Single channel analysis of electromagnetic brain signals through ICA in a dynamical systems framework
Christopher J. James, David G. Lowe · 2005
This paper introduces a method for extracting information from single channel recordings of electromagnetic (EM) brain signals. In a dynamical embedding framework, the measured electroencephalogram (EEG) and magnetoencephalogram (MEG) signals are assumed generated by the non-linear interaction of a few degrees of freedom. In a three-step process, first an appropriate embedding matrix is constructed out of a series of delay vectors from the measured signal. Then independent component analysis (ICA) is performed on the embedding matrix to decompose the single channel recording into its underlying independent components (ICs). The ICs are treated as a convenient expansion basis and subjective methods are then used to identify components of interest relevant to the application. These ICs are then projected back onto the measurement space in isolation. The method has been applied to single channels of both EEG and MEG recordings and is shown to isolate, amongst others: (i) artifactual components such as ocular, electrocardiographic and electrode artifact, (ii) seizure components in epileptic EEG recordings and (iii) theta band, tumour related, activity in MEG recordings.