Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people’s emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making i...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jing Cai, Ruolan Xiao, Wenjie Cui, Shang Zhang, Guangda Liu
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	EEG machine learning emotion recognition feature extraction classification Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/33e3a525e21a4865a5d5d8099c0e1427
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:33e3a525e21a4865a5d5d8099c0e1427
record_format	dspace
spelling	oai:doaj.org-article:33e3a525e21a4865a5d5d8099c0e14272021-11-30T12:25:08ZApplication of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review1662-513710.3389/fnsys.2021.729707https://doaj.org/article/33e3a525e21a4865a5d5d8099c0e14272021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnsys.2021.729707/fullhttps://doaj.org/toc/1662-5137Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people’s emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject’s emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016–2021.Jing CaiRuolan XiaoWenjie CuiShang ZhangGuangda LiuFrontiers Media S.A.articleEEGmachine learningemotion recognitionfeature extractionclassificationNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Systems Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	EEG machine learning emotion recognition feature extraction classification Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	EEG machine learning emotion recognition feature extraction classification Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Jing Cai Ruolan Xiao Wenjie Cui Shang Zhang Guangda Liu Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review
description	Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people’s emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject’s emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016–2021.
format	article
author	Jing Cai Ruolan Xiao Wenjie Cui Shang Zhang Guangda Liu
author_facet	Jing Cai Ruolan Xiao Wenjie Cui Shang Zhang Guangda Liu
author_sort	Jing Cai
title	Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review
title_short	Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review
title_full	Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review
title_fullStr	Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review
title_full_unstemmed	Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review
title_sort	application of electroencephalography-based machine learning in emotion recognition: a review
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/33e3a525e21a4865a5d5d8099c0e1427
work_keys_str_mv	AT jingcai applicationofelectroencephalographybasedmachinelearninginemotionrecognitionareview AT ruolanxiao applicationofelectroencephalographybasedmachinelearninginemotionrecognitionareview AT wenjiecui applicationofelectroencephalographybasedmachinelearninginemotionrecognitionareview AT shangzhang applicationofelectroencephalographybasedmachinelearninginemotionrecognitionareview AT guangdaliu applicationofelectroencephalographybasedmachinelearninginemotionrecognitionareview
_version_	1718406614080290816

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Ejemplares similares