Decomposition algorithms for analysing brain signals

IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium, AS-SPCC 2000

Volumn , Issue , 2000, Pages 105-110

  Muller, K R ^a,b   Kohlmorgen, J ^a   Ziehe, A ^a   Blankertz, B ^a  

^a FRAUNHOFER FIRST   (Germany)

^b UNIVERSITY OF POTSDAM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; ADAPTIVE SYSTEMS; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMEDICAL SIGNAL PROCESSING; INDEPENDENT COMPONENT ANALYSIS; LEARNING SYSTEMS;

AUDITORY PERCEPTION; BIOMEDICAL DATA; BRAIN SIGNALS; DECOMPOSITION ALGORITHM; EEG RECORDING; INDEPENDENT COMPONENT ANALYSIS(ICA); NONSTATIONARY SIGNALS; SIGNAL PROCESSING TECHNIQUE;

SIGNAL PROCESSING;

EID: 84962385710     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ASSPCC.2000.882455     Document Type: Conference Paper

References (30)

1. S. Amari, A. Cichocki, and H. H. Yang. A new learning algorithm for blind signal separation. In D.S. Touretzky, M.C. Mozer, and M.E. Hasselmo, editors, Advances in Neural Information Processing Systems (NIPS 95), volume 8, pages 882-893. The MIT Press, 1996.
2. H. Attias. Independent factor analysis. Neural Computation, 11:803-851, 1998.
3. A. J. Bell and T. J. Sejnowski. An information maximisation approach to blind separation and blind deconvolution. Neural Computation, 7:1129-1159, 1995.
4. A. Belouchrani, K. Abed Meraim, J.-F. Cardoso, and E. Moulines. A blind source separation technique based on second order statistics. IEEE Trans. on SP, 45(2):434-44, Feb 1997.
5. J.-F. Cardoso and A. Souloumiac. Blind beamforming for non Gaussian signals. IEE Proceedings-F, 140(6):362-370, 1993.
6. J.-F. Cardoso and A. Souloumiac. Jacobi angles for simultaneous diagonalization. SIAM J. Mat. Anal. Appl., 17(1):161 ff., 1996.
7. P. Comon. Independent component analysis, a new concept? Signal Processing, Elsevier, 36(3):287-314, 1994.
8. G.H. Golub and C.F. van Loan. Matrix Computation. The Johns Hopkins University Press, London, 1989.
9. A. Hyvärinen. Independent component analysis in the presence of gaussian noise by maximizing joint likelihood. Neurocomputing, 22:49-67, 1998.
10. A. Hyvärinen and E. Oja. A fast fixed-point algorithm for independent component analysis. Neural Computation, 9(7):1483-1492, 1997.
11. C.G.J. Jacobi. Über ein leichtes Verfahren, die in der Theorie der Säcularstörungen vorkommenden Gleichungen numerisch aufzulösen. Crelle J. reine angew. Mathematik, 30:51-94, 1846.
12. R. A. Jacobs, M.I. Jordan, S.J. Nowlan, and G.E. Hinton. Adaptive mixtures of local experts. Neural Computation, 3:79-87, 1991.
13. Ch. Jutten and J. Herault. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing, 24:1-10, 1991.
14. B.-U. Köhler and R. Orglmeister. Independent component analysis using autoregressive models. In Proc. Int. Workshop on Independent Component Analysis and Blind Source Separation (ICA '99), pages 359-364, Aussois, France, January 11-15, 1999.
15. J. Kohlmorgen, K.-R. Müller, J. Rittweger, and K. Pawelzik. Identification of nonstationary dynamics in physiological recordings. Biological Cybernetics, 2000. in press.
16. B. Laheld and J.-F. Cardoso. Equivariant adaptive source separation. IEEE Trans. on Signal Processing, 44(12):3017-3030, 1996.
17. D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401:788-791, 1999.
18. S. Makeig, T-P. Jung, D. Ghahremani, A.J. Bell, and T.J. Sejnowski. Blind separation of event-related brain responses into independent components. Proc. Natl. Acad. Sci. USA, 94:10979-10984, 1997.
19. G.C. Marques and L.B. Almeida. Separation of non-linear mixtures using pattern repulsion. In Proc. Int. Workshop on Independent Component Analysis and Blind Source Separation (ICA '99), pages 277-282, Aussois, 1999.
20. L. Molgedey and H.G. Schuster. Separation of a mixture of independent signals using time delayed correlations. Physical Review Letters, 72(23):3634-3637, 1994.
21. TD: Combining higher-order statistics and temporal information for blind source separation (with noise). In Proc. Int. Workshop on Independent Component Analysis and Blind Source Separation (ICA '99), pages 87-92, Aussois, 1999.
22. L. Parra, C. D. Spence, P. Sajda, A. Ziehe, and K.-R. Müller. Unmixing hyperspectral data. In S.A. Solla, T.K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12, pages 942-948. MIT Press, 2000.
23. K. Pawelzik, J. Kohlmorgen, and K.-R. Müller. Annealed competition of experts for a segmentation and classification of switching dynamics. Neural Computation, 8:340-356, 1996.
24. J.C. Principe and D. Xu. Information-theoretic learning using Renyi's quadratic entropy. In Proc. Int. Workshop on Independent Component Analysis and Blind Source Separation (ICA '99), pages 407-412, Aussois, 1999.
25. B. Schölkopf, A. Smola, and K.-R. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10:1299-1319, 1998.
26. L. Tong, V.C. Soon, and Y. Huang. Indeterminacy and identifiability of identification. IEEE Trans. on Circuits and Systems, 38(5):499-509, 1991.
27. R. Vigário, V. Jousmäki, M. Hämäläinen, R. Hari, and E. Oja. Independent component analysis for identification of artifacts in magnetoencephalographic recordings. In Michael I. Jordan, Michael J. Kearns, and Sara A. Solla, editors, Advances in Neural Information Processing Systems, volume 10. The MIT Press, 1998.
28. G. Wübbeler, K.-R. Müller, A. Ziehe, B.-M. Mackert, L. Trahms, and G. Curio. Independent component analysis of non-invasively recorded cortical magnetic dc-fields in humans. IEEE Transactions on biomedical Engineering, 2000.
29. A. Ziehe and K.-R. Müller. TDSEP - an efficient algorithm for blind separation using time structure. In L. Niklasson, M. Bodén, and T. Ziemke, editors, Proc. Int. Conf. on Artificial Neural Networks (ICANN'98), pages 675-680, Skövde, Sweden, 1998. Springer Verlag.
30. A. Ziehe, K.-R. Müller, G. Nolte, B.-M. Mackert, and G. Curio. Artifact reduction in magnetoneurography based on time-delayed second order correlations. IEEE Trans. Biomed. Eng., 47(1):75-87, 2000. also GMD Technical Report No. 31, 1998.