Statistical Detection of Movement Activities in a Human Brain by Moving Separation of Mixture Distributions*
- Authors: Gorshenin A.K.1,2, Korolev V.Y.3,1, Korchagin A.Y.3, Zakharova T.V.3, Zeifman A.I.1,4
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Affiliations:
- Institute of Informatics Problems of Federal Research Center “Informatics and Control”, Russian Academy of Sciences
- Federal State Budget Educational Institution of Higher Education “Moscow Technological University”
- Faculty of Computational Mathematics and Cybernetics, Moscow State University
- Vologda State University
- Issue: Vol 218, No 3 (2016)
- Pages: 278-286
- Section: Article
- URL: https://journals.rcsi.science/1072-3374/article/view/238217
- DOI: https://doi.org/10.1007/s10958-016-3029-1
- ID: 238217
Cite item
Abstract
One of the most popular experimental techniques for investigation of brain activity is the so-called method of evoked potentials: the subject repeatedly makes some movements (by his/her finger), whereas brain activity and some auxiliary signals are recorded for further analysis. The key problem is the detection of points in the myogram that correspond to the beginning of the movements. The more precisely the points are detected, the more successfully the magnetoencephalogram is processed aiming at the identification of sensors that are closest to the activity areas.
This paper proposes a statistical approach to this problem based on mixtures models that uses a specially modified method of moving separation of mixtures of probability distributions (MSMmethod) to detect the start points of the finger’s movements. We demonstrate the correctness of the new procedure and its advantages as compared with the method based on the notion of the myogram window variance.
About the authors
A. K. Gorshenin
Institute of Informatics Problems of Federal Research Center “Informatics and Control”, Russian Academy of Sciences; Federal State Budget Educational Institution of Higher Education “Moscow Technological University”
Author for correspondence.
Email: a.k.gorshenin@gmail.com
Russian Federation, Moscow; Moscow
V. Yu. Korolev
Faculty of Computational Mathematics and Cybernetics, Moscow State University; Institute of Informatics Problems of Federal Research Center “Informatics and Control”, Russian Academy of Sciences
Email: a.k.gorshenin@gmail.com
Russian Federation, Moscow; Moscow
A. Yu. Korchagin
Faculty of Computational Mathematics and Cybernetics, Moscow State University
Email: a.k.gorshenin@gmail.com
Russian Federation, Moscow
T. V. Zakharova
Faculty of Computational Mathematics and Cybernetics, Moscow State University
Email: a.k.gorshenin@gmail.com
Russian Federation, Moscow
A. I. Zeifman
Institute of Informatics Problems of Federal Research Center “Informatics and Control”, Russian Academy of Sciences; Vologda State University
Email: a.k.gorshenin@gmail.com
Russian Federation, Moscow; Vologda