. Chun Sing Louis Tsui, John Q. Gan and Stephen J. Roberts. A self-paced brain-computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training. Medical and Biological Engineering and Computing, Vol.47, no.3 , pp.257-265, 2009.
14. A. J. Smola, B. Schölkopf, R. C. Williamson, and P. L. Bartlett, New support vector algorithms, Neural Computation, vol. 12, no. 5, pp. 1207-1245, 2000.