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Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling
Motor imagery-based brain–computer interfaces (MI-BCIs) send commands to a computer using the brain activity registered when a subject imagines—but does not perform—a given movement. However, inconsistent MI-BCI performance occurs in variations of brain signals across subjects and experiments; this...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438792/ https://www.ncbi.nlm.nih.gov/pubmed/32903663 http://dx.doi.org/10.3389/fnhum.2020.00321 |
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author | Lee, Minji Yoon, Jae-Geun Lee, Seong-Whan |
author_facet | Lee, Minji Yoon, Jae-Geun Lee, Seong-Whan |
author_sort | Lee, Minji |
collection | PubMed |
description | Motor imagery-based brain–computer interfaces (MI-BCIs) send commands to a computer using the brain activity registered when a subject imagines—but does not perform—a given movement. However, inconsistent MI-BCI performance occurs in variations of brain signals across subjects and experiments; this is considered to be a significant problem in practical BCI. Moreover, some subjects exhibit a phenomenon referred to as “BCI-inefficiency,” in which they are unable to generate brain signals for BCI control. These subjects have significant difficulties in using BCI. The primary goal of this study is to identify the connections of the resting-state network that affect MI performance and predict MI performance using these connections. We used a public database of MI, which includes the results of psychological questionnaires and pre-experimental resting-state taken over two sessions on different days. A dynamic causal model was used to calculate the coupling strengths between brain regions with directionality. Specifically, we investigated the motor network in resting-state, including the dorsolateral prefrontal cortex, which performs motor planning. As a result, we observed a significant difference in the connectivity strength from the supplementary motor area to the right dorsolateral prefrontal cortex between the low- and high-MI performance groups. This coupling, measured in the resting-state, is significantly stronger in the high-MI performance group than the low-MI performance group. The connection strength is positively correlated with MI-BCI performance (Session 1: r = 0.54; Session 2: r = 0.42). We also predicted MI performance using linear regression based on this connection (r-squared = 0.31). The proposed predictors, based on dynamic causal modeling, can develop new strategies for improving BCI performance. These findings can further our understanding of BCI-inefficiency and help BCI users to lower costs and save time. |
format | Online Article Text |
id | pubmed-7438792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74387922020-09-03 Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling Lee, Minji Yoon, Jae-Geun Lee, Seong-Whan Front Hum Neurosci Neuroscience Motor imagery-based brain–computer interfaces (MI-BCIs) send commands to a computer using the brain activity registered when a subject imagines—but does not perform—a given movement. However, inconsistent MI-BCI performance occurs in variations of brain signals across subjects and experiments; this is considered to be a significant problem in practical BCI. Moreover, some subjects exhibit a phenomenon referred to as “BCI-inefficiency,” in which they are unable to generate brain signals for BCI control. These subjects have significant difficulties in using BCI. The primary goal of this study is to identify the connections of the resting-state network that affect MI performance and predict MI performance using these connections. We used a public database of MI, which includes the results of psychological questionnaires and pre-experimental resting-state taken over two sessions on different days. A dynamic causal model was used to calculate the coupling strengths between brain regions with directionality. Specifically, we investigated the motor network in resting-state, including the dorsolateral prefrontal cortex, which performs motor planning. As a result, we observed a significant difference in the connectivity strength from the supplementary motor area to the right dorsolateral prefrontal cortex between the low- and high-MI performance groups. This coupling, measured in the resting-state, is significantly stronger in the high-MI performance group than the low-MI performance group. The connection strength is positively correlated with MI-BCI performance (Session 1: r = 0.54; Session 2: r = 0.42). We also predicted MI performance using linear regression based on this connection (r-squared = 0.31). The proposed predictors, based on dynamic causal modeling, can develop new strategies for improving BCI performance. These findings can further our understanding of BCI-inefficiency and help BCI users to lower costs and save time. Frontiers Media S.A. 2020-08-06 /pmc/articles/PMC7438792/ /pubmed/32903663 http://dx.doi.org/10.3389/fnhum.2020.00321 Text en Copyright © 2020 Lee, Yoon and Lee. http://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 | Neuroscience Lee, Minji Yoon, Jae-Geun Lee, Seong-Whan Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling |
title | Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling |
title_full | Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling |
title_fullStr | Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling |
title_full_unstemmed | Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling |
title_short | Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling |
title_sort | predicting motor imagery performance from resting-state eeg using dynamic causal modeling |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438792/ https://www.ncbi.nlm.nih.gov/pubmed/32903663 http://dx.doi.org/10.3389/fnhum.2020.00321 |
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