The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Nos últimos anos, várias soluções inversas de magnetoencefalografia (MEG) foram propostas. Entre eles, o método de classificação de sinais múltiplos (MUSIC) utiliza informações espaço-temporais obtidas de campos magnéticos. O método MUSIC convencional é, no entanto, sensível ao ruído gaussiano e uma relação sinal-ruído (SNR) suficientemente grande é necessária para estimar o número de fontes e especificar as localizações precisas das atividades neurais elétricas. Neste artigo é proposto um novo algoritmo para resolução do problema inverso utilizando o método MUSIC de quarta ordem (FO-MUSIC). Nós o aplicamos ao problema de estimativa da fonte MEG. Simulações numéricas demonstram que o algoritmo FO-MUSIC proposto é mais robusto contra ruído gaussiano do que o algoritmo MUSIC convencional.
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Satoshi NIIJIMA, Shoogo UENO, "MEG Source Estimation Using the Fourth Order MUSIC Method" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 1, pp. 167-174, January 2002, doi: .
Abstract: In recent years, several inverse solutions of magnetoencephalography (MEG) have been proposed. Among them, the multiple signal classification (MUSIC) method utilizes spatio-temporal information obtained from magnetic fields. The conventional MUSIC method is, however, sensitive to Gaussian noise and a sufficiently large signal-to-noise ratio (SNR) is required to estimate the number of sources and to specify the precise locations of electrical neural activities. In this paper, a new algorithm for solving the inverse problem using the fourth order MUSIC (FO-MUSIC) method is proposed. We apply it to the MEG source estimation problem. Numerical simulations demonstrate that the proposed FO-MUSIC algorithm is more robust against Gaussian noise than the conventional MUSIC algorithm.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_1_167/_p
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@ARTICLE{e85-d_1_167,
author={Satoshi NIIJIMA, Shoogo UENO, },
journal={IEICE TRANSACTIONS on Information},
title={MEG Source Estimation Using the Fourth Order MUSIC Method},
year={2002},
volume={E85-D},
number={1},
pages={167-174},
abstract={In recent years, several inverse solutions of magnetoencephalography (MEG) have been proposed. Among them, the multiple signal classification (MUSIC) method utilizes spatio-temporal information obtained from magnetic fields. The conventional MUSIC method is, however, sensitive to Gaussian noise and a sufficiently large signal-to-noise ratio (SNR) is required to estimate the number of sources and to specify the precise locations of electrical neural activities. In this paper, a new algorithm for solving the inverse problem using the fourth order MUSIC (FO-MUSIC) method is proposed. We apply it to the MEG source estimation problem. Numerical simulations demonstrate that the proposed FO-MUSIC algorithm is more robust against Gaussian noise than the conventional MUSIC algorithm.},
keywords={},
doi={},
ISSN={},
month={January},}
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TY - JOUR
TI - MEG Source Estimation Using the Fourth Order MUSIC Method
T2 - IEICE TRANSACTIONS on Information
SP - 167
EP - 174
AU - Satoshi NIIJIMA
AU - Shoogo UENO
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2002
AB - In recent years, several inverse solutions of magnetoencephalography (MEG) have been proposed. Among them, the multiple signal classification (MUSIC) method utilizes spatio-temporal information obtained from magnetic fields. The conventional MUSIC method is, however, sensitive to Gaussian noise and a sufficiently large signal-to-noise ratio (SNR) is required to estimate the number of sources and to specify the precise locations of electrical neural activities. In this paper, a new algorithm for solving the inverse problem using the fourth order MUSIC (FO-MUSIC) method is proposed. We apply it to the MEG source estimation problem. Numerical simulations demonstrate that the proposed FO-MUSIC algorithm is more robust against Gaussian noise than the conventional MUSIC algorithm.
ER -