MEG analysis with spatial filter and multiple linear regression

Shinpei Okawa, Satoshi Honda

Research output: Contribution to conferencePaper

Abstract

A spatial filterlor MEG analysis which does not utilize any temporal and prior information is proposed. The spatial filter is normalized to satisfy the criterion which is derived from the definition of the spatial filter. Due to the normalization, the spatial filter outputs the largest value at its target position. Furthermore, the current density distribution estimated with spatial filter is localized with Mallows Cp statistic which selects an optimum regression model. Some numerical experiments verify that this method estimates almost correct positions of dipoles. It is also confirmed that new method we propose gives more reliable estimation than the conventional method which decides dipole on the position of the largest current density estimated with spatial filter iteratively.

Original languageEnglish
Pages1981-1985
Number of pages5
Publication statusPublished - 2004 Dec 1
EventSICE Annual Conference 2004 - Sapporo, Japan
Duration: 2004 Aug 42004 Aug 6

Other

OtherSICE Annual Conference 2004
CountryJapan
CitySapporo
Period04/8/404/8/6

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Keywords

  • Inverse problem
  • MEG
  • Multiple linear regression
  • Spatial filter

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Okawa, S., & Honda, S. (2004). MEG analysis with spatial filter and multiple linear regression. 1981-1985. Paper presented at SICE Annual Conference 2004, Sapporo, Japan.