Skip to main content
SpringerLink
Log in

Partitioned Factor Analysis for Interference Suppression and Source Extraction

  • Conference paper
Independent Component Analysis and Blind Signal Separation (ICA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3889))

Abstract

It is common for data to be contaminated with artifacts, interference, and noise. Several methods including independent components analysis (ICA) and principal components analysis (PCA) have been used to suppress these undesired signals and/or to extract the underlying (desired) source waveforms. For some data it is known, or can be extracted post hoc, how to partition the data into periods of source activity and source inactivity. Two examples include cardiac data and data collected using the stimulus-evoked paradigm. However, neither ICA nor PCA are able to take full advantage of the knowledge of the partition. Here we introduce an interference suppression method, partitioned factor analysis (PFA), that takes into account the data partition.

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

Access this chapter

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ossadtchi, A., Baillet, S., Mosher, J.C., Thyerlei, D., Sutherling, W., Leahy, R.M.: Automated interictal spike detection and source localization in magnetoencephalography using independent components analysis and spatio-temporal clustering. Clinical Neurophysiology 115, 508–522 (2004)

    Article  Google Scholar 

  2. Jackson, J.E.: A User’s Guide to Principal Components (2003)

    Google Scholar 

  3. Attias, H.: A variational Bayesian framework for graphical models. In: Advances in Neural Information Proc. Systems, vol. 13, pp. 209–215 (2000)

    Google Scholar 

  4. Cover, T.M., Thomas, J.A.: Elements of Information Theory (1991)

    Google Scholar 

  5. Ungan, P., Basar, E.: Comparison of Wiener filtering and selective averaging of evoked potentials. Electroencephalography and Clinical Neurophysiology 40, 516–520 (1976)

    Article  Google Scholar 

  6. Ziehe, A., Mller, K.R.: TDSEP - an efficient algorithm for blind separation using time structure. In: Intl. Conf. on Artificial Neural Networks, pp. 675–680 (1998)

    Google Scholar 

  7. Hyvarinen, A.: Fast and robust fixed-point algorithms for Independent Component Analysis. IEEE Trans. on Neural Networks 10, 626–634 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Radiology, University of California at San Francisco, CA, 94122, USA

    Srikantan S. Nagarajan & Kenneth E. Hild II

  2. Golden Metallic Inc., San Francisco, CA, 94147, USA

    Hagai T. Attias

  3. Dept. of Electronic Systems and Engineering, Tokyo Metropolitan Institute of Technology, 191-0065, Japan

    Kensuke Sekihara

Authors
  1. Srikantan S. Nagarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hagai T. Attias
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kensuke Sekihara
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kenneth E. Hild II
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Siemens Corporate Research, 755 College Road East, 08540, Princeton, NJ, USA

    Justinian Rosca

  2. Department of CSEE, Oregon Health and Science University, Portland, Oregon, USA

    Deniz Erdogmus

  3. Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA

    José C. Príncipe

  4. McMaster University, 1280 Main Street West, L8S 4K1, Hamilton, Ontario, Canada

    Simon Haykin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nagarajan, S.S., Attias, H.T., Sekihara, K., Hild, K.E. (2006). Partitioned Factor Analysis for Interference Suppression and Source Extraction. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_24

Download citation

  • DOI: https://doi.org/10.1007/11679363_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32630-4

  • Online ISBN: 978-3-540-32631-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation