Can we learn anything from single-channel unaveraged MEG data

W. L. Woon*, D. Lowe

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

A method for the decomposition of singlechannel 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.

Original languageEnglish
Pages (from-to)360-368
Number of pages9
JournalNeural Computing and Applications
Volume13
Issue number4
DOIs
Publication statusPublished - 1 Dec 2004

Keywords

  • Dynamical embedding
  • ICA
  • MEG
  • Single channel

Fingerprint Dive into the research topics of 'Can we learn anything from single-channel unaveraged MEG data'. Together they form a unique fingerprint.

  • Cite this