Noise reduction from MEG data

Shinpei Okawa, Satoshi Honda

Research output: Contribution to conferencePaperpeer-review

Abstract

A method that reduces sensor noise and artifacts from MEG data is proposed. Factor analysis and Kalman filter are employed for sensor noise reduction. Factor analysis estimates noise covariances for Kalman filter. After the sensor noise reduction, Independent Component Analysis (ICA) is used to eliminate artifacts. Simulation studies confirmed that the signal-to-noise ratio of estimated independent component increases.

Original languageEnglish
Pages1975-1979
Number of pages5
Publication statusPublished - 2004
Externally publishedYes
EventSICE Annual Conference 2004 - Sapporo, Japan
Duration: 2004 Aug 42004 Aug 6

Other

OtherSICE Annual Conference 2004
Country/TerritoryJapan
CitySapporo
Period04/8/404/8/6

Keywords

  • Factor analysis
  • ICA
  • Kalman filter
  • MEG

ASJC Scopus subject areas

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

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