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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,...

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Autores principales: Li, Mingai, Zhang, Na
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
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.
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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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