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Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators
Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neuro...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7999933/ https://www.ncbi.nlm.nih.gov/pubmed/33801817 http://dx.doi.org/10.3390/s21061932 |
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author | Caicedo-Acosta, Julian Castaño, German A. Acosta-Medina, Carlos Alvarez-Meza, Andres Castellanos-Dominguez, German |
author_facet | Caicedo-Acosta, Julian Castaño, German A. Acosta-Medina, Carlos Alvarez-Meza, Andres Castellanos-Dominguez, German |
author_sort | Caicedo-Acosta, Julian |
collection | PubMed |
description | Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neurophysiological and mechanistic interpretation in MI-related applications. Thus, to understand the organization of information processing, measures of functional connectivity are used. Also, models of neural network regression prediction are becoming popular, These intend to reduce the need for extracting features manually. However, predicting MI practicing’s neurophysiological inefficiency raises several problems, like enhancing network regression performance because of the overfitting risk. Here, to increase the prediction performance, we develop a deep network regression model that includes three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning between neighboring runs. Validation is performed using functional connectivity predictors extracted from two electroencephalographic databases acquired in conditions close to real MI applications (150 users), resulting in a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability. |
format | Online Article Text |
id | pubmed-7999933 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79999332021-03-28 Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators Caicedo-Acosta, Julian Castaño, German A. Acosta-Medina, Carlos Alvarez-Meza, Andres Castellanos-Dominguez, German Sensors (Basel) Article Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neurophysiological and mechanistic interpretation in MI-related applications. Thus, to understand the organization of information processing, measures of functional connectivity are used. Also, models of neural network regression prediction are becoming popular, These intend to reduce the need for extracting features manually. However, predicting MI practicing’s neurophysiological inefficiency raises several problems, like enhancing network regression performance because of the overfitting risk. Here, to increase the prediction performance, we develop a deep network regression model that includes three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning between neighboring runs. Validation is performed using functional connectivity predictors extracted from two electroencephalographic databases acquired in conditions close to real MI applications (150 users), resulting in a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability. MDPI 2021-03-10 /pmc/articles/PMC7999933/ /pubmed/33801817 http://dx.doi.org/10.3390/s21061932 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Caicedo-Acosta, Julian Castaño, German A. Acosta-Medina, Carlos Alvarez-Meza, Andres Castellanos-Dominguez, German Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators |
title | Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators |
title_full | Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators |
title_fullStr | Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators |
title_full_unstemmed | Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators |
title_short | Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators |
title_sort | deep neural regression prediction of motor imagery skills using eeg functional connectivity indicators |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7999933/ https://www.ncbi.nlm.nih.gov/pubmed/33801817 http://dx.doi.org/10.3390/s21061932 |
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