High-Resolution Cortical Dipole Imaging Using Spatial Inverse Filter Based on Filtering Property
Joint Authors
Hori, Junichi
Takasawa, Shintaro
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-29
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Cortical dipole imaging has been developed to visualize brain electrical activity in high spatial resolution.
It is necessary to solve an inverse problem to estimate the cortical dipole distribution from the scalp potentials.
In the present study, the accuracy of cortical dipole imaging was improved by focusing on filtering property of the spatial inverse filter.
We proposed an inverse filter that optimizes filtering property using a sigmoid function.
The ability of the proposed method was compared with the traditional inverse techniques, such as Tikhonov regularization, truncated singular value decomposition (TSVD), and truncated total least squares (TTLS), in a computer simulation.
The proposed method was applied to human experimental data of visual evoked potentials.
As a result, the estimation accuracy was improved and the localized dipole distribution was obtained with less noise.
American Psychological Association (APA)
Hori, Junichi& Takasawa, Shintaro. 2016. High-Resolution Cortical Dipole Imaging Using Spatial Inverse Filter Based on Filtering Property. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1099800
Modern Language Association (MLA)
Hori, Junichi& Takasawa, Shintaro. High-Resolution Cortical Dipole Imaging Using Spatial Inverse Filter Based on Filtering Property. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1099800
American Medical Association (AMA)
Hori, Junichi& Takasawa, Shintaro. High-Resolution Cortical Dipole Imaging Using Spatial Inverse Filter Based on Filtering Property. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1099800
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099800