Independent component analysis as a tool to eliminate artifacts in EEG. A quantitative study
Ver/
Fecha
2003Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión aceptada / Onetsi den bertsioa
Impacto
|
nodoi-noplumx
|
Resumen
Independent component analysis (ICA) is a novel technique that calculates
independent components from mixed signals. A hypothetical clinical application is to
remove artifacts in EEG. The goal of this study was to apply ICA to standard EEG
recordings to eliminate well-known artifacts, thus quantifying its efficacy in an
objective way. Eighty samples of recordings with spikes and evident artif ...
[++]
Independent component analysis (ICA) is a novel technique that calculates
independent components from mixed signals. A hypothetical clinical application is to
remove artifacts in EEG. The goal of this study was to apply ICA to standard EEG
recordings to eliminate well-known artifacts, thus quantifying its efficacy in an
objective way. Eighty samples of recordings with spikes and evident artifacts of
electrocardiogram (EKG), eye movements, 50-Hz interference, muscle, or electrode
artifact were studied. ICA components were calculated using the Joint Approximate
Diagonalization of Eigen-matrices (JADE) algorithm. The signal was reconstructed
excluding those components related to the artifacts. A normalized correlation coefficient
was used as a measure of the changes caused by the suppression of these
components. ICA produced an evident clearing-up of signals in all the samples. The
morphology and the topography of the spike were very similar before and after the
removal of the artifacts. The correlation coefficient showed that the rest of the signal
did not change significantly. Two examiners independently looked at the samples to
identify the changes in the morphology and location of the discharge and the artifacts.
In conclusion, ICA proved to be a useful tool to clean artifacts in short EEG samples,
without having the disadvantages associated with the digital filters. The distortion of
the interictal activity measured by correlation analysis was minimal. [--]
Materias
EEG,
Artifacts,
Independent component analysis
Editor
Lippincott, Williams & Wilkins
Publicado en
Journal of Clinical Neurophysiology, 20 (4):249-257, July/August 2003
Departamento
Universidad Pública de Navarra. Departamento de Ingeniería Eléctrica y Electrónica /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Elektrikoa eta Elektronikoa Saila
Entidades Financiadoras
This study was supported by the Government of Navarra, grants for
research in Health 12/2003 and 15/2003.