Skip to main content
SpringerLink
Log in
Book cover

International Workshop on Deterministic and Statistical Methods in Machine Learning

DSMML 2004: Deterministic and Statistical Methods in Machine Learning pp 159–179Cite as

Bayesian Independent Component Analysis with Prior Constraints: An Application in Biosignal Analysis

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3635))

Abstract

In many data-driven machine learning problems it is useful to consider the data as generated from a set of unknown (latent) generators or sources. The observations we make are then taken to be related to these sources through some unknown functionaility. Furthermore, the (unknown) number of underlying latent sources may be different to the number of observations and hence issues of model complexity plague the analysis. Recent developments in Independent Component Analysis (ICA) have shown that, in the case where the unknown function linking sources to observations is linear, data decomposition may be achieved in a mathematically elegant manner. In this paper we extend the general ICA paradigm to include a very flexible source model and prior constraints and argue that for particular biomedical signal processing problems (we consider EEG analysis) we require the constraint of positivity in the mixing process.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Roberts, S., Everson, R.: Independent Component Analysis: principles and practice. Cambridge University Press, Cambridge (2001)

    MATH  Google Scholar 

  2. Independent Component Analysis. John Wiley & Sons, Chichester (2001)

    Google Scholar 

  3. Comon, P.: Independent component analysis, a new concept? Signal Processing 36, 287–314 (1994)

    Article  MATH  Google Scholar 

  4. Hyvärinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)

    Article  Google Scholar 

  5. Bell, A.J., Sejnowski, T.J.: An information maximisation approach to blind separation and blind deconvolution. Neural Computation 7(6), 1129–1159 (1995)

    Article  Google Scholar 

  6. MacKay, D.J.C.: Maximum Likelihood and Covariant Algorithms for Independent Component Analysis. Technical report, University of Cambridge (December 1996), Available from http://wol.ra.phy.cam.ac.uk/mackay/

  7. Cardoso, J.-F.: Infomax and Maximum Likelihood for Blind Separation. IEEE Signal Processing Letters 4(4), 112–114 (1997)

    Article  Google Scholar 

  8. Roberts, S.J.: Independent Component Analysis: Source Assessment and Separation, a Bayesian Approach. IEE Proceedings, Vision, Image and Signal Processing 145(3), 149–154 (1998)

    Article  Google Scholar 

  9. Everson, R., Roberts, S.: Independent Component Analysis: A flexible non-linearity and decorrelating manifold approach. Neural Computation 11(8), 1957–1983 (1999)

    Article  Google Scholar 

  10. Choudrey, R., Penny, W., Roberts, S.: An ensemble learning approach to Independent Component Analysis. In: Proceedings of Neural Networks for Signal Processing, Sydney, Australia (December 2000)

    Google Scholar 

  11. Miskin, J., MacKay, D.: Ensemble learning for blind source separation. In: Roberts, S., Everson, R. (eds.) Independent Component Analysis: Principles and Practice,  ch. 8. Cambridge University Press, Cambridge (2001)

    Google Scholar 

  12. Choudrey, R., Roberts, S.: Variational Mixture of Bayesian Independent Component Analysers. Neural Computation 15(1), 213–252 (2003)

    Article  MATH  Google Scholar 

  13. Makeig, S., Bell, A.J., Jung, T.-P., Sejnowski, T.J.: Independent component analysis of electroencephalographic data. In: Advances in Neural Information Processing Systems, vol. 8, pp. 145–151. MIT Press, Cambridge (1996)

    Google Scholar 

  14. Makeig, S., Jung, T.-P., Bell, A.J., Ghahremani, D., Sejnowski, T.J.: Blind separation of auditory event-related brain responses into independent components. Proceedings of the National Academy of Sciences 94, 10979–10984 (1997)

    Article  Google Scholar 

  15. Jung, T.-P., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., Sejnowski, T.J.: Independent component analysis of single-trial event-related potentials. In: Proc. First International Conference on Independent Component Analysis and Blind Source Separation ICA 1999, Aussois, France, pp. 173–178 (1999)

    Google Scholar 

  16. Wübbeler, G., Ziehe, A., Mackert, B.-M., Müller, K.-R., Trahms, L., Curio, G.: Independent component analysis of non-invasively recorded cortical magnetic DC-fields in humans. IEEE Trans Biomed Eng. 47(5) (2000)

    Google Scholar 

  17. Penny, W., Everson, R., Roberts, S.: Hidden Markov Independent Components Analysis. In: Girolami, M. (ed.) chapter in Advances in Independent Components Analysis. Springer, Heidelberg (2000)

    Google Scholar 

  18. Roberts, S., Roussos, E., Choudrey, R.: Hierarchy, Priors and Wavelets: Structure and Signal Modelling using ICA. Signal Processing 84, 283–297 (2004)

