Noise reduction from MEG data

Shinpei Okawa; Satoshi Honda

Noise reduction from MEG data

Shinpei Okawa
, Satoshi Honda

Research output: Contribution to conference › Paper › peer-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 language	English
Pages	1975-1979
Number of pages	5
Publication status	Published - 2004
Externally published	Yes
Event	SICE Annual Conference 2004 - Sapporo, Japan Duration: 2004 Aug 4 → 2004 Aug 6

Other

Other	SICE Annual Conference 2004
Country/Territory	Japan
City	Sapporo
Period	04/8/4 → 04/8/6

Keywords

Factor analysis
ICA
Kalman filter
MEG

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

Cite this

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title = "Noise reduction from MEG data",

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.",

keywords = "Factor analysis, ICA, Kalman filter, MEG",

author = "Shinpei Okawa and Satoshi Honda",

year = "2004",

language = "English",

pages = "1975--1979",

note = "SICE Annual Conference 2004 ; Conference date: 04-08-2004 Through 06-08-2004",

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TY - CONF

T1 - Noise reduction from MEG data

AU - Okawa, Shinpei

AU - Honda, Satoshi

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

KW - Factor analysis

KW - ICA

KW - Kalman filter

KW - MEG

UR - https://www.scopus.com/pages/publications/12744253708

UR - https://www.scopus.com/pages/publications/12744253708#tab=citedBy

M3 - Paper

AN - SCOPUS:12744253708

SP - 1975

EP - 1979

T2 - SICE Annual Conference 2004

Y2 - 4 August 2004 through 6 August 2004

ER -

Noise reduction from MEG data

Abstract

Other

Keywords

ASJC Scopus subject areas

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Cite this