ECSMP: A dataset on emotion, cognition, sleep, and multi-model physiological signals

This paper described the collection of multi-modal physiological signals, which include electroencephalography, electrocardiograph (ECG), photoplethysmography, electrodermal activity, temperature, and accelerometer data, recorded from 89 healthy college students during resting state, the emotion ind...

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Autores principales:	Zhilin Gao, Xingran Cui, Wang Wan, Wenming Zheng, Zhongze Gu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Video-induced emotion Cognitive function assessment Sleep quality EEG Multi-model physiological signals Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/565abc20f6ec4cfe9775f30446865917
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spelling	oai:doaj.org-article:565abc20f6ec4cfe9775f304468659172021-12-04T04:34:40ZECSMP: A dataset on emotion, cognition, sleep, and multi-model physiological signals2352-340910.1016/j.dib.2021.107660https://doaj.org/article/565abc20f6ec4cfe9775f304468659172021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352340921009355https://doaj.org/toc/2352-3409This paper described the collection of multi-modal physiological signals, which include electroencephalography, electrocardiograph (ECG), photoplethysmography, electrodermal activity, temperature, and accelerometer data, recorded from 89 healthy college students during resting state, the emotion induction and recovery, and a set of cognitive function assessment tasks. Emotion, sleep, cognition, depression, mood, and other factors were evaluated through different methods, and were included in this dataset. Six emotions (neutral, fear, sad, happy, anger, and disgust) were induced by movie clips. The cognitive functions such as sustained attention, response inhibition, working memory, and strategy use, were quantitatively measured by Cambridge neuropsychological test automatic battery. The sleep ECG was collected the night before the emotion-induction experiment, and the sleep quality was analysed based on the sleep ECG. After the experiment, the participants were required to fill in questionnaires to evaluate the emotion regulation strategies, depression score, recent mood, and sleep quality index. The database can not only be directly used for the research of emotion recognition on multi-modal physiological signals, but also can further explore the interactions between emotion, cognition, and sleep.Zhilin GaoXingran CuiWang WanWenming ZhengZhongze GuElsevierarticleVideo-induced emotionCognitive function assessmentSleep qualityEEGMulti-model physiological signalsComputer applications to medicine. Medical informaticsR858-859.7Science (General)Q1-390ENData in Brief, Vol 39, Iss , Pp 107660- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Video-induced emotion Cognitive function assessment Sleep quality EEG Multi-model physiological signals Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
spellingShingle	Video-induced emotion Cognitive function assessment Sleep quality EEG Multi-model physiological signals Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390 Zhilin Gao Xingran Cui Wang Wan Wenming Zheng Zhongze Gu ECSMP: A dataset on emotion, cognition, sleep, and multi-model physiological signals
description	This paper described the collection of multi-modal physiological signals, which include electroencephalography, electrocardiograph (ECG), photoplethysmography, electrodermal activity, temperature, and accelerometer data, recorded from 89 healthy college students during resting state, the emotion induction and recovery, and a set of cognitive function assessment tasks. Emotion, sleep, cognition, depression, mood, and other factors were evaluated through different methods, and were included in this dataset. Six emotions (neutral, fear, sad, happy, anger, and disgust) were induced by movie clips. The cognitive functions such as sustained attention, response inhibition, working memory, and strategy use, were quantitatively measured by Cambridge neuropsychological test automatic battery. The sleep ECG was collected the night before the emotion-induction experiment, and the sleep quality was analysed based on the sleep ECG. After the experiment, the participants were required to fill in questionnaires to evaluate the emotion regulation strategies, depression score, recent mood, and sleep quality index. The database can not only be directly used for the research of emotion recognition on multi-modal physiological signals, but also can further explore the interactions between emotion, cognition, and sleep.
format	article
author	Zhilin Gao Xingran Cui Wang Wan Wenming Zheng Zhongze Gu
author_facet	Zhilin Gao Xingran Cui Wang Wan Wenming Zheng Zhongze Gu
author_sort	Zhilin Gao
title	ECSMP: A dataset on emotion, cognition, sleep, and multi-model physiological signals
title_short	ECSMP: A dataset on emotion, cognition, sleep, and multi-model physiological signals
title_full	ECSMP: A dataset on emotion, cognition, sleep, and multi-model physiological signals
title_fullStr	ECSMP: A dataset on emotion, cognition, sleep, and multi-model physiological signals
title_full_unstemmed	ECSMP: A dataset on emotion, cognition, sleep, and multi-model physiological signals
title_sort	ecsmp: a dataset on emotion, cognition, sleep, and multi-model physiological signals
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/565abc20f6ec4cfe9775f30446865917
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ECSMP: A dataset on emotion, cognition, sleep, and multi-model physiological signals

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