Adolescents Environmental Emotion Perception by Integrating EEG and Eye Movements

Giving a robot the ability to perceive emotion in its environment can improve human-robot interaction (HRI), thereby facilitating more human-like communication. To achieve emotion recognition in different built environments for adolescents, we propose a multi-modal emotion intensity perception metho...

Full description

Bibliographic Details
Main Authors: Yuanyuan Su, Wenchao Li, Ning Bi, Zhao Lv
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-06-01
Series:Frontiers in Neurorobotics
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fnbot.2019.00046/full
id doaj-7ca6aae437db417e8bcc7526d7e1aa5d
record_format Article
spelling doaj-7ca6aae437db417e8bcc7526d7e1aa5d2020-11-24T23:52:10ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182019-06-011310.3389/fnbot.2019.00046445967Adolescents Environmental Emotion Perception by Integrating EEG and Eye MovementsYuanyuan Su0Yuanyuan Su1Wenchao Li2Ning Bi3Zhao Lv4Zhao Lv5Department of Design, Anhui University, Hefei, ChinaCollege of Design, Iowa State University, Ames, IA, United StatesSchool of Computer Science and Technology, Anhui University, Hefei, ChinaSchool of Computer Science, Georgia Institute of Technology, Atlanta, GA, United StatesSchool of Computer Science and Technology, Anhui University, Hefei, ChinaInstitute of Physical Science and Information Technology, Anhui University, Hefei, ChinaGiving a robot the ability to perceive emotion in its environment can improve human-robot interaction (HRI), thereby facilitating more human-like communication. To achieve emotion recognition in different built environments for adolescents, we propose a multi-modal emotion intensity perception method using an integration of electroencephalography (EEG) and eye movement information. Specifically, we first develop a new stimulus video selection method based on computation of normalized arousal and valence scores according to subjective feedback from participants. Then, we establish a valence perception sub-model and an arousal sub-model by collecting and analyzing emotional EEG and eye movement signals, respectively. We employ this dual recognition method to perceive emotional intensities synchronously in two dimensions. In the laboratory environment, the best recognition accuracies of the modality fusion for the arousal and valence dimensions are 72.8 and 69.3%. The experimental results validate the feasibility of the proposed multi-modal emotion recognition method for environment emotion intensity perception. This promising tool not only achieves more accurate emotion perception for HRI systems but also provides an alternative approach to quantitatively assess environmental psychology.https://www.frontiersin.org/article/10.3389/fnbot.2019.00046/fullelectroencephalograph (EEG)eye movementshuman-robot interaction (HRI)adolescentsenvironmental emotion perception
collection DOAJ
language English
format Article
sources DOAJ
author Yuanyuan Su
Yuanyuan Su
Wenchao Li
Ning Bi
Zhao Lv
Zhao Lv
spellingShingle Yuanyuan Su
Yuanyuan Su
Wenchao Li
Ning Bi
Zhao Lv
Zhao Lv
Adolescents Environmental Emotion Perception by Integrating EEG and Eye Movements
Frontiers in Neurorobotics
electroencephalograph (EEG)
eye movements
human-robot interaction (HRI)
adolescents
environmental emotion perception
author_facet Yuanyuan Su
Yuanyuan Su
Wenchao Li
Ning Bi
Zhao Lv
Zhao Lv
author_sort Yuanyuan Su
title Adolescents Environmental Emotion Perception by Integrating EEG and Eye Movements
title_short Adolescents Environmental Emotion Perception by Integrating EEG and Eye Movements
title_full Adolescents Environmental Emotion Perception by Integrating EEG and Eye Movements
title_fullStr Adolescents Environmental Emotion Perception by Integrating EEG and Eye Movements
title_full_unstemmed Adolescents Environmental Emotion Perception by Integrating EEG and Eye Movements
title_sort adolescents environmental emotion perception by integrating eeg and eye movements
publisher Frontiers Media S.A.
series Frontiers in Neurorobotics
issn 1662-5218
publishDate 2019-06-01
description Giving a robot the ability to perceive emotion in its environment can improve human-robot interaction (HRI), thereby facilitating more human-like communication. To achieve emotion recognition in different built environments for adolescents, we propose a multi-modal emotion intensity perception method using an integration of electroencephalography (EEG) and eye movement information. Specifically, we first develop a new stimulus video selection method based on computation of normalized arousal and valence scores according to subjective feedback from participants. Then, we establish a valence perception sub-model and an arousal sub-model by collecting and analyzing emotional EEG and eye movement signals, respectively. We employ this dual recognition method to perceive emotional intensities synchronously in two dimensions. In the laboratory environment, the best recognition accuracies of the modality fusion for the arousal and valence dimensions are 72.8 and 69.3%. The experimental results validate the feasibility of the proposed multi-modal emotion recognition method for environment emotion intensity perception. This promising tool not only achieves more accurate emotion perception for HRI systems but also provides an alternative approach to quantitatively assess environmental psychology.
topic electroencephalograph (EEG)
eye movements
human-robot interaction (HRI)
adolescents
environmental emotion perception
url https://www.frontiersin.org/article/10.3389/fnbot.2019.00046/full
work_keys_str_mv AT yuanyuansu adolescentsenvironmentalemotionperceptionbyintegratingeegandeyemovements
AT yuanyuansu adolescentsenvironmentalemotionperceptionbyintegratingeegandeyemovements
AT wenchaoli adolescentsenvironmentalemotionperceptionbyintegratingeegandeyemovements
AT ningbi adolescentsenvironmentalemotionperceptionbyintegratingeegandeyemovements
AT zhaolv adolescentsenvironmentalemotionperceptionbyintegratingeegandeyemovements
AT zhaolv adolescentsenvironmentalemotionperceptionbyintegratingeegandeyemovements
_version_ 1725474524307128320