A study on temporal segmentation strategies for extracting common spatial patterns for brain computer interfacing

UKCI 2011 - Proceedings of the 11th UK Workshop on Computational Intelligence

Volumn , Issue , 2011, Pages 98-102

  Asensio Cubero, Javier ^a   Gan, John Q ^a   Palaniappan, Ramaswamy ^a  

^a UNIVERSITY OF ESSEX   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER CONTROL SYSTEMS;

BRAIN SIGNALS; BRAIN-COMPUTER INTERFACING; COMMON SPATIAL PATTERNS; COMPUTER COMMUNICATIONS; MOTOR IMAGERY; NEW APPROACHES; TEMPORAL SEGMENTATIONS; TIME SEGMENTATION;

BRAIN COMPUTER INTERFACE;

EID: 85031968661     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. A. Nijholt and D. Tan, “Brain-computer interfacing for intelligent systems,” IEEE Intelligent Systems, vol. 23, no. 3, pp. 72–79, 2008.
2. B. Allison, B. Graimann, and A. Grser, “Why use a BCI if you are healthy?” in BRAINPLAY 07 Brain-Computer Interfaces and Games Workshop at ACE (Advances in Computer Entertainment), 2007, p. 7.
3. R. Palaniappan, C. S. Syan, and R. Paramesran, “Current practices in Electroencephalogram-Based Brain-Computer interfaces,” Encyclopedia of Information Science and Technology, 2nd ed., vol. II, pp. 888–901, 2009.
4. G. Dornhege, Toward Brain-Computer Interfacing. The MIT Press, 2007.
5. C. Neuper, M. Wortz, and G. Pfurtscheller, “ERD/ERS patterns reflecting sensorimotor activation and deactivation,” Progress in Brain Research, vol. 159, pp. 211–222, 2006.
6. G. Pfurtscheller and F. H. Lopes da Silva, “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clinical Neurophysiology, vol. 110, no. 11, pp. 1842–1857, 1999.
7. J. Wang, L. Gao, H. Zhang, and J. Xu, “Adaboost with SVM-based classifier for the classification of brain motor imagery tasks,” Universal Access in Human-Computer Interaction. Users Diversity, pp. 629–634, 2011.
8. W. Y. Hsu, “EEG-based motor imagery classification using enhanced active segment selection and adaptive classifier,” Computers in Biology and Medicine, vol. 41, no. 8, pp. 633–639, 2011.
9. B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K. R. Muller, “Optimizing spatial filters for robust EEG single-trial analysis,” IEEE Signal Processing Magazine, vol. 25, no. 1, pp. 41–56, 2008.
10. N. F. nce, A. H. Tewfik, S. Arica, and S. Yagcioglu, “Analysis and visualization of movement related EEG activities using local discriminant bases,” in The Second International IEEE EMBS Conference on Neural Engineering, Washington, DC, USA, 2005.
11. Y. Wongsawat, S. Oraintara, and K. R. Rao, “Reduced complexity space-time-frequency model for multi-channel EEG and its applications,” in IEEE International Symposium on Circuits and Systems. IEEE, 2007, pp. 1305–1308.
12. K. Nazarpour, S. Sanei, L. Shoker, and J. A. Chambers, “Parallel space-time-frequency decomposition of EEG signals for brain computer interfacing,” in Proceedings of the 14th European Signal Processing Conference (EUSIPCO’06), 2006.
13. G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, and K. R. Muller, “Combined optimization of spatial and temporal filters for improving brain-computer interfacing,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 11, pp. 2274–2281, 2006.
14. K. K. Ang, Z. Y. Chin, H. Zhang, and C. Guan, “Filter bank common spatial pattern (FBCSP) in brain-computer interface,” in IEEE International Joint Conference on Neural Networks. IEEE, 2008, pp. 2390–2397.
15. E. A. Mousavi, J. J. Maller, P. B. Fitzgerald, and B. J. Lithgow, “Wavelet common spatial pattern in asynchronous offline brain computer interfaces,” Biomedical Signal Processing and Control, vol. 6, no. 2, pp. 121–128, 2010.
16. C. Brunner, R. Leeb, G. R. Muller-Putz, A. Schlogl, and G. Pfurtscheller, “BCI competition 2008Graz data set a,” 2009.
17. S. Wold, K. Esbensen, and P. Geladi, “Principal component analysis,” Chemometrics and Intelligent Laboratory Systems, vol. 2, no. 1-3, pp. 37–52, 1987.
18. P. Comon, “Independent component analysis, a new concept?” Signal Processing, vol. 36, no. 3, pp. 287–314, 1994.
19. L. Parra and P. Sajda, “Blind source separation via generalized eigenvalue decomposition,” The Journal of Machine Learning Research, vol. 4, pp. 1261–1269, 2003.
20. Z. J. Koles, “The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG,” Electroencephalography and Clinical Neurophysiology, vol. 79, no. 6, pp. 440–447, 1991.
21. B. Blankertz, K. R. Muller, D. J. Krusienski, G. Schalk, J. R. Wolpaw, A. Schlogl, G. Pfurtscheller, J. R. Millan, M. Schroder, and N. Birbaumer, “The BCI competition III: validating alternative approaches to actual BCI problems,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 153–159, 2006.
22. K. Fukunaga, Introduction To Statistical Pattern Recognition. Academic Press Professional, 1990.
23. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. wiley New York:, 2001, vol. 2.
24. J. Cohen, “A coefficient of agreement for nominal scales,” Educational and Psychological Measurement, vol. 20, no. 1, pp. 37–46, 1960.