Title
Extracting multisource brain activity from a single electromagnetic channel
Abstract
This paper develops a methodology for the extraction of multisource brain activity using only single channel recordings of electromagnetic (EM) brain signals. Measured electroencephalogram (EEG) and magnetoencephalogram (MEG) signals are used to demonstrate the utility of the method on extracting multisource activity from a single channel recording. At the heart of the method is dynamical embedding (DE) where first an appropriate embedding matrix is constructed out of a series of delay vectors from the measured signal. The embedding matrix contains the information we require, but in a mixed form which therefore needs to be deconstructed. In particular, we demonstrate how one form of independent component analysis (ICA) performed on the embedding matrix can deconstruct the single channel recording into its underlying informative components. The components are treated as a convenient expansion basis and subjective methods are then used to identify components of interest relevant to the application. The framework has been applied to single channels of both EEG and MEG recordings and is shown to isolate multiple sources of activity which includes: (i) artifactual components such as ocular, electrocardiographic and electrode artefact, (ii) seizure components in epileptic EEG recordings, and (iii) theta band, tumour related, activity in MEG recordings. The results are intuitive and meaningful in a neurophysiological setting.
Year
DOI
Venue
2003
10.1016/S0933-3657(03)00037-X
Artificial Intelligence In Medicine
Keywords
Field
DocType
independent component analysis
Embedding,Neurophysiology,Computer science,Matrix (mathematics),Communication channel,Brain activity and meditation,Speech recognition,Independent component analysis,Electroencephalography
Journal
Volume
Issue
ISSN
28
1
0933-3657
Citations 
PageRank 
References 
25
3.15
7
Authors
2
Name
Order
Citations
PageRank
Christopher J. James120621.93
David Lowe244141.90