The Experience of Developing an Adaptive Hybrid EEG Signal Filter with Extended Information Adaptability

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Abstract

This article discusses the development of a hybrid EEG signal filter based on independent component analysis (ICA) and wavelet transform. The purpose of the filter is to remove artifacts from EEG signals caused by physiological processes that can be identified by synchronous time series data. The article describes the algorithm and justifies the suitability of the method for the task. Empirical results from real experimental studies are also presented.

General Information

Keywords: signal filtering, adaptive algorithms, multi-scale analysis, electroencephalography (EEG), independent component analysis

Journal rubric: Data Analysis

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2024140206

Received: 13.05.2024

Accepted:

For citation: Yuryev G.A. The Experience of Developing an Adaptive Hybrid EEG Signal Filter with Extended Information Adaptability. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2024. Vol. 14, no. 2, pp. 98–113. DOI: 10.17759/mda.2024140206. (In Russ., аbstr. in Engl.)

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Information About the Authors

Grigory A. Yuryev, PhD in Physics and Matematics, Associate Professor, Head of Department of the Computer Science Faculty, Leading Researcher, Youth Laboratory Information Technologies for Psychological Diagnostics, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-2960-6562, e-mail: g.a.yuryev@gmail.com

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