The Determination of the Evoked Potential Generating Mechanism Based on Radial Basis Neural Network Model
Summary :
This paper investigates the modeling of non-linearity on the generation of the single trial evoked potential signal (s-EP) by means of using a mixed radial basis function neural network (M-RBFN). The more emphasis is put on the contribution of spontaneous EEG term to s-EP signal. The method is based on a nonlinear M-RBFN neural network model that is trained simultaneously with the different segments of EEG/EP data. Then, the output of the trained model (estimator) is a both fitted and reduced (optimized) nonlinear model and then provide a global representation of the passage dynamics between spontaneous brain activity and poststimulus periods. The performance of the proposed neural network method is evaluated using a realistic simulation and applied to a real EEG/EP measurement.
- Publication
- IEICE TRANSACTIONS on Information Vol.E83-D No.9 pp.1819-1823
- Publication Date
- 2000/09/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- LETTER
- Category
- Biocybernetics, Neurocomputing
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Rustu Murat DEMIRER, Yukio KOSUGI, Halil Ozcan GULCUR, "The Determination of the Evoked Potential Generating Mechanism Based on Radial Basis Neural Network Model" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 9, pp. 1819-1823, September 2000, doi: .
Abstract: This paper investigates the modeling of non-linearity on the generation of the single trial evoked potential signal (s-EP) by means of using a mixed radial basis function neural network (M-RBFN). The more emphasis is put on the contribution of spontaneous EEG term to s-EP signal. The method is based on a nonlinear M-RBFN neural network model that is trained simultaneously with the different segments of EEG/EP data. Then, the output of the trained model (estimator) is a both fitted and reduced (optimized) nonlinear model and then provide a global representation of the passage dynamics between spontaneous brain activity and poststimulus periods. The performance of the proposed neural network method is evaluated using a realistic simulation and applied to a real EEG/EP measurement.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_9_1819/_p
Copy
@ARTICLE{e83-d_9_1819,
author={Rustu Murat DEMIRER, Yukio KOSUGI, Halil Ozcan GULCUR, },
journal={IEICE TRANSACTIONS on Information},
title={The Determination of the Evoked Potential Generating Mechanism Based on Radial Basis Neural Network Model},
year={2000},
volume={E83-D},
number={9},
pages={1819-1823},
abstract={This paper investigates the modeling of non-linearity on the generation of the single trial evoked potential signal (s-EP) by means of using a mixed radial basis function neural network (M-RBFN). The more emphasis is put on the contribution of spontaneous EEG term to s-EP signal. The method is based on a nonlinear M-RBFN neural network model that is trained simultaneously with the different segments of EEG/EP data. Then, the output of the trained model (estimator) is a both fitted and reduced (optimized) nonlinear model and then provide a global representation of the passage dynamics between spontaneous brain activity and poststimulus periods. The performance of the proposed neural network method is evaluated using a realistic simulation and applied to a real EEG/EP measurement.},
keywords={},
doi={},
ISSN={},
month={September},}
Copy
TY - JOUR
TI - The Determination of the Evoked Potential Generating Mechanism Based on Radial Basis Neural Network Model
T2 - IEICE TRANSACTIONS on Information
SP - 1819
EP - 1823
AU - Rustu Murat DEMIRER
AU - Yukio KOSUGI
AU - Halil Ozcan GULCUR
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2000
AB - This paper investigates the modeling of non-linearity on the generation of the single trial evoked potential signal (s-EP) by means of using a mixed radial basis function neural network (M-RBFN). The more emphasis is put on the contribution of spontaneous EEG term to s-EP signal. The method is based on a nonlinear M-RBFN neural network model that is trained simultaneously with the different segments of EEG/EP data. Then, the output of the trained model (estimator) is a both fitted and reduced (optimized) nonlinear model and then provide a global representation of the passage dynamics between spontaneous brain activity and poststimulus periods. The performance of the proposed neural network method is evaluated using a realistic simulation and applied to a real EEG/EP measurement.
ER -
English
