An expectation-maximization method for spatio-temporal blind source separation using an AR-MOG source model

IEEE Transactions on Neural Networks

Volumn 19, Issue 3, 2008, Pages 508-519

  Hild II, Kenneth E ^a   Attias, Hagai T ^b   Nagarajan, Srikantan S ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b Golden Metallic Inc   (United States)

Author keywords

Blind source separation (BSS); Expectation maximization (EM); Independent components analysis (ICA); Maximum likelihood (ML)

Indexed keywords

INDEPENDENT COMPONENT ANALYSIS; MAGNETOCARDIOGRAPHY; MAXIMUM LIKELIHOOD; OPTIMIZATION;

AUTOREGRESSIVE PROCESS; EXPECTATION-MAXIMIZATION; MAGNETOCARDIOGRAPHIC DATA;

BLIND SOURCE SEPARATION;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; FETUS; HEART RATE; HUMAN; INFORMATION RETRIEVAL; MAGNETOCARDIOGRAPHY; METHODOLOGY; PHYSIOLOGY; POISSON DISTRIBUTION; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; FETUS; HEART RATE; HUMANS; INFORMATION STORAGE AND RETRIEVAL; LIKELIHOOD FUNCTIONS; MAGNETOCARDIOGRAPHY; NEURAL NETWORKS (COMPUTER); POISSON DISTRIBUTION; PRINCIPAL COMPONENT ANALYSIS;

EID: 40949084740     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2007.914154     Document Type: Article

References (72)

