Noise reduction from MEG data

Shinpei Okawa; Satoshi Honda

Noise reduction from MEG data

Shinpei Okawa
, Satoshi Honda

研究成果: Paper › 査読

抄録

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.

本文言語	English
ページ	1975-1979
ページ数	5
出版ステータス	Published - 2004
外部発表	はい
イベント	SICE Annual Conference 2004 - Sapporo, Japan 継続期間: 2004 8月 4 → 2004 8月 6

Other

Other	SICE Annual Conference 2004
国/地域	Japan
City	Sapporo
Period	04/8/4 → 04/8/6

ASJC Scopus subject areas

制御およびシステム工学
コンピュータサイエンスの応用
電子工学および電気工学

他のファイルとリンク

引用スタイル

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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

抄録

Other

ASJC Scopus subject areas

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