Dipole estimation with a combination of noise reduction and spatial filter

S. Okawa; S. Honda

doi:10.1016/j.ics.2007.01.020

Dipole estimation with a combination of noise reduction and spatial filter

S. Okawa, S. Honda

Department of Applied Physics and Physico-Informatics

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

An analysis of magnetoencephalography data with a reduction of noise by a Kalman filter, independent component analysis and a reconstruction of current dipole via a non-adaptive spatial filter is proposed and applied to actual data. The N1m component is extracted and the bilateral neural activity is reconstructed from a set of single-trial AEF (auditory evoked field) data.

Original language	English
Pages (from-to)	249-252
Number of pages	4
Journal	International Congress Series
Volume	1300
DOIs	https://doi.org/10.1016/j.ics.2007.01.020
Publication status	Published - 2007 Jun 1

Keywords

Independent component analysis
Inverse problem
Kalman filter
Magnetoencephalography
Spatial filter

ASJC Scopus subject areas

Medicine(all)

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10.1016/j.ics.2007.01.020

Cite this

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title = "Dipole estimation with a combination of noise reduction and spatial filter",

abstract = "An analysis of magnetoencephalography data with a reduction of noise by a Kalman filter, independent component analysis and a reconstruction of current dipole via a non-adaptive spatial filter is proposed and applied to actual data. The N1m component is extracted and the bilateral neural activity is reconstructed from a set of single-trial AEF (auditory evoked field) data.",

keywords = "Independent component analysis, Inverse problem, Kalman filter, Magnetoencephalography, Spatial filter",

author = "S. Okawa and S. Honda",

year = "2007",

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AU - Okawa, S.

AU - Honda, S.

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AB - An analysis of magnetoencephalography data with a reduction of noise by a Kalman filter, independent component analysis and a reconstruction of current dipole via a non-adaptive spatial filter is proposed and applied to actual data. The N1m component is extracted and the bilateral neural activity is reconstructed from a set of single-trial AEF (auditory evoked field) data.

KW - Independent component analysis

KW - Inverse problem

KW - Kalman filter

KW - Magnetoencephalography

KW - Spatial filter

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SP - 249

EP - 252

JO - International Congress Series

JF - International Congress Series

ER -

Dipole estimation with a combination of noise reduction and spatial filter

Abstract

Keywords

ASJC Scopus subject areas

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