IEICE TRANSACTIONS on Information
Accuracy of Two-Dipole Source Localization Using a Method Combining BP Neural Network with NLS Method from 32-Channel EEGs
Summary :
The electroencephalogram (EEG) has become a widely used tool for investigating brain function. Brain signal source localization is a process of inverse calculation from sensor information (electric potentials for EEG) to the identification of multiple brain sources to obtain the locations and orientation parameters. In this paper, we describe a combination of the backpropagation neural network (BPNN) with the nonlinear least-square (NLS) method to localize two dipoles with reasonable accuracy and speed from EEG data computerized by two dipoles randomly positioned in the brain. The trained BPNN, obtains the initial values for the two dipoles through fast calculation and also avoids the influence of noise. Then the NLS method (Powell algorithm) is used to accurately estimate the two dipole parameters. In this study, we also obtain the minimum distance between the assumed dipole pair, 0.8 cm, in order to localize two sources from a smaller limited distance between the dipole pair. The present simulation results demonstrate that the combined method can allow us to localize two dipoles with high speed and accuracy, that is, in 20 seconds and with the position error of around 6.5%, and to reduce the influence of noise.
- Publication
- IEICE TRANSACTIONS on Information Vol.E89-D No.7 pp.2234-2242
- Publication Date
- 2006/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e89-d.7.2234
- Type of Manuscript
- PAPER
- Category
- Human-computer Interaction
Authors
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Zhuoming LI, Xiaoxiao BAI, Qinyu ZHANG, Masatake AKUTAGAWA, Fumio SHICHIJO, Yohsuke KINOUCHI, "Accuracy of Two-Dipole Source Localization Using a Method Combining BP Neural Network with NLS Method from 32-Channel EEGs" in IEICE TRANSACTIONS on Information,
vol. E89-D, no. 7, pp. 2234-2242, July 2006, doi: 10.1093/ietisy/e89-d.7.2234.
Abstract: The electroencephalogram (EEG) has become a widely used tool for investigating brain function. Brain signal source localization is a process of inverse calculation from sensor information (electric potentials for EEG) to the identification of multiple brain sources to obtain the locations and orientation parameters. In this paper, we describe a combination of the backpropagation neural network (BPNN) with the nonlinear least-square (NLS) method to localize two dipoles with reasonable accuracy and speed from EEG data computerized by two dipoles randomly positioned in the brain. The trained BPNN, obtains the initial values for the two dipoles through fast calculation and also avoids the influence of noise. Then the NLS method (Powell algorithm) is used to accurately estimate the two dipole parameters. In this study, we also obtain the minimum distance between the assumed dipole pair, 0.8 cm, in order to localize two sources from a smaller limited distance between the dipole pair. The present simulation results demonstrate that the combined method can allow us to localize two dipoles with high speed and accuracy, that is, in 20 seconds and with the position error of around 6.5%, and to reduce the influence of noise.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.7.2234/_p
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@ARTICLE{e89-d_7_2234,
author={Zhuoming LI, Xiaoxiao BAI, Qinyu ZHANG, Masatake AKUTAGAWA, Fumio SHICHIJO, Yohsuke KINOUCHI, },
journal={IEICE TRANSACTIONS on Information},
title={Accuracy of Two-Dipole Source Localization Using a Method Combining BP Neural Network with NLS Method from 32-Channel EEGs},
year={2006},
volume={E89-D},
number={7},
pages={2234-2242},
abstract={The electroencephalogram (EEG) has become a widely used tool for investigating brain function. Brain signal source localization is a process of inverse calculation from sensor information (electric potentials for EEG) to the identification of multiple brain sources to obtain the locations and orientation parameters. In this paper, we describe a combination of the backpropagation neural network (BPNN) with the nonlinear least-square (NLS) method to localize two dipoles with reasonable accuracy and speed from EEG data computerized by two dipoles randomly positioned in the brain. The trained BPNN, obtains the initial values for the two dipoles through fast calculation and also avoids the influence of noise. Then the NLS method (Powell algorithm) is used to accurately estimate the two dipole parameters. In this study, we also obtain the minimum distance between the assumed dipole pair, 0.8 cm, in order to localize two sources from a smaller limited distance between the dipole pair. The present simulation results demonstrate that the combined method can allow us to localize two dipoles with high speed and accuracy, that is, in 20 seconds and with the position error of around 6.5%, and to reduce the influence of noise.},
keywords={},
doi={10.1093/ietisy/e89-d.7.2234},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Accuracy of Two-Dipole Source Localization Using a Method Combining BP Neural Network with NLS Method from 32-Channel EEGs
T2 - IEICE TRANSACTIONS on Information
SP - 2234
EP - 2242
AU - Zhuoming LI
AU - Xiaoxiao BAI
AU - Qinyu ZHANG
AU - Masatake AKUTAGAWA
AU - Fumio SHICHIJO
AU - Yohsuke KINOUCHI
PY - 2006
DO - 10.1093/ietisy/e89-d.7.2234
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2006
AB - The electroencephalogram (EEG) has become a widely used tool for investigating brain function. Brain signal source localization is a process of inverse calculation from sensor information (electric potentials for EEG) to the identification of multiple brain sources to obtain the locations and orientation parameters. In this paper, we describe a combination of the backpropagation neural network (BPNN) with the nonlinear least-square (NLS) method to localize two dipoles with reasonable accuracy and speed from EEG data computerized by two dipoles randomly positioned in the brain. The trained BPNN, obtains the initial values for the two dipoles through fast calculation and also avoids the influence of noise. Then the NLS method (Powell algorithm) is used to accurately estimate the two dipole parameters. In this study, we also obtain the minimum distance between the assumed dipole pair, 0.8 cm, in order to localize two sources from a smaller limited distance between the dipole pair. The present simulation results demonstrate that the combined method can allow us to localize two dipoles with high speed and accuracy, that is, in 20 seconds and with the position error of around 6.5%, and to reduce the influence of noise.
ER -
English
