TSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals

Educational theory claims that integrating learning style into learning-related activities can improve academic performance. Traditional methods to recognize learning styles are mostly based on questionnaires and online behavior analyses. These methods are highly subjective and inaccurate in terms o...

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Autores principales:	Bingxue Zhang, Yang Shi, Longfeng Hou, Zhong Yin, Chengliang Chai
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	learning style EEG signal deep learning one-dimensional spatio-temporal convolution multi-scale feature extraction Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/c62f2e11381748f7bd7adf59346ec2a8
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id	oai:doaj.org-article:c62f2e11381748f7bd7adf59346ec2a8
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spelling	oai:doaj.org-article:c62f2e11381748f7bd7adf59346ec2a82021-11-25T16:56:32ZTSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals10.3390/brainsci111113972076-3425https://doaj.org/article/c62f2e11381748f7bd7adf59346ec2a82021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3425/11/11/1397https://doaj.org/toc/2076-3425Educational theory claims that integrating learning style into learning-related activities can improve academic performance. Traditional methods to recognize learning styles are mostly based on questionnaires and online behavior analyses. These methods are highly subjective and inaccurate in terms of recognition. Electroencephalography (EEG) signals have significant potential for use in the measurement of learning style. This study uses EEG signals to design a deep-learning-based model of recognition to recognize people’s learning styles with EEG features by using a non-overlapping sliding window, one-dimensional spatio-temporal convolutions, multi-scale feature extraction, global average pooling, and the group voting mechanism; this model is named the TSMG model (Temporal-Spatial-Multiscale-Global model). It solves the problem of processing EEG data of variable length, and improves the accuracy of recognition of the learning style by nearly 5% compared with prevalent methods, while reducing the cost of calculation by 41.93%. The proposed TSMG model can also recognize variable-length data in other fields. The authors also formulated a dataset of EEG signals (called the LSEEG dataset) containing features of the learning style processing dimension that can be used to test and compare models of recognition. This dataset is also conducive to the application and further development of EEG technology to recognize people’s learning styles.Bingxue ZhangYang ShiLongfeng HouZhong YinChengliang ChaiMDPI AGarticlelearning styleEEG signaldeep learningone-dimensional spatio-temporal convolutionmulti-scale feature extractionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENBrain Sciences, Vol 11, Iss 1397, p 1397 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	learning style EEG signal deep learning one-dimensional spatio-temporal convolution multi-scale feature extraction Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	learning style EEG signal deep learning one-dimensional spatio-temporal convolution multi-scale feature extraction Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Bingxue Zhang Yang Shi Longfeng Hou Zhong Yin Chengliang Chai TSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals
description	Educational theory claims that integrating learning style into learning-related activities can improve academic performance. Traditional methods to recognize learning styles are mostly based on questionnaires and online behavior analyses. These methods are highly subjective and inaccurate in terms of recognition. Electroencephalography (EEG) signals have significant potential for use in the measurement of learning style. This study uses EEG signals to design a deep-learning-based model of recognition to recognize people’s learning styles with EEG features by using a non-overlapping sliding window, one-dimensional spatio-temporal convolutions, multi-scale feature extraction, global average pooling, and the group voting mechanism; this model is named the TSMG model (Temporal-Spatial-Multiscale-Global model). It solves the problem of processing EEG data of variable length, and improves the accuracy of recognition of the learning style by nearly 5% compared with prevalent methods, while reducing the cost of calculation by 41.93%. The proposed TSMG model can also recognize variable-length data in other fields. The authors also formulated a dataset of EEG signals (called the LSEEG dataset) containing features of the learning style processing dimension that can be used to test and compare models of recognition. This dataset is also conducive to the application and further development of EEG technology to recognize people’s learning styles.
format	article
author	Bingxue Zhang Yang Shi Longfeng Hou Zhong Yin Chengliang Chai
author_facet	Bingxue Zhang Yang Shi Longfeng Hou Zhong Yin Chengliang Chai
author_sort	Bingxue Zhang
title	TSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals
title_short	TSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals
title_full	TSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals
title_fullStr	TSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals
title_full_unstemmed	TSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals
title_sort	tsmg: a deep learning framework for recognizing human learning style using eeg signals
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c62f2e11381748f7bd7adf59346ec2a8
work_keys_str_mv	AT bingxuezhang tsmgadeeplearningframeworkforrecognizinghumanlearningstyleusingeegsignals AT yangshi tsmgadeeplearningframeworkforrecognizinghumanlearningstyleusingeegsignals AT longfenghou tsmgadeeplearningframeworkforrecognizinghumanlearningstyleusingeegsignals AT zhongyin tsmgadeeplearningframeworkforrecognizinghumanlearningstyleusingeegsignals AT chengliangchai tsmgadeeplearningframeworkforrecognizinghumanlearningstyleusingeegsignals
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TSMG: A Deep Learning Framework for Recognizing Human Learning Style Using EEG Signals

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