    Article  MATH  Google Scholar 

  19. Jolliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (1986)

    Google Scholar 

  20. Nunez, P.L.: Electric Fields of the Brain. Oxford University Press, Oxford (1981)

    Google Scholar 

  21. Gielser, C.D., Gerstein, G.L.: The Surface EEG in Relation to its Sources. Electroenceph. Clin. Neur., 927–934 (1961)

    Google Scholar 

  22. Gloor, P.: Neuronal Generators and the Problem of Localization in Electroencephalography: Application of Volume Conductor Theory to Electroencephalography. Journ. Clin. Neur. 2(4), 327–354 (1985)

    Article  Google Scholar 

  23. Hosek, R.S., Sances, A., Jodat, R.W., Larson, S.J.: Contributions of Intracerebral Currents to the EEG and Evoked Potentials. IEEE Transactions on Biomed. Eng. 25(5), 405–413

    Google Scholar 

  24. Nicholson, C.: Theoretic Analysis of Field Potentials in Anisotropic Ensembles of Neuronal Elements. IEEE Trans. Biomed. Eng. 20(4), 279–288 (1973)

    Article  MathSciNet  Google Scholar 

  25. Peters, M.J.: On the Magnetic Field and the Electric Potential generated by Bioelectric Sources in an Anisotropic Volume Conductor. Med. & Biol. Eng. & Comput. 26, 617–623 (1988)

    Article  Google Scholar 

  26. Rush, S., Drsicoll, D.A.: Current Distribution in the Brain from Surface Electrodes. Anesth. Analg. 47, 717–723 (1968)

    Article  Google Scholar 

  27. Choudrey, R., Roberts, S.: Flexible Bayesian Independent Component Analysis for Blind Source Separation. In: Proceedings of ICA 2001, San Diego (December 2001)

    Google Scholar 

  28. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. J. Roy. Stat. Soc. 39(1), 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

  29. Neal, R.M., Hinton, G.E.: A view of the EM algorithm that justifies incremental, sparse and other variants. In: Jordan, (ed.) [42], pp. 355–368

    Google Scholar 

  30. Attias, H.: Independent Factor Analysis. Neural Computation 11, 803–851 (1999)

    Article  Google Scholar 

  31. Pearlmutter, B., Parra, L.: A Context-Sensitive Generalization of ICA. In: 1996 International Conference on Neural Information Processing (1996)

    Google Scholar 

  32. Cardoso, J.-F.: Blind signal separation: statistical principles. IEEE Transactions on Signal Processing 9(10), 2009–2025 (1998)

    MathSciNet  Google Scholar 

  33. Husmeier, D., Penny, W.D., Roberts, S.J.: An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers. Neural Networks 12, 677–705 (1999)

    Article  Google Scholar 

  34. Neal, R.M.: Bayesian learning for neural networks. Lecture notes in statistics. Springer, Berlin (1996)

    MATH  Google Scholar 

  35. Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. In: Jordan, (ed.) [42]

    Google Scholar 

  36. Jaakkola, T.S., Jordan, M.I.: Bayesian parameter estimation via variational methods. Statistics and Computing 10, 25–37 (2000)

    Article  Google Scholar 

  37. O’ Ruanaidth, J.J.K., Fitzgerald, W.J.: Numerical Bayesian methods applied to signal processing. Springer, Heidelberg (1996)

    Google Scholar 

  38. Information Theory. John Wiley, Chichester (1991)

    Google Scholar 

  39. Attias, H.: Inferring Parameters and Structure of Latent Variable Models by Variational Bayes. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (1999)

    Google Scholar 

  40. Keirn, Z.A., Aunon, J.I.: A new mode of communication between man and his surroundings. IEEE Transactions of Biomedical Engineering 37(12), 1209–1214 (1990)

    Article  Google Scholar 

  41. Roberts, S.J., Penny, W.D.: Real-time Brain Computer Interfacing: a preliminary study using Bayesian learning. Medical and Biological Engineering & Computing 38(1), 56–61 (2000)

    Article  Google Scholar 

  42. Jordan, M.I. (ed.): Learning in Graphical Models. MIT Press, Cambridge (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Robotics Research Group, University of Oxford, UK

    Stephen Roberts & Rizwan Choudrey

Authors
  1. Stephen Roberts
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rizwan Choudrey
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, The University of Sheffield, Regent Court, 211 Portobello Street, S1 4DP, Sheffield, UK

    Joab Winkler

  2. Department of Computer Science, The University of Sheffield, Regent Court,211 Portobello Street, S1 4DP, Sheffield, UK

    Mahesan Niranjan

  3. University of Manchester, UK

    Neil Lawrence

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Roberts, S., Choudrey, R. (2005). Bayesian Independent Component Analysis with Prior Constraints: An Application in Biosignal Analysis. In: Winkler, J., Niranjan, M., Lawrence, N. (eds) Deterministic and Statistical Methods in Machine Learning. DSMML 2004. Lecture Notes in Computer Science(), vol 3635. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559887_10

Download citation

  • DOI: https://doi.org/10.1007/11559887_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29073-5

  • Online ISBN: 978-3-540-31728-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation