Principal component based covariate shift adaption to reduce non-stationarity in a MEG-based brain-computer interface

Eurasip Journal on Advances in Signal Processing

Volumn 2012, Issue 1, 2012, Pages

  Spüler, Martin ^a   Rosenstiel, Wolfgang ^a   Bogdan, Martin ^a  

^a UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

BCI; Covariate shift adaption; Non stationarity; PCA

Indexed keywords

BCI; COVARIATE SHIFTS; NON-STATIONARITIES; OFF-LINE ANALYSIS; ONLINE EXPERIMENT; PCA; PRINCIPAL COMPONENTS; RECORDED SIGNALS;

BRAIN COMPUTER INTERFACE;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84872926988     PISSN: 16876172     EISSN: 16876180     Source Type: Journal    
DOI: 10.1186/1687-6180-2012-129     Document Type: Article

References (21)

1. A Kübler, F Nijboer, J Mellinger, T Vaughan, H Pawelzik, G Schalk, D McFarland, N Birbaumer, J Wolpaw, Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology. 64(10), 1775 (2005)
2. J Mellinger, G Schalk, C Braun, H Preissl, W Rosenstiel, N Birbaumer, A Kübler, An MEG-based brain-computer interface (BCI). NeuroImage. 36(3), 581 (2007)
3. M Sugiyama, M Krauledat, KR Müller, Covariate shift adaptation by Importance Weighted Cross Validation. J. Mach. Learn. Res. 8, 985 (2007). http://dl.acm.org/citation. cfm?id=1314498.1390324
4. Y Li, H Kambara, Y Koike, M Sugiyama, Application of covariate shift adaption techniques in brain computer interfaces. IEEE Transactions on Biomedical Engineering. 57, 1318 (2010)
5. C Vidaurre, M Kawanabe, P von Bünau, B Blankertz, K Müller, Toward unsupervised adaptation of LDA for Brain-Computer interfaces. Biomedical Engineering, IEEE Transactions on. 58(3), 587 (2011). doi:10.1109/TBME.2010. 2093133
6. R Tomioka, J Hill, B Blankertz, K Aihara, Adapting spatial filter methods for nonstationary BCIs. in IBIS 2006 (Max-Planck-Gesellschaft, 2006), 65-70
7. Q Zhao, L Zhang, A Cichocki, J Li, Incremental common spatial pattern algorithm for BCI. in Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on, 2656-2659, (2008). doi:10.1109/IJCNN.2008.4634170
8. H Ramoser, JR Wolpaw, G Pfurtscheller, Biomedizinische technik. Biomedical Engineering. 42(9), 226 (1997)
9. GE Fabiani, DJ McFarland, JR Wolpaw, G Pfurtscheller, Conversion of EEG activity into cursor movement by a brain-computer interface (BCI). Neural Systems and Rehabilitation Engineering, IEEE Transactions on. 12(3), 331 (2004). doi:10.1109/TNSRE.2004.834627 (Pubitemid 39223614)
10. B Reuderink, J Farquhar, M Poel, A Nijholt, A subject-independent brain-computer interface based on smoothed, second-order baselining. in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, 4600-4604, (2011). doi:10.1109/IEMBS.2011.6091139
11. B Reuderink, J Farquhar, M Poel, Slow sphering to suppress Non-Stationaries in the EEG. International Journal of Bioelectromagnetism. 13(2), 78 (2011)
12. IT Jolliffe, Principal Component Analysis. (New York: Springer-Verlag, 1986)
13. A Satti, C Guan, D Coyle, G Prasad, A covariance shift minimization method to alleviate Non-Stationary effects for an adaptive Brain-Computer interface. in Proceedings of the 20th International Conference on Pattern Recognition, 105-108, (2010)
14. M Bensch, J Mellinger, M Bogdan, W Rosenstiel, A multiclass BCI using MEG. in Proceedings of the 4th Int. Brain-Computer Interface Workshop (Graz, 2008), 191-196
15. M Spüler, W Rosenstiel, M Bogdan, A fast feature selection method for high-dimensional MEG BCI data. in Proceedings of the 5th Int. Brain-Computer Interface Conference (Graz, 2011), 24-27
16. CC Chang, CJ Lin, LIBSVM: a library for support vector machines, (2001). Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm
17. G Schalk, DJ Mcfarland, T Hinterberger, N Birbaumer, JR Wolpaw, BCI2000: A General-Purpose Brain-Computer Interface (BCI) system. IEEE Transactions on Biomedical Engineering. 51(6), 1034 (2004). doi:10.1109/TBME.2004.827072. http://dx.doi.org/10.1109/TBME.2004. 827072
18. J Pascual, M Kawanabe, C Vidaurre, Modelling Non-stationarities in EEG Data with robust principal component analysis. in Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science, vol. 6679, ed. by Corchado E, Kurzynski M, and Wozniak M: Springer Berlin/Heidelberg, 51-58, 2011)
19. M Hubert, PJ Rousseeuw, ROBPCA: A new approach to robust principal component analysis. Technometrics. 47(1), 64 (2005). http://www.jstor.org/ stable/25470935
20. P von Bünau, FC Meinecke, FC Király, KR Müller, Finding stationary subspaces in multivariate time series. Physical Review Letters. 103(21), 214101 (2009). doi:10.1103/PhysRevLett.103.214101
21. W Klimesch, EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Reviews. 29(2-3), 169 (1999). doi:10.1016/S0165-0173 (98) 00056-3. http://dx.doi.org/10.1016/S0165- 0173(98)00056-3 (Pubitemid 29191338)