n-Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation.

Abstract:

: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.

journal_name

Comput Intell Neurosci

authors

Aguilar Cruz KA,Zagaceta Álvarez MT,Palma Orozco R,Medel Juárez JJ

doi

10.1155/2018/4613740

subject

Has Abstract

pub_date

2018-01-15 00:00:00

pages

4613740

eissn

1687-5265

issn

1687-5273

journal_volume

2018

pub_type

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