MEG analysis using ICA with spatial arrangement

Shunta Echigoya; Satoshi Honda

doi:10.1109/SICE.2006.314697

MEG analysis using ICA with spatial arrangement

Shunta Echigoya, Satoshi Honda

Department of Applied Physics and Physico-Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

One of the problems in analyzing magnetoencepharograpy (MEG) is that brain signals are contaminated with high-level noise and artifacts. Although independent component analysis (ICA) is a useful method to separate brain signals from other components, not all signals are statistically independent. Additionally, each component should be judged as a brain signals or the others objectively. In this paper, we propose two ICA approaches that utilize spatial characteristics of brain activities to separate signals more precisely and meaningfully. Numerical experiments showed that it is helpful for ICA to use spatial arrangement, and a experiment using auditory evoked field (AEF) data brought out the features of proposal techniques.

Original language	English
Title of host publication	2006 SICE-ICASE International Joint Conference
Pages	3548-3553
Number of pages	6
DOIs	https://doi.org/10.1109/SICE.2006.314697
Publication status	Published - 2006 Dec 1
Event	2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of Duration: 2006 Oct 18 → 2006 Oct 21

Publication series

Name	2006 SICE-ICASE International Joint Conference

Other

Other	2006 SICE-ICASE International Joint Conference
Country/Territory	Korea, Republic of
City	Busan
Period	06/10/18 → 06/10/21

Keywords

Independent component analysis
Magnetoencephalography
Spatial characteristic

ASJC Scopus subject areas

Computer Science Applications
Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/SICE.2006.314697

Cite this

@inproceedings{5c31a235ba2e4d068f73cb9f8e1a79ec,

title = "MEG analysis using ICA with spatial arrangement",

abstract = "One of the problems in analyzing magnetoencepharograpy (MEG) is that brain signals are contaminated with high-level noise and artifacts. Although independent component analysis (ICA) is a useful method to separate brain signals from other components, not all signals are statistically independent. Additionally, each component should be judged as a brain signals or the others objectively. In this paper, we propose two ICA approaches that utilize spatial characteristics of brain activities to separate signals more precisely and meaningfully. Numerical experiments showed that it is helpful for ICA to use spatial arrangement, and a experiment using auditory evoked field (AEF) data brought out the features of proposal techniques.",

keywords = "Independent component analysis, Magnetoencephalography, Spatial characteristic",

author = "Shunta Echigoya and Satoshi Honda",

year = "2006",

month = dec,

day = "1",

doi = "10.1109/SICE.2006.314697",

language = "English",

isbn = "8995003855",

series = "2006 SICE-ICASE International Joint Conference",

pages = "3548--3553",

booktitle = "2006 SICE-ICASE International Joint Conference",

note = "2006 SICE-ICASE International Joint Conference ; Conference date: 18-10-2006 Through 21-10-2006",

}

TY - GEN

T1 - MEG analysis using ICA with spatial arrangement

AU - Echigoya, Shunta

AU - Honda, Satoshi

PY - 2006/12/1

Y1 - 2006/12/1

N2 - One of the problems in analyzing magnetoencepharograpy (MEG) is that brain signals are contaminated with high-level noise and artifacts. Although independent component analysis (ICA) is a useful method to separate brain signals from other components, not all signals are statistically independent. Additionally, each component should be judged as a brain signals or the others objectively. In this paper, we propose two ICA approaches that utilize spatial characteristics of brain activities to separate signals more precisely and meaningfully. Numerical experiments showed that it is helpful for ICA to use spatial arrangement, and a experiment using auditory evoked field (AEF) data brought out the features of proposal techniques.

AB - One of the problems in analyzing magnetoencepharograpy (MEG) is that brain signals are contaminated with high-level noise and artifacts. Although independent component analysis (ICA) is a useful method to separate brain signals from other components, not all signals are statistically independent. Additionally, each component should be judged as a brain signals or the others objectively. In this paper, we propose two ICA approaches that utilize spatial characteristics of brain activities to separate signals more precisely and meaningfully. Numerical experiments showed that it is helpful for ICA to use spatial arrangement, and a experiment using auditory evoked field (AEF) data brought out the features of proposal techniques.

KW - Independent component analysis

KW - Magnetoencephalography

KW - Spatial characteristic

UR - http://www.scopus.com/inward/record.url?scp=34250728052&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34250728052&partnerID=8YFLogxK

U2 - 10.1109/SICE.2006.314697

DO - 10.1109/SICE.2006.314697

M3 - Conference contribution

AN - SCOPUS:34250728052

SN - 8995003855

SN - 9788995003855

T3 - 2006 SICE-ICASE International Joint Conference

SP - 3548

EP - 3553

BT - 2006 SICE-ICASE International Joint Conference

T2 - 2006 SICE-ICASE International Joint Conference

Y2 - 18 October 2006 through 21 October 2006

ER -

MEG analysis using ICA with spatial arrangement

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this