n-Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation
Electroencephalograms (EEG) signals are of interest because of their relationship with physiological activities, allowing a description of motion, speaking, or thinking. Important research has been developed to take advantage of EEG using classification or predictor algorithms based on parameters th...
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Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2018/4613740 |
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doaj-bcbfdd2483fd4c8ea1e0355e57f3781c2020-11-24T23:06:08ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732018-01-01201810.1155/2018/46137404613740n-Iterative Exponential Forgetting Factor for EEG Signals Parameter EstimationKaren Alicia Aguilar Cruz0María Teresa Zagaceta Álvarez1Rosaura Palma Orozco2José de Jesús Medel Juárez3Centro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN), Avenida Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal, Col. Nueva Industrial Vallejo, Delegación Gustavo A. Madero, 07738 Ciudad de México, MexicoEscuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Azcapotzalco, Instituto Politécnico Nacional, Avenida de las Granjas, No. 682, Col. Santa Catarina, Delegación Azcapotzalco, 02250 Ciudad de México, MexicoEscuela Superior de Cómputo, Instituto Politécnico Nacional, Avenida Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal, Col. Lindavista, Delegación Gustavo A. Madero, 07738 Ciudad de México, MexicoCentro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN), Avenida Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal, Col. Nueva Industrial Vallejo, Delegación Gustavo A. Madero, 07738 Ciudad de México, MexicoElectroencephalograms (EEG) signals are of interest because of their relationship with physiological activities, allowing a description of motion, speaking, or thinking. Important research has been developed to take advantage of EEG using classification or predictor algorithms based on parameters that help to describe the signal behavior. Thus, great importance should be taken to feature extraction which is complicated for the Parameter Estimation (PE)–System Identification (SI) process. When based on an average approximation, nonstationary characteristics are presented. For PE the comparison of three forms of iterative-recursive uses of the Exponential Forgetting Factor (EFF) combined with a linear function to identify a synthetic stochastic signal is presented. The one with best results seen through the functional error is applied to approximate an EEG signal for a simple classification example, showing the effectiveness of our proposal.http://dx.doi.org/10.1155/2018/4613740 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Karen Alicia Aguilar Cruz María Teresa Zagaceta Álvarez Rosaura Palma Orozco José de Jesús Medel Juárez |
spellingShingle |
Karen Alicia Aguilar Cruz María Teresa Zagaceta Álvarez Rosaura Palma Orozco José de Jesús Medel Juárez n-Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation Computational Intelligence and Neuroscience |
author_facet |
Karen Alicia Aguilar Cruz María Teresa Zagaceta Álvarez Rosaura Palma Orozco José de Jesús Medel Juárez |
author_sort |
Karen Alicia Aguilar Cruz |
title |
n-Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation |
title_short |
n-Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation |
title_full |
n-Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation |
title_fullStr |
n-Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation |
title_full_unstemmed |
n-Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation |
title_sort |
n-iterative exponential forgetting factor for eeg signals parameter estimation |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5265 1687-5273 |
publishDate |
2018-01-01 |
description |
Electroencephalograms (EEG) signals are of interest because of their relationship with physiological activities, allowing a description of motion, speaking, or thinking. Important research has been developed to take advantage of EEG using classification or predictor algorithms based on parameters that help to describe the signal behavior. Thus, great importance should be taken to feature extraction which is complicated for the Parameter Estimation (PE)–System Identification (SI) process. When based on an average approximation, nonstationary characteristics are presented. For PE the comparison of three forms of iterative-recursive uses of the Exponential Forgetting Factor (EFF) combined with a linear function to identify a synthetic stochastic signal is presented. The one with best results seen through the functional error is applied to approximate an EEG signal for a simple classification example, showing the effectiveness of our proposal. |
url |
http://dx.doi.org/10.1155/2018/4613740 |
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1725624086225223680 |