Systems and Means of Informatics
2018, Volume 28, Issue 3, pp 7285
EVALUATION OF THE WAVEFORM IN THE MAGNETOENCEFALOGRAPHIC SIGNALS WITH NOISE IN THE FORM OF A FINITE NORMAL MIXTURE
Abstract
The article is devoted to the methods of evaluation of the waveform (or activation pattern) that occurs in the signals of the subject in response to an external stimulus during a neurophysiological experiment. The peculiarity of the mathematical model considered is the representation of noise in terms of finite normal mixtures. This approach is more consistent with the experimental data. Within the framework of the considered model, the analysis of different waveform estimators is carried out and their statistical properties are investigated. Several new estimators that utilize features of noise distribution were proposed.
The author proved higher efficiency of new estimators in terms of variance amount in relation to the method of moments which is widely used in studies. Despite the fact that the article focuses on the application of estimators to the magnetoencephalographic signals, the considered methods can be used in similar applications for any neurophysiological signals as well as in the most general setting  for bias estimation of a finite normal mixture.
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[+] About this article
Title
EVALUATION OF THE WAVEFORM IN THE MAGNETOENCEFALOGRAPHIC SIGNALS WITH NOISE IN THE FORM OF A FINITE NORMAL MIXTURE
Journal
Systems and Means of Informatics
Volume 28, Issue 3, pp 7285
Cover Date
20180930
DOI
10.14357/08696527180306
Print ISSN
08696527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
magnetoencephalography; signal processing; finite Gaussian mixtures; point estimators; efficient estimators; Fisher information; estimation of distribution bias
Authors
M. B. Goncharenko
Author Affiliations
Department of Mathematical Statistics, Faculty of Computational Mathematics and Cybernetics, M. V. Lomonosov Moscow State University, 152 Leninskiye Gory, GSP1, Moscow 119991, Russian Federation
