Blind source separation of underdetermined mixtures of event-related sources

Signal Processing

Volumn 101, Issue , 2014, Pages 52-64

  Niknazar, Mohammad ^a   Becker, Hanna ^a,b   Rivet, Bertrand ^a   Jutten, Christian ^a,c   Comon, Pierre ^a  

^a UNIV GRENOBLE ALPES   (France)

^b UNIVERSITY OF NICE SOPHIA ANTIPOLIS   (France)

^c INSTITUT UNIVERSITAIRE DE FRANCE   (France)

Author keywords

Event related source; Extended Kalman filtering; Fetal ECG extraction; Quasi periodic source; Robust tensor decomposition; Underdetermined mixtures

Indexed keywords

EID: 84894682018     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2014.01.031     Document Type: Article

References (39)

1. P. Comon, and C. Jutten Handbook of Blind Source Separation Independent Component Analysis and Applications 2010 Elsevier Science
2. A. Ozerov, and C. Févotte Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation IEEE Trans. Audio Speech Lang. Process. 18 3 2010 550 563
3. A.Y. Kibangou, and G. Favier Blind equalization of nonlinear channels using a tensor decomposition with code/space/time diversities Signal Process. 89 2 2009 133 143
4. H. Lee, Y.-D. Kim, A. Cichocki, and S. Choi Nonnegative tensor factorization for continuous eeg classification Int. J. Neural Syst. 17 04 2007 305 317
5. D. Letexier, S. Bourennane, and J. Talon Nonorthogonal tensor matricization for hyperspectral image filtering IEEE Geosci. Remote Sens. Lett. 5 1 2008 3 7
6. P. Comon Blind identification and source separation in 2×3 under-determined mixtures IEEE Trans. Signal Process. 52 1 2004 11 22
7. R. Gribonval, S. Lesage, et al., A survey of sparse component analysis for blind source separation: principles, perspectives, and new challenges, in: ESANN'06 Proceedings-14th European Symposium on Artificial Neural Networks, 2006, pp. 323-330.
8. Z. He, S. Xie, S. Ding, and A. Cichocki Convolutive blind source separation in the frequency domain based on sparse representation IEEE Trans. Audio Speech Lang. Process. 15 5 2007 1551 1563
9. P. Georgiev, F. Theis, and A. Cichocki Sparse component analysis and blind source separation of underdetermined mixtures 16 4 2005 992 996
10. P. Tichavsky, and Z. Koldovsky Weight adjusted tensor method for blind separation of underdetermined mixtures of nonstationary sources IEEE Trans. Signal Process. 59 3 2011 1037 1047
11. P. Comon Contrasts, independent component analysis, and blind deconvolution Int. J. Adapt. Control Signal Process. 18 3 2004 225 243
12. A. Cichocki, R. Zdunek, A.H. Phan, S.-I. Amari, Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation, Wiley.com, 2009.
13. G. Zhou, and A. Cichocki Canonical polyadic decomposition based on a single mode blind source separation IEEE Signal Process. Lett. 19 8 2012 523 526
14. P. Chevalier, A. Ferreol, L. Albera, On the behavior of current second order blind source separation methods for first and second order cyclostationary sources-application to CPFSK sources, in: 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 3, 2002, pp. III-3081-III-3084.
15. A. Almeida, P. Comon, X. Luciani, Deterministic blind separation of sources having different symbol rates using tensor-based parallel deflation, in: Proceedings of the 9th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA'10, Springer-Verlag, Berlin, Heidelberg, 2010, pp. 362-369.
16. P. Comon, L. Luciani, and A.L.F.D. Almeida Tensor decompositions, alternating least squares and other tales J. Chemom. 23 2009 393 405
17. R. Bro, Multi-way analysis in the food industry: Models, algorithms and applications (Ph.D. thesis), University of Amsterdam, NL, 1998.
