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Can we learn anything from single-channel unaveraged MEG data?

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Abstract

A method for the decomposition of single-channel unaveraged magnetoencephalographic (MEG) data into statistically independent components is presented. The study of MEG recordings is characterised by a host of difficulties, most of which stem from the inherently noisy recording process by which the data is obtained. MEG time series typically contain a mix of artifactual components from a variety of sources, and the isolation of interesting signals from this noise background poses a difficult problem. In this article, we present a novel approach combining the techniques of independent component analysis (ICA) and dynamical embedding, which can be used to extract and isolate components of interest from single-channel unaveraged MEG data. In our approach, the method of delays is proposed as a means of augmenting the single-channel data, thus, facilitating the application of ICA. Finally, because the single-channel approach yields no information regarding the physiological origins of extracted sources, we discuss a method by which extracted sources may be projected back into the multichannel measurement space, permitting an estimate of the respective spatial distributions to be obtained. The proposed methods are tested on three separate MEG channels and the results are presented and discussed.

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Acknowledgements

The research detailed in this paper has been sponsored by the UK EPSRC grant GR/L94673. We thank the EPSRC for supporting this research. We also wish to thank the staff of the Clinical Neurophysiology Unit at Aston University, Birmingham for much-needed assistance and advice in the MEG data collection process, as well as regarding various MEG theoretical issues in general.

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Authors and Affiliations

  1. Malaysia University of Science and Technology, Ground Fl., Blk. C, Dataran Usahawan Kelana, 17 Jalan SS7/26, 47301, Petaling Jaya, Malaysia

    W. L. Woon

  2. Neural Computing Research Group, Aston University, Aston Triangle, Birmingham, B4 7ET, UK

    D. Lowe

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  1. W. L. Woon
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  2. D. Lowe
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Correspondence to W. L. Woon.

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Woon, W.L., Lowe, D. Can we learn anything from single-channel unaveraged MEG data?. Neural Comput & Applic 13, 360–368 (2004). https://doi.org/10.1007/s00521-004-0432-1

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  • DOI: https://doi.org/10.1007/s00521-004-0432-1

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