1. R. N. Vigario, "Extraction of ocular artefacts from EEG using independent component analysis," Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 395-404, Sep. 1997.
2. S. S. Nagarajan, H. T. Attias, K. E. Hild, II, and K. Sekihara, "A graphical model for estimating stimulus-evoked brain responses from MEG data with large background brain activity," NeuroImage, vol. 30, no. 2, pp. 400-416, Apr. 2006.
3. J. Cao, N. Murata, S. Amari, A. Cichocki, and T. Takeda, "A robust approach to independent component analysis of signals with high-level noise measurements," IEEE Trans. Neural Netw., vol. 14, no. 3, pp. 631-645, May 2003.
4. S. Comani, D. Mantini, G. Alleva, S. Di Luzio, and G. L. Romani, "Fetal magnetocardiographic mapping using independent component analysis," Physiol. Meas., vol. 25, no. 6, pp. 1459-1472, Dec. 2004.
5. S. Van Gerven and D. Van Compernolle, "Signal separation by symmetric adaptive decorrelation: Stability, convergence, and uniqueness," IEEE Trans. Signal Process., vol. 43, no. 7, pp. 1602-1612, Jul. 1995.
6. E. Weinstein, M. Feder, and A. Oppenheim, "Multi-channel signal separation by decorrelation," IEEE Trans. Speech Audio Process., vol. 1, no. 4, pp. 405-413, Oct. 1993.
7. H.-C. Wu and J. C. Principe, "A unifying criterion for blind source separation and decorrelation: Simultaneous diagonalization of correlation matrices," Neural Netw. Signal Process., pp. 496-505, Sep. 1997.
8. Z. Koldovsky, P. Tichavsky, and E. Oja, "Efficient variant of algorithm FastICA for independent component analysis attaining the Cramer-Rao lower bound," IEEE Trans. Neural Netw., vol. 17, no. 5, pp. 1265-1277, Sep. 2006.
9. R. Boscolo, H. Pan, and V. P. Roychowdhury, "Independent component analysis based on nonparametric density estimation," IEEE Trans. Neural Netw., vol. 15, no. 1, pp. 55-65, Jan. 2004.
10. A. J. Bell and T. J. Sejnowski, "An information-maximization approach to blind separation and blind deconvolution," Neural Comput., vol. 7, no. 6, pp. 1129-1159, Nov. 1995.
11. TD: Combining higher-order statistics and temporal information for blind source separation (with noise)," in Proc. Int. Workshop Independent Component Anal Signal Separat., Jan. 1999, pp. 87-92.
12. K. E. Hild, II, D. Erdogmus, and J. C. Principe, "An analysis of entropy estimators for blind source separation," Signal Process., vol. 86, no. 1, pp. 182-194, Jan. 2006.
13. C. Bishop, Neural Networks for Pattern Recognition. Oxford, U.K.: Oxford Univ. Press, 1995.
14. E. Moulines, J.-F. Cardoso, and E. Gassiat, "Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models," in Proc. Int. Conf. Acoust. Speech Signal Process., Apr. 1997, vol. 5, pp. 3617-3620.
15. D. T. Pham and P. Garat, "Blind separation of mixture of independent sources through a quasi-maximum likelihood approach," IEEE Trans. Signal Process., vol. 45, no. 7, pp. 1712-1725, Jul. 1997.
16. B. A. Pearlmutter and L. C. Parra, "Maximum likelihood blind source separation: A context-sensitive generalization of ICA," Adv. Neural Inf. Process. Syst., pp. 613-619, Dec. 1996.
17. S. Hosseini, C. Jutten, and D. T. Pham, "Markovian source separation," IEEE Trans. Signal Process., vol. 51, no. 12, pp. 3009-3019, Dec. 2003.
18. A. Delorme and S. Makeig, "EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis," J. Neurosci. Methods, vol. 134, no. 1, pp. 9-21, Mar. 2004.
19. B. A. Pearlmutter and L. C. Parra, "A context-sensitive generalization of ICA," in Proc. Int. Conf. Neural Inf. Process., Sep. 1996, pp. 151-157.
20. A. Hyvarinen, "A unifying model for blind separation of independent sources," Signal Process., vol. 85, no. 7, pp. 1419-1427, Jul. 2005.
21. H. Attias, "Independent factor analysis with temporally structured sources," Adv. Neural Inf. Process. Syst., pp. 386-392, Nov. 2000.
22. M. Dyrholm and L. K. Hansen, "CICAAR: Convolutive ICA with an auto-regressive inverse model," in Proc. Int. Workshop Independent Component Anal. Signal Separat., Sep. 2004, pp. 594-601.
23. H. Attias, "Blind source separation by dynamical graphical models," in Proc. Int. Conf. Artif. Neural Netw., Sep. 1999, pp. 126-131.
