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A dynamic directed transfer function for brain functional network-based feature extraction
Directed transfer function (DTF) is good at characterizing the pairwise interactions from whole brain network and has been applied in discrimination of motor imagery (MI) tasks. Considering the fact that MI electroencephalogram signals are more non-stationary in frequency domain than in time domain,...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8933605/ https://www.ncbi.nlm.nih.gov/pubmed/35304652 http://dx.doi.org/10.1186/s40708-022-00154-8 |
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author | Li, Mingai Zhang, Na |
author_facet | Li, Mingai Zhang, Na |
author_sort | Li, Mingai |
collection | PubMed |
description | Directed transfer function (DTF) is good at characterizing the pairwise interactions from whole brain network and has been applied in discrimination of motor imagery (MI) tasks. Considering the fact that MI electroencephalogram signals are more non-stationary in frequency domain than in time domain, and the activated intensities of α band (8–13 Hz) and β band [13–30 Hz, with [Formula: see text] (13–21 Hz) and [Formula: see text] (21–30 Hz) included] have considerable differences for different subjects, a dynamic DTF (DDTF) with variable model order and frequency band is proposed to construct the brain functional networks (BFNs), whose information flows and outflows are further calculated as network features and evaluated by support vector machine. Extensive experiments are conducted based on a public BCI competition dataset and a real-world dataset, the highest recognition rate achieve 100% and 86%, respectively. The experimental results suggest that DDTF can reflect the dynamic evolution of BFN, the best subject-based DDTF appears in one of four frequency sub-bands (α, β, [Formula: see text] [Formula: see text] ) for discrimination of MI tasks and is much more related to the current and previous states. Besides, DDTF is superior compared to granger causality-based and traditional feature extraction methods, the t-test and Kappa values show its statistical significance and high consistency as well. |
format | Online Article Text |
id | pubmed-8933605 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-89336052022-04-01 A dynamic directed transfer function for brain functional network-based feature extraction Li, Mingai Zhang, Na Brain Inform Research Directed transfer function (DTF) is good at characterizing the pairwise interactions from whole brain network and has been applied in discrimination of motor imagery (MI) tasks. Considering the fact that MI electroencephalogram signals are more non-stationary in frequency domain than in time domain, and the activated intensities of α band (8–13 Hz) and β band [13–30 Hz, with [Formula: see text] (13–21 Hz) and [Formula: see text] (21–30 Hz) included] have considerable differences for different subjects, a dynamic DTF (DDTF) with variable model order and frequency band is proposed to construct the brain functional networks (BFNs), whose information flows and outflows are further calculated as network features and evaluated by support vector machine. Extensive experiments are conducted based on a public BCI competition dataset and a real-world dataset, the highest recognition rate achieve 100% and 86%, respectively. The experimental results suggest that DDTF can reflect the dynamic evolution of BFN, the best subject-based DDTF appears in one of four frequency sub-bands (α, β, [Formula: see text] [Formula: see text] ) for discrimination of MI tasks and is much more related to the current and previous states. Besides, DDTF is superior compared to granger causality-based and traditional feature extraction methods, the t-test and Kappa values show its statistical significance and high consistency as well. Springer Berlin Heidelberg 2022-03-18 /pmc/articles/PMC8933605/ /pubmed/35304652 http://dx.doi.org/10.1186/s40708-022-00154-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Li, Mingai Zhang, Na A dynamic directed transfer function for brain functional network-based feature extraction |
title | A dynamic directed transfer function for brain functional network-based feature extraction |
title_full | A dynamic directed transfer function for brain functional network-based feature extraction |
title_fullStr | A dynamic directed transfer function for brain functional network-based feature extraction |
title_full_unstemmed | A dynamic directed transfer function for brain functional network-based feature extraction |
title_short | A dynamic directed transfer function for brain functional network-based feature extraction |
title_sort | dynamic directed transfer function for brain functional network-based feature extraction |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8933605/ https://www.ncbi.nlm.nih.gov/pubmed/35304652 http://dx.doi.org/10.1186/s40708-022-00154-8 |
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