Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Nonlinear time series analysis
Nonlinear time series analysis
BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
Source separation using single channel ICA
Signal Processing
Novel features for brain-computer interfaces
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
On the analysis of single versus multiple channels of electromagnetic brain signals
Artificial Intelligence in Medicine
Independent complexity patterns in single neuron activity induced by static magnetic field
Computer Methods and Programs in Biomedicine
Hi-index | 0.00 |
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.