ICA-based ocular artifact removal
Some recent developments.
The logic for the following paper is simple: Do ICA, identify the artifact component, and do a high-pass filter with Wavelet to take out the oculomotor signals, then to put everything back again. This leaves "leaked" brain signals untouched in the supposedly artifact component. The tests seem very promising. This differs from other attemps to first filter via Wavelet and then ICA.
Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis.
Neuroscience Laboratory, Department of Applied Mathematics, Escuela de Optica, Universidad Complutense de Madrid, Avda. Arcos de Jalon s/n, 28037 Madrid, Spain.
Independent component analysis (ICA) has been proven useful for suppression of artifacts in EEG recordings. It involves separation of measured signals into statistically independent components or sources, followed by rejection of those deemed artificial. We show that a "leak" of cerebral activity of interest into components marked as artificial means that one is going to lost that activity. To overcome this problem we propose a novel wavelet enhanced ICA method (wICA) that applies a wavelet thresholding not to the observed raw EEG but to the demixed independent components as an intermediate step. It allows recovering the neural activity present in "artificial" components. Employing semi-simulated and real EEG recordings we quantify the distortions of the cerebral part of EEGs introduced by the ICA and wICA artifact suppressions in the time and frequency domains. In the context of studying cortical circuitry we also evaluate spectral and partial spectral coherences over ICA/wICA-corrected EEGs. Our results suggest that ICA may lead to an underestimation of the neural power spectrum and to an overestimation of the coherence between different cortical sites. wICA artifact suppression preserves both spectral (amplitude) and coherence (phase) characteristics of the underlying neural activity.
PMID: 16828877 [PubMed - as supplied by publisher]
Entrez PubMed
This next one puts the constraint on the spatial distribution rather than temporal characteristics as this paper (www.gibo.demon.co.uk/papers/JamesGibsonTBME.pdf) did.
Physiol Meas. 2006 Apr;27(4):425-36. Epub 2006 Mar 14. Related Articles, Links
Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach.
Department of Automation, Tsinghua University, Beijing 100084, People’s Republic of China. liyd02@mails.tsinghua.edu.cn
Independent component analysis (ICA) proves to be effective in the removing the ocular artifact from electroencephalogram recordings (EEG). While using ICA in ocular artifact correction, a crucial step is to correctly identify the artifact components among the decomposed independent components. In most previous works, this step of selecting the artifact components was manually implemented, which is time consuming and inconvenient when dealing with a large amount of EEG data. We present a new method which automatically selects the eye blink artifact components based on the pattern of their scalp topographies, which can be exemplified as a template matching approach. The feasibility of using a fixed template for singling out the eye blink component after ICA decomposition was validated by an experiment in which 18 subjects among the 21 subjects involved exhibited a highly consistent pattern of eye blink scalp topographies. Since only the spatial feature is employed for singling out the eye blink component, the proposed method is very efficient and easy to implement. Objective evaluation of the real results shows that the proposed algorithm can remove the eye blink artifact from the EEG while causing little distortion to the underlying brain activities.
PMID: 16537983 [PubMed - indexed for MEDLINE]
This is similar to
The FastICA algorithm with spatial constraints
Hesse, C.W.; James, C.J.;
Signal Processing Letters, IEEE
Volume 12, Issue 11, Nov. 2005 Page(s):792 - 795
Abstract:In many blind source separation (BSS) applications, especially for biomedical signal processing, there are specific expectations regarding the spatial and temporal characteristics of some sources, but post-hoc comparisons between source estimates and anticipated outcomes can be complicated and unreliable. One alternative is to incorporate additional prior knowledge, e.g., about the spatial topography of selected source sensor projections, into the BSS approach by means of constraints. This letter describes a modified version of the FastICA algorithm for spatially constrained BSS, where the estimates of selected columns of the mixing matrix are constrained with reference to predetermined source sensor projections.