24. R. A. Choudrey and S. J. Roberts, "Bayesian ICA with Hidden Markov model sources," in Proc. Int. Workshop Independent Component Anal. Signal Separat., Apr. 2003, pp. 809-814.
25. H. Snoussi and A. Mohammad-Djafari, "Bayesian unsupervised learning for source separation with mixture of Gaussians prior," J. VLSI Signal Process. Syst., vol. 37, no. 2/3, pp. 263-279, Jun./Jul. 2004.
26. W. D. Penny, R. M. Everson, and S. J. Roberts, M. Girolami, Ed., "Hidden Markov independent components analysis," in Advances in Independent Component Analysis. Berlin, Germany: Springer-Verlag, 2000, pp. 3-22.
27. W. D. Penny, S. J. Roberts, and R. M. Everson, "Hidden Markov independent components for biosignal analysis," in Proc. Int. Conf. Med. Signal Inf. Process., Sep. 2000, pp. 244-250.
28. L. B. Jackson, Signals and Systems. Boston, MA: Addison-Wesley, 1991.
29. Z. Roth and Y. Baram, "Multidimensional density shaping by sigmoids," IEEE Trans. Neural Netw., vol. 7, no. 5, pp. 1291-1298, Sep. 1996.
30. S. Fiori and P. Bucciarelli, "Probability density estimation using adaptive activation function neurons," Neural Process. Lett., vol. 13, no. 1, pp. 31-42, Feb. 2001.
31. S. Fiori, "Hybrid independent component analysis by adaptive LUT activation function neurons," Neural Netw., vol. 15, no. 1, pp. 85-94, Jan. 2002.
32. T. W. Lee and R. Orglmeister, "A contextual blind separation of delayed and convolved sources," in Proc. Int. Conf. Acoust. Speech Signal Process., Apr. 1997, pp. 1199-1202.
33. N. Vlassis and Y. Motomura, "Efficient source adaptivity in independent component analysis," IEEE Trans. Neural Netw., vol. 12, no. 3, pp. 559-566, May 2001.
34. P. Comon, "Independent component analysis, a new concept?," Signal Process., vol. 36, no. 3, pp. 287-314, Apr. 1994.
35. H. H. Yang and S.-I. Amari, "Adaptive online learning algorithms for blind separation: Maximum entropy and minimum mutual information," Neural Comput., vol. 9, no. 7, pp. 1457-1482, Oct. 1997.
36. M. Girolami and C. Fyfe, "Generalised independent component analysis through unsupervised learning with Bussgang properties," in Proc. Int. Conf. Neural Netw., Jun. 1997, pp. 1788-1791.
37. T.-W. Lee, M. Girolami, and T. J. Sejnowski, "Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources," Neural Comput., vol. 11, no. 2, pp. 417-441, Feb. 1999.
38. H. Liang and N. K. Logothetis, "Adaptive independent component analysis through a maximum likelihood approach," in Proc. Int. Conf. Neural Netw. Brain, Oct. 1998, pp. 353-356.
39. L. Xu, C. C. Cheung, and S.-I. Amari, "Learned parametric mixture based ICA algorithm," Neurocomputing, vol. 22, no. 1-3, pp. 69-80, Jul. 1998.
40. S. Choi, A. Cichocki, and S.-I. Amari, "Flexible independent component analysis," J. VLSI Signal Process. Syst., vol. 26, no. 1-2, pp. 25-38, Aug. 2000.
41. H. Liang, "Adaptive independent component analysis of multichannel electrogastrograms," Med. Eng. Phys., vol. 23, no. 2, pp. 91-97, Mar. 2001.
42. M. M. Ichir and A. Mohammad-Djafari, "A mean field approximation approach to blind source separation with Lp priors," Eusipco, Sep. 2005.
43. L. Xu, "Independent component analysis and extensions with noise and time: A Bayesian Ying-Yang learning perspective," Neural Inf. Process. - Lett. Rev., vol. 1, no. 1, pp. 1-52, Oct. 2003.
44. H. Attias, "Independent factor analysis," Neural Comput., vol. 11, no. 4, pp. 803-851, May 1999.
45. R. A. Choudrey and S. J. Roberts, "Flexible Bayesian independent component analysis for blind source separation," in Proc. Int. Workshop Independent Component Anal. Signal Separat., Dec. 2001, pp. 90-95.
46. S. Deligne and R. Gopinath, "An EM algorithm for convolutive independent component analysis," Neurocomputing, vol. 49, no. 1-4, pp. 187-211, Dec. 2002.
47. H. Snoussi and A. Mohammad-Djafari, "Unsupervised learning for source separation with mixture of Gaussians prior for sources and Gaussian prior for mixture coefficients," in Neural Netw. Signal Process., Sep. 2001, pp. 293-302.
48. L. Xu, C. C. Cheung, H. H. Yang, and S.-I. Amari, "Independent Component Analysis by the information-theoretic approach with mixture of densities," in Proc. Int. Conf. Neural Netw., Jun. 1997, pp. 1821-1826.
