Accuracy of Two-Dipole Source Localization Using a Method Combining BP Neural Network with NLS Method from 32-Channel EEGs
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- LI Zhuoming
- Department of Electrical and Electronic Engineering, the University of Tokushima
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- BAI Xiaoxiao
- Department of Electrical and Electronic Engineering, the University of Tokushima
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- ZHANG Qinyu
- Shenzhen Graduate School of Harbin Institute of Tech.
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- AKUTAGAWA Masatake
- Department of Electrical and Electronic Engineering, the University of Tokushima
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- SHICHIJO Fumio
- Suzue Hospital
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- KINOUCHI Yohsuke
- Department of Electrical and Electronic Engineering, the University of Tokushima
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説明
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, 0.8cm, 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.
収録刊行物
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- IEICE transactions on information and systems
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IEICE transactions on information and systems 89 (7), 2234-2242, 2006-07-01
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詳細情報 詳細情報について
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- CRID
- 1573105977363710720
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- NII論文ID
- 110007541113
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- NII書誌ID
- AA10826272
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- ISSN
- 09168532
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- 本文言語コード
- en
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- データソース種別
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- CiNii Articles
