IEICE TRANSACTIONS on Information
Multi-Dipole Sources Identification from EEG Topography Using System Identification Method
Summary :
The goal of source localization in the brain is to estimate a set of parameters for representing source characteristics; one of such parameters is the source number. We here propose a method combining the Powell algorithm with the information criterion method for determining the optimal dipole number. The potential errors can be calculated by the Powell algorithm with the concentric 4-sphere head model and 32 electrodes, then the number of dipoles is determined by the information criterion method with the potential errors mentioned above. This method has the advantages of a high identification accuracy of dipole number and a small number of EEG data because in this method: (1) only one EEG topography is used in the computation, (2) 32 electrodes are used to obtain the EEG data, (3) the optimal dipole number can be obtained by this method. In order to prove our method to be efficient, precise and robust to noise, 10% white noise is introduced to test this method theoretically. Some investigations are presented here to show our method is an advanced approach for determining the optimal dipole number.
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- IEICE TRANSACTIONS on Information Vol.E87-D No.6 pp.1566-1574
- Publication Date
- 2004/06/01
- Publicized
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- Type of Manuscript
- PAPER
- Category
- Biological Engineering
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Xiaoxiao BAI, Qinyu ZHANG, Yohsuke KINOUCHI, Tadayoshi MINATO, "Multi-Dipole Sources Identification from EEG Topography Using System Identification Method" in IEICE TRANSACTIONS on Information,
vol. E87-D, no. 6, pp. 1566-1574, June 2004, doi: .
Abstract: The goal of source localization in the brain is to estimate a set of parameters for representing source characteristics; one of such parameters is the source number. We here propose a method combining the Powell algorithm with the information criterion method for determining the optimal dipole number. The potential errors can be calculated by the Powell algorithm with the concentric 4-sphere head model and 32 electrodes, then the number of dipoles is determined by the information criterion method with the potential errors mentioned above. This method has the advantages of a high identification accuracy of dipole number and a small number of EEG data because in this method: (1) only one EEG topography is used in the computation, (2) 32 electrodes are used to obtain the EEG data, (3) the optimal dipole number can be obtained by this method. In order to prove our method to be efficient, precise and robust to noise, 10% white noise is introduced to test this method theoretically. Some investigations are presented here to show our method is an advanced approach for determining the optimal dipole number.
URL: https://global.ieice.org/en_transactions/information/10.1587/e87-d_6_1566/_p
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@ARTICLE{e87-d_6_1566,
author={Xiaoxiao BAI, Qinyu ZHANG, Yohsuke KINOUCHI, Tadayoshi MINATO, },
journal={IEICE TRANSACTIONS on Information},
title={Multi-Dipole Sources Identification from EEG Topography Using System Identification Method},
year={2004},
volume={E87-D},
number={6},
pages={1566-1574},
abstract={The goal of source localization in the brain is to estimate a set of parameters for representing source characteristics; one of such parameters is the source number. We here propose a method combining the Powell algorithm with the information criterion method for determining the optimal dipole number. The potential errors can be calculated by the Powell algorithm with the concentric 4-sphere head model and 32 electrodes, then the number of dipoles is determined by the information criterion method with the potential errors mentioned above. This method has the advantages of a high identification accuracy of dipole number and a small number of EEG data because in this method: (1) only one EEG topography is used in the computation, (2) 32 electrodes are used to obtain the EEG data, (3) the optimal dipole number can be obtained by this method. In order to prove our method to be efficient, precise and robust to noise, 10% white noise is introduced to test this method theoretically. Some investigations are presented here to show our method is an advanced approach for determining the optimal dipole number.},
keywords={},
doi={},
ISSN={},
month={June},}
Copy
TY - JOUR
TI - Multi-Dipole Sources Identification from EEG Topography Using System Identification Method
T2 - IEICE TRANSACTIONS on Information
SP - 1566
EP - 1574
AU - Xiaoxiao BAI
AU - Qinyu ZHANG
AU - Yohsuke KINOUCHI
AU - Tadayoshi MINATO
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2004
AB - The goal of source localization in the brain is to estimate a set of parameters for representing source characteristics; one of such parameters is the source number. We here propose a method combining the Powell algorithm with the information criterion method for determining the optimal dipole number. The potential errors can be calculated by the Powell algorithm with the concentric 4-sphere head model and 32 electrodes, then the number of dipoles is determined by the information criterion method with the potential errors mentioned above. This method has the advantages of a high identification accuracy of dipole number and a small number of EEG data because in this method: (1) only one EEG topography is used in the computation, (2) 32 electrodes are used to obtain the EEG data, (3) the optimal dipole number can be obtained by this method. In order to prove our method to be efficient, precise and robust to noise, 10% white noise is introduced to test this method theoretically. Some investigations are presented here to show our method is an advanced approach for determining the optimal dipole number.
ER -
English
