The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo investiga a modelagem da não linearidade na geração do sinal de potencial evocado de tentativa única (s-EP) por meio do uso de uma rede neural de função de base radial mista (M-RBFN). Mais ênfase é colocada na contribuição do termo EEG espontâneo para o sinal s-EP. O método é baseado em um modelo de rede neural não linear M-RBFN que é treinado simultaneamente com os diferentes segmentos de dados de EEG/EP. Então, a saída do modelo treinado (estimador) é um modelo não linear ajustado e reduzido (otimizado) e fornece uma representação global da dinâmica de passagem entre a atividade cerebral espontânea e os períodos pós-estímulo. O desempenho do método de rede neural proposto é avaliado usando uma simulação realista e aplicado a uma medição real de EEG/EP.
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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
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@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},}
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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 -