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EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms
Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS meth...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9449652/ https://www.ncbi.nlm.nih.gov/pubmed/36091378 http://dx.doi.org/10.3389/fphys.2022.910368 |
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author | Zangeneh Soroush, Morteza Tahvilian, Parisa Nasirpour, Mohammad Hossein Maghooli, Keivan Sadeghniiat-Haghighi, Khosro Vahid Harandi, Sepide Abdollahi, Zeinab Ghazizadeh, Ali Jafarnia Dabanloo, Nader |
author_facet | Zangeneh Soroush, Morteza Tahvilian, Parisa Nasirpour, Mohammad Hossein Maghooli, Keivan Sadeghniiat-Haghighi, Khosro Vahid Harandi, Sepide Abdollahi, Zeinab Ghazizadeh, Ali Jafarnia Dabanloo, Nader |
author_sort | Zangeneh Soroush, Morteza |
collection | PubMed |
description | Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information related to neural activity leaks to these sources. So, an approach should be employed to detect and suppress the artifacts and reserve neural activity. This study introduces a novel method based on EEG and Poincare planes in the phase space to detect artifactual components estimated by second-order blind identification (SOBI). Artifacts are detected using a mixture of well-known conventional classifiers and were removed employing stationary wavelet transform (SWT) to reserve neural information. The proposed method is a combination of signal processing techniques and machine learning algorithms, including multi-layer perceptron (MLP), K-nearest neighbor (KNN), naïve Bayes, and support vector machine (SVM) which have significant results while applying our proposed method to different scenarios. Simulated, semi-simulated, and real EEG signals are employed to evaluate the proposed method, and several evaluation criteria are calculated. We achieved acceptable results, for example, 98% average accuracy and 97% average sensitivity in artifactual EEG component detection or about 2% as mean square error in EEG reconstruction after artifact removal. Results showed that the proposed method is effective and can be used in future studies as we have considered different real-world scenarios to evaluate it. |
format | Online Article Text |
id | pubmed-9449652 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94496522022-09-08 EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms Zangeneh Soroush, Morteza Tahvilian, Parisa Nasirpour, Mohammad Hossein Maghooli, Keivan Sadeghniiat-Haghighi, Khosro Vahid Harandi, Sepide Abdollahi, Zeinab Ghazizadeh, Ali Jafarnia Dabanloo, Nader Front Physiol Physiology Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information related to neural activity leaks to these sources. So, an approach should be employed to detect and suppress the artifacts and reserve neural activity. This study introduces a novel method based on EEG and Poincare planes in the phase space to detect artifactual components estimated by second-order blind identification (SOBI). Artifacts are detected using a mixture of well-known conventional classifiers and were removed employing stationary wavelet transform (SWT) to reserve neural information. The proposed method is a combination of signal processing techniques and machine learning algorithms, including multi-layer perceptron (MLP), K-nearest neighbor (KNN), naïve Bayes, and support vector machine (SVM) which have significant results while applying our proposed method to different scenarios. Simulated, semi-simulated, and real EEG signals are employed to evaluate the proposed method, and several evaluation criteria are calculated. We achieved acceptable results, for example, 98% average accuracy and 97% average sensitivity in artifactual EEG component detection or about 2% as mean square error in EEG reconstruction after artifact removal. Results showed that the proposed method is effective and can be used in future studies as we have considered different real-world scenarios to evaluate it. Frontiers Media S.A. 2022-08-24 /pmc/articles/PMC9449652/ /pubmed/36091378 http://dx.doi.org/10.3389/fphys.2022.910368 Text en Copyright © 2022 Zangeneh Soroush, Tahvilian, Nasirpour, Maghooli, Sadeghniiat-Haghighi, Vahid Harandi, Abdollahi, Ghazizadeh and Jafarnia Dabanloo. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Zangeneh Soroush, Morteza Tahvilian, Parisa Nasirpour, Mohammad Hossein Maghooli, Keivan Sadeghniiat-Haghighi, Khosro Vahid Harandi, Sepide Abdollahi, Zeinab Ghazizadeh, Ali Jafarnia Dabanloo, Nader EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms |
title | EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms |
title_full | EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms |
title_fullStr | EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms |
title_full_unstemmed | EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms |
title_short | EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms |
title_sort | eeg artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9449652/ https://www.ncbi.nlm.nih.gov/pubmed/36091378 http://dx.doi.org/10.3389/fphys.2022.910368 |
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