title: Estimation of signal and noise covariance using ICA for high-resolution cortical dipole imaging.
abstract: Suitable spatial filters were explored for inverse estimation of cortical dipole imaging from a scalp electroencephalogram. Computer simulations were used to examine the effects of incorporating statistical information of signal and noise into inverse procedures. Actually, the parametric projection filter (PPF) and parametric Wiener filter (PWF) were applied to an inhomogeneous three-sphere head model. The signal and noise covariance matrices were estimated by applying independent component analysis (ICA) to the scalp potentials. The simulation results described herein suggest that the PPF using differential noise between EEG and separated signal were equivalent to those obtained using the method with actual noise. Moreover, the PWF using separated signals has better performance than traditional inverse techniques.
authors.affiliation: Department of Biocybernetics, Niigata University, Niigata, Japan. hori@eng.niigata-u.ac.jp
authors.forename: Junichi
authors.initials: J
authors.lastname: Hori
mesh_terms: D000465:Algorithms; D001698:Biomedical Engineering; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D004553:Electric Conductivity; D004569:Electroencephalography / Q000379:methods* / Q000706:statistics & numerical data; D004594:Electrophysiology; D006801:Humans; D008959:Models, Neurological; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted