Summary
|
For Full-Text PDF, please login, if you are a member of IEICE, or go to Pay Per View on menu list, if you are a nonmember of IEICE. |
|
Accuracy of Two-Dipole Source Localization Using a Method Combining BP Neural Network with NLS Method from 32-Channel EEGs Zhuoming LI Xiaoxiao BAI Qinyu ZHANG Masatake AKUTAGAWA Fumio SHICHIJO Yohsuke KINOUCHI Publication IEICE TRANSACTIONS on Information and Systems Vol.E89-D No.7 pp.2234-2242 Publication Date: 2006/07/01 Online ISSN: 1745-1361 DOI: 10.1093/ietisy/e89-d.7.2234 Print ISSN: 0916-8532 Type of Manuscript: PAPER Category: Human-computer Interaction Keyword: EEG, brain source localization, dipole, neural network, nonlinear least-square method, Full Text: PDF(640.9KB)>>
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. | ||||||||||
| ||||||||||
