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Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network
In recent years, the development of adaptive models to tailor instructional content to learners by measuring their cognitive load has become a topic of active research. Brain fog, also known as confusion, is a common cause of poor performance, and real-time detection of confusion is a challenging an...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10044910/ https://www.ncbi.nlm.nih.gov/pubmed/36978752 http://dx.doi.org/10.3390/bioengineering10030361 |
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author | Yoo, Gilsang Kim, Hyeoncheol Hong, Sungdae |
author_facet | Yoo, Gilsang Kim, Hyeoncheol Hong, Sungdae |
author_sort | Yoo, Gilsang |
collection | PubMed |
description | In recent years, the development of adaptive models to tailor instructional content to learners by measuring their cognitive load has become a topic of active research. Brain fog, also known as confusion, is a common cause of poor performance, and real-time detection of confusion is a challenging and important task for applications in online education and driver fatigue detection. In this study, we propose a deep learning method for cognitive load recognition based on electroencephalography (EEG) signals using a long short-term memory network (LSTM) with an attention mechanism. We obtained EEG signal data from a database of brainwave information and associated data on mental load. We evaluated the performance of the proposed LSTM technique in comparison with random forest, Adaptive Boosting (AdaBoost), support vector machine, eXtreme Gradient Boosting (XGBoost), and artificial neural network models. The experimental results demonstrated that the proposed approach had the highest accuracy of 87.1% compared to those of other algorithms, including random forest (64%), AdaBoost (64.31%), support vector machine (60.9%), XGBoost (67.3%), and artificial neural network models (71.4%). The results of this study support the development of a personalized adaptive learning system designed to measure and actively respond to learners’ cognitive load in real time using wireless portable EEG systems. |
format | Online Article Text |
id | pubmed-10044910 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100449102023-03-29 Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network Yoo, Gilsang Kim, Hyeoncheol Hong, Sungdae Bioengineering (Basel) Article In recent years, the development of adaptive models to tailor instructional content to learners by measuring their cognitive load has become a topic of active research. Brain fog, also known as confusion, is a common cause of poor performance, and real-time detection of confusion is a challenging and important task for applications in online education and driver fatigue detection. In this study, we propose a deep learning method for cognitive load recognition based on electroencephalography (EEG) signals using a long short-term memory network (LSTM) with an attention mechanism. We obtained EEG signal data from a database of brainwave information and associated data on mental load. We evaluated the performance of the proposed LSTM technique in comparison with random forest, Adaptive Boosting (AdaBoost), support vector machine, eXtreme Gradient Boosting (XGBoost), and artificial neural network models. The experimental results demonstrated that the proposed approach had the highest accuracy of 87.1% compared to those of other algorithms, including random forest (64%), AdaBoost (64.31%), support vector machine (60.9%), XGBoost (67.3%), and artificial neural network models (71.4%). The results of this study support the development of a personalized adaptive learning system designed to measure and actively respond to learners’ cognitive load in real time using wireless portable EEG systems. MDPI 2023-03-15 /pmc/articles/PMC10044910/ /pubmed/36978752 http://dx.doi.org/10.3390/bioengineering10030361 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yoo, Gilsang Kim, Hyeoncheol Hong, Sungdae Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network |
title | Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network |
title_full | Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network |
title_fullStr | Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network |
title_full_unstemmed | Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network |
title_short | Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network |
title_sort | prediction of cognitive load from electroencephalography signals using long short-term memory network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10044910/ https://www.ncbi.nlm.nih.gov/pubmed/36978752 http://dx.doi.org/10.3390/bioengineering10030361 |
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