Artificial neural networks for source localization in the human brain

Abeyratne, Udantha R., Kinouchi, Yohsuke, Oki, Hideo, Okada, Jun, Shichijo, Fumio and Matsumoto, Keizo (1991) Artificial neural networks for source localization in the human brain. Brain Topography, 4 1: 3-21. doi:10.1007/BF01129661

Author Abeyratne, Udantha R.
Kinouchi, Yohsuke
Oki, Hideo
Okada, Jun
Shichijo, Fumio
Matsumoto, Keizo
Title Artificial neural networks for source localization in the human brain
Journal name Brain Topography   Check publisher's open access policy
ISSN 0896-0267
Publication date 1991
Sub-type Article (original research)
DOI 10.1007/BF01129661
Volume 4
Issue 1
Start page 3
End page 21
Total pages 19
Place of publication New York, NY United States
Publisher Springer New York LLC
Collection year 1991
Language eng
Formatted abstract
Source localization in the brain remains an ill-posed problem unless further constraints about the type of sources and the head model are imposed. Human head is modeled in various ways depending critically on the computing power available and/or the required level of accuracy. Sophisticated and truly representative models may yield more accurate results in general, but at the cost of prohibitively long computer times and huge memory requirements. In conventional source localization techniques, solution source parameters are taken as those which minimize an index of performance, defined relative to the model-generated and clinically measured voltages. We propose the use of a neural network in the place of commonly employed minimization algorithms such as the Simplex Method and the Marquardt algorithm, which are iterative and time consuming. With the aid of the error-backpropagation technique, a neural network is trained to compute source parameters, starting from a voltage set measured on the scalp. Here we describe the methods of training the neural network and investigate its localization accuracy. Based on the results of extensive studies, we conclude that neural networks are highly feasible as source localizers. A trained neural network's independence of localization speed from the head model, and the rapid localization ability, makes it possible to employ the most complex head model with the ease of the simplest model. No initial parameters need to be guessed in order to start the calculation, implying a possible automation of the entire localization process. One may train the network on experimental data, if available, thereby possibly doing away with head models.
Keyword Source Localization
Optimization Techniques
Sophisticated Head Models
Localization Accuracy
Neural Networks
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Information Technology and Electrical Engineering Publications
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Created: Wed, 14 Nov 2012, 13:44:15 EST by Dr Udantha Abeyratne on behalf of School of Information Technol and Elec Engineering