18. S.A. Vorobyov, Y. Rong, N.C. Sidiropoulos, and A.B. Gershman Robust iterative fitting of multilinear models IEEE Trans. Signal Process. 53 8 2005 2678 2689
19. J. Möcks Decomposing event-related potentials a new topographic components model Biol. Psychol. 26 1988 199 215
20. J. Möcks Topographic components model for event-related potentials and some biophysical considerations IEEE Trans. Biomed. Eng. 35 6 1988 482 484
21. A. Field, and D. Graupe Topographic component (parallel factor) analysis of multichannel evoked potentials practical issues in trilinear spatiotemporal decomposition Brain Topogr. 3 4 1991 407 423
22. L. de Lathauwer, B. de Moor, and J. Vandewalle Fetal electrocardiogram extraction by blind source subspace separation IEEE Trans. Biomed. Eng. 47 5 2000 567 572
23. J.-F. Cardoso, Multidimensional Independent Component Analysis, in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-98), vol. 4, 1998, pp. 1941-1944.
24. A.G. Favret Computer matched filter location of fetal R-waves Med. Biol. Eng. 6 5 1968 467 475
25. B. Widrow, J. Glover Jr., J. McCool, J. Kaunitz, C. Williams, R. Hearn, J. Zeidler, J. Eugene Dong, R. Goodlin, Adaptive noise cancelling: principles and applications, Proc. IEEE 63 (12) (1975) 1692-1716.
26. R. Sameni, C. Jutten, and M.B. Shamsollahi Multichannel electrocardiogram decomposition using periodic component analysis IEEE Trans. Biomed. Eng. 55 8 2008 1935 1940
27. M. Richter, T. Schreiber, and D. Kaplan Fetal ECG extraction with nonlinear state-space projections IEEE Trans. Biomed. Eng. 45 1 1998 133 137
28. J.B. Kruskal Three-way arrays rank and uniqueness of trilinear decompositions with application to arithmetic complexity and statistics Linear Algebra Appl. 18 1977 95 138
29. N.D. Sidiropoulos, G.B. Giannakis, and R. Bro Blind PARAFAC receivers for DS-CDMA systems IEEE Trans. Signal Process. 48 3 2000 810 822
30. T.G. Kolda, and B.W. Bader Tensor decompositions and applications SIAM Rev. 51 3 2009 455 500
31. P.J. Huber, and E.M. Ronchetti Robust Statistics 2009 John Wiley & Sons, Hoboken NJ 105 124 (Chapter: Scale estimates)
32. M.S. Grewal, and A.P. Andrews Kalman Filtering Theory and Practice using MATLAB 2011 Wiley, IEEE Press
33. A. Hyvarinen Fast and robust fixed-point algorithms for independent component analysis IEEE Trans. Neural Netw. 10 3 1999 626 634
34. S. Georgiadis, P. Ranta-aho, M. Tarvainen, and P. Karjalainen Single-trial dynamical estimation of event-related potentials a Kalman filter-based approach IEEE Trans. Biomed. Eng. 52 8 2005 1397 1406
35. H. Mohseni, E. Wilding, S. Sanei, Preprocrssing of event-related potential signals via Kalman filtering and smoothing, in: 2007 15th International Conference on Digital Signal Processing, 2007, pp. 179-182.
36. M.L. Jongsma, T. Eichele, C.M.V. Rijn, A.M. Coenen, K. Hugdahl, H. Nordby, and R.Q. Quiroga Tracking pattern learning with single-trial event-related potentials Clin. Neurophysiol. 117 9 2006 1957 1973
37. W. Deburchgraeve, P.J. Cherian, M.D. Vos, R.M. Swarte, J.H. Blok, G.H. Visser, and P. Govaert Neonatal seizure localization using parafac decomposition Clin. Neurophysiol. 120 2009 1787 1796
38. J.R. Wolpaw, N. Birbaumer, W.J. Heetderks, D.J. McFarland, P.H. Peckham, G. Schalk, E. Donchin, L.A. Quatrano, C.J. Robinson, and T.M. Vaughan et al. Brain-computer interface technology a review of the first international meeting IEEE Trans. Rehabil. Eng. 8 2 2000 164 173
39. B. De Moor, P. De Gersem, B. De Schutter, and W. Favoreel DAISY a database for identification of systems Journal A 38 3 1997 4 5 (Special Issue on CACSD (Computer Aided Control Systems Design))