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
A magnetoencefalografia (MEG) é uma técnica poderosa e não invasiva para medir a atividade cerebral humana com alta resolução temporal. A motivação para estudar a análise de dados MEG é extrair as características essenciais dos dados medidos e representá-las correspondendo às funções do cérebro humano. Neste artigo, é proposto um novo método de análise de dados MEG baseado na abordagem de análise de componentes independentes (ICA) com procedimentos de vários estágios de pré-processamento e pós-processamento. Além disso, vários tipos de algoritmos ICA são investigados para analisar dados de ensaio único MEG que são registrados no experimento fantasma. Os resultados analisados são apresentados para ilustrar a eficácia e o alto desempenho tanto na decomposição de fontes por abordagens ICA quanto na localização de fontes pelo método de ajuste de dipolos de corrente equivalente.
Jianting CAO
Noboru MURATA
Shun-ichi AMARI
Andrzej CICHOCKI
Tsunehiro TAKEDA
Hiroshi ENDO
Nobuyoshi HARADA
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Jianting CAO, Noboru MURATA, Shun-ichi AMARI, Andrzej CICHOCKI, Tsunehiro TAKEDA, Hiroshi ENDO, Nobuyoshi HARADA, "Single-Trial Magnetoencephalographic Data Decomposition and Localization Based on Independent Component Analysis Approach" in IEICE TRANSACTIONS on Fundamentals,
vol. E83-A, no. 9, pp. 1757-1766, September 2000, doi: .
Abstract: Magnetoencephalography (MEG) is a powerful and non-invasive technique for measuring human brain activity with a high temporal resolution. The motivation for studying MEG data analysis is to extract the essential features from measured data and represent them corresponding to the human brain functions. In this paper, a novel MEG data analysis method based on independent component analysis (ICA) approach with pre-processing and post-processing multistage procedures is proposed. Moreover, several kinds of ICA algorithms are investigated for analyzing MEG single-trial data which is recorded in the experiment of phantom. The analyzed results are presented to illustrate the effectiveness and high performance both in source decomposition by ICA approaches and source localization by equivalent current dipoles fitting method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e83-a_9_1757/_p
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@ARTICLE{e83-a_9_1757,
author={Jianting CAO, Noboru MURATA, Shun-ichi AMARI, Andrzej CICHOCKI, Tsunehiro TAKEDA, Hiroshi ENDO, Nobuyoshi HARADA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Single-Trial Magnetoencephalographic Data Decomposition and Localization Based on Independent Component Analysis Approach},
year={2000},
volume={E83-A},
number={9},
pages={1757-1766},
abstract={Magnetoencephalography (MEG) is a powerful and non-invasive technique for measuring human brain activity with a high temporal resolution. The motivation for studying MEG data analysis is to extract the essential features from measured data and represent them corresponding to the human brain functions. In this paper, a novel MEG data analysis method based on independent component analysis (ICA) approach with pre-processing and post-processing multistage procedures is proposed. Moreover, several kinds of ICA algorithms are investigated for analyzing MEG single-trial data which is recorded in the experiment of phantom. The analyzed results are presented to illustrate the effectiveness and high performance both in source decomposition by ICA approaches and source localization by equivalent current dipoles fitting method.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Single-Trial Magnetoencephalographic Data Decomposition and Localization Based on Independent Component Analysis Approach
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1757
EP - 1766
AU - Jianting CAO
AU - Noboru MURATA
AU - Shun-ichi AMARI
AU - Andrzej CICHOCKI
AU - Tsunehiro TAKEDA
AU - Hiroshi ENDO
AU - Nobuyoshi HARADA
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E83-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2000
AB - Magnetoencephalography (MEG) is a powerful and non-invasive technique for measuring human brain activity with a high temporal resolution. The motivation for studying MEG data analysis is to extract the essential features from measured data and represent them corresponding to the human brain functions. In this paper, a novel MEG data analysis method based on independent component analysis (ICA) approach with pre-processing and post-processing multistage procedures is proposed. Moreover, several kinds of ICA algorithms are investigated for analyzing MEG single-trial data which is recorded in the experiment of phantom. The analyzed results are presented to illustrate the effectiveness and high performance both in source decomposition by ICA approaches and source localization by equivalent current dipoles fitting method.
ER -