49. L. Xu, "Bayesian Ying-Yang system and theory as a unified statistical learning approach: (III) Models and algorithms for dependence reduction, data dimension reduction, ICA, and supervised learning," in Proc. Int. Workshop Theor. Aspects Neural Comput.: Multidiscip. Persp., May 1997, pp. 43-60.
50. R. M. Neal and G. E. Hinton, M. I. Jordan, Ed., "A view of the EM algorithm that justifies incremental, sparse, and other variants," in Learning in Graphical Models. Cambridge, MA: MIT Press, 1998, pp. 355-368.
51. D. P. Mandic and A. Cichocki, "An online algorithm for blind extraction of sources with different dynamical structures," in Proc. Int. Workshop Independent Component Anal. Signal Separat., Apr. 2003, pp. 645-650.
52. J.-F. Cardoso and B. H. Laheld, "Equivariant adaptive source separation," IEEE Trans. Signal Process., vol. 44, no. 12, pp. 3017-3030, Dec. 1996.
53. S.-I. Amari and J.-F. Cardoso, "Blind source separation-semiparametric statistical approach," IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2692-2700, Nov. 1997.
54. S. A. Cruces-Alvarez, A. Cichocki, and S.-I. Amari, "On a new blind signal extraction algorithm: Different criteria and stability analysis," IEEE Signal Process. Lett., vol. 9, no. 8, pp. 233-236, Aug. 2002.
55. J.-F. Cardoso, "Infomax and maximum likelihood for blind source separation," IEEE Signal Process. Lett., vol. 4, no. 4, pp. 112-114, Apr. 1997.
56. S. Haykin, Adaptive Filter Theory, 4th ed. Englewood Cliffs, NJ: Prentice-Hall, 2001.
57. D.-T. Pham, "Mutual information approach to blind separation of stationary sources," in Proc. Int. Workshop Independent Component Anal. Signal Separat., Jan. 1999, pp. 215-220.
58. D. T. Pham, "Mutual information approach to blind separation of stationary sources," IEEE Trans. Inf. Theory, vol. 48, no. 7, pp. 1935-1946, Jul. 2002.
59. L. Xu, "BYY harmony learning, independent state space, and generalized APT financial analysis," IEEE Trans. Neural Netw., vol. 12, no. 4, pp. 822-849, Jul. 2001.
60. F. Castells, J. J. Rieta, J. Millet, and V. Zarzoso, "Spatiotemporal blind source separation approach to atrial activity estimation in atrial tachyarrhythmias," IEEE Trans. Biomed. Eng., vol. 52, no. 2, pp. 258-267, Feb. 2005.
61. A. Hyvarinen, "Independent component analysis for time-dependent stochastic processes," in Proc. Int. Conf. Artif. Neural Netw., Sep. 1998, pp. 541-546.
62. A. Hyvarinen, "Fast and robust fixed-point algorithms for independent component analysis," IEEE Trans. Neural Netw., vol. 10, no. 3, pp. 626-634, May 1999.
63. V. C. Soon, L. Tong, Y. F. Huang, and R. Liu, "An extended fourth order blind identification algorithm in spatially correlated noise," in Proc. Int. Conf. Acoust. Speech Signal Process., Apr. 1990, pp. 1365-1368.
64. L. Tong, R.-W. Liu, V. C. Soon, and Y.-F. Huang, "Indeterminacy and identifiability of blind identification," IEEE Trans. Circuits Syst. vol. 38, no. 5, pp. 499-509, May 1991.
65. L. Parra and P. Sajda, "Blind source separation via generalized eigenvalue decomposition," J. Mach. Learn. Res., vol. 4, pp. 1261-1269, Dec. 2003.
66. A. Ziehe and K.-R. Muller, "TDSEP - An efficient algorithm for blind separation using time structure," in Proc. Int. Conf. Artif. Neural Netw., Sep. 1998, pp. 675-680.
67. L. Tong, V. C. Soon, Y. F. Huang, and R. Liu, "Amuse: A new blind identification algorithm," in Proc. IEEE Int. Symp. Circuits Syst. May 1990, pp. 1784-1787.
68. J. F. Cardoso and A. Souloumiac, "Blind beamforming for non-Gaussian signals," Inst. Electr. Eng. Proc. - F, vol. 140, no. 6, pp. 362-370, Dec. 1993.
69. L. R. Rabiner and R. W. Schafer, Digital Processing of Speech Signals Upper Saddle River, NJ: Prentice-Hall, 1978.
70. J. G. Proakis, Digital Communications, 3rd ed. Boston, MA: McGraw-Hill, 1995.
71. K. E. Hild, II, H. T. Attias, and S. S. Nagarajan, "An EM method for spatio-temporal blind source separation using an AR-MOG source model," in Proc. Int. Workshop Independent Component Anal. Signal Separat. Mar. 2006, pp. 98-105.
72. S. Amari, "Neural learning in structured parameter spaces - Natural Riemannian gradient," Adv. Neural Inf. Process. Syst., pp. 127-133, Dec. 1996.