Loading…
PhyMER: Physiological Dataset for Multimodal Emotion Recognition With Personality as a Context
Physiological signals are widely used in the recognition of affective status. Recording of such physiological signals involves elicitation of emotions through different stimuli including video-based stimulus. Considering that the same stimulus videos often induce different emotions in different indi...
Saved in:
Published in: | IEEE access 2023, Vol.11, p.107638-107656 |
---|---|
Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c409t-b0786a2bb14129399373ff3c4d1913f25d21117f3985c5d6ca685d21bf8072d93 |
---|---|
cites | cdi_FETCH-LOGICAL-c409t-b0786a2bb14129399373ff3c4d1913f25d21117f3985c5d6ca685d21bf8072d93 |
container_end_page | 107656 |
container_issue | |
container_start_page | 107638 |
container_title | IEEE access |
container_volume | 11 |
creator | Pant, Sudarshan Yang, Hyung-Jeong Lim, Eunchae Kim, Soo-Hyung Yoo, Seok-Bong |
description | Physiological signals are widely used in the recognition of affective status. Recording of such physiological signals involves elicitation of emotions through different stimuli including video-based stimulus. Considering that the same stimulus videos often induce different emotions in different individuals, emotion recognition in such a scenario requires consideration of the individual differences in the consumption of the stimulus content. With this as our goal, we present a Physiological dataset for Multimodal Emotion Recognition (PhyMER) for studying emotion through physiological response with personality as a context. The PhyMER dataset consists of electroencephalogram (EEG), electrodermal activity (EDA), blood volume pulse (BVP), and skin temperature along with the personality traits of 30 participants. We collected the video-based stimulus dataset for emotion elicitation and developed a web-based annotation tool for labeling felt emotions. We compared the stimulus labels and the self-annotation of felt emotions labeled during physiological data recording. Correlation among personalities was analyzed to study the impact of personality on the intensity of emotions in arousal and valence dimensions. Finally, we proposed a baseline model for the classification of emotions using physiological signals. The dataset is publicly available to the academic community for analysis of affective states and the development of emotion recognition models. |
doi_str_mv | 10.1109/ACCESS.2023.3320053 |
format | article |
fullrecord | <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_ieee_primary_10265252</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10265252</ieee_id><doaj_id>oai_doaj_org_article_6f09dd4d5ecc472da8f228719d7ef003</doaj_id><sourcerecordid>2873584971</sourcerecordid><originalsourceid>FETCH-LOGICAL-c409t-b0786a2bb14129399373ff3c4d1913f25d21117f3985c5d6ca685d21bf8072d93</originalsourceid><addsrcrecordid>eNpNUdtKAzEUXETBon6BPgR8bk1yNrsb32StF7AoXvDNkObSpmwbTVKwf2_qivS8zGGYmcNhiuKU4BEhmF9cte345WVEMYURAMWYwV4xoKTiQ2BQ7e_sh8VJjAucp8kUqwfFx9N8Mxk_X6KM0fnOz5ySHbqWSUaTkPUBTdZdckuvMz1e-uT8Cj0b5Wcr97u_uzRHTyZEv5KdSxskI5Ko9atkvtNxcWBlF83JHx4Vbzfj1_Zu-PB4e99ePQxViXkaTnHdVJJOp6QklAPnUIO1oEpNOAFLmaaEkNoCb5hiulKyarbc1Da4pprDUXHf52ovF-IzuKUMG-GlE7-EDzMhQ3KqM6KymGtdamaUKrNZNpbSpiZc18ZiDDnrvM_6DP5rbWISC78O-bkosg5YU_KaZBX0KhV8jMHY_6sEi20vou9FbHsRf71k11nvcsaYHQetGGUUfgCAiYhJ</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2873584971</pqid></control><display><type>article</type><title>PhyMER: Physiological Dataset for Multimodal Emotion Recognition With Personality as a Context</title><source>IEEE Open Access Journals</source><creator>Pant, Sudarshan ; Yang, Hyung-Jeong ; Lim, Eunchae ; Kim, Soo-Hyung ; Yoo, Seok-Bong</creator><creatorcontrib>Pant, Sudarshan ; Yang, Hyung-Jeong ; Lim, Eunchae ; Kim, Soo-Hyung ; Yoo, Seok-Bong</creatorcontrib><description>Physiological signals are widely used in the recognition of affective status. Recording of such physiological signals involves elicitation of emotions through different stimuli including video-based stimulus. Considering that the same stimulus videos often induce different emotions in different individuals, emotion recognition in such a scenario requires consideration of the individual differences in the consumption of the stimulus content. With this as our goal, we present a Physiological dataset for Multimodal Emotion Recognition (PhyMER) for studying emotion through physiological response with personality as a context. The PhyMER dataset consists of electroencephalogram (EEG), electrodermal activity (EDA), blood volume pulse (BVP), and skin temperature along with the personality traits of 30 participants. We collected the video-based stimulus dataset for emotion elicitation and developed a web-based annotation tool for labeling felt emotions. We compared the stimulus labels and the self-annotation of felt emotions labeled during physiological data recording. Correlation among personalities was analyzed to study the impact of personality on the intensity of emotions in arousal and valence dimensions. Finally, we proposed a baseline model for the classification of emotions using physiological signals. The dataset is publicly available to the academic community for analysis of affective states and the development of emotion recognition models.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2023.3320053</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Annotations ; Arousal ; Blood volume ; Context ; Data recording ; Datasets ; EEG ; Electrocardiography ; Electroencephalography ; emotion classification ; Emotion recognition ; Emotions ; Labels ; Motion pictures ; Personality ; personality traits ; Physiological signals ; Physiology ; Skin temperature ; Videos</subject><ispartof>IEEE access, 2023, Vol.11, p.107638-107656</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c409t-b0786a2bb14129399373ff3c4d1913f25d21117f3985c5d6ca685d21bf8072d93</citedby><cites>FETCH-LOGICAL-c409t-b0786a2bb14129399373ff3c4d1913f25d21117f3985c5d6ca685d21bf8072d93</cites><orcidid>0000-0003-3575-5035 ; 0000-0003-3339-4192 ; 0000-0003-3024-5060 ; 0000-0002-2385-9673 ; 0000-0002-6528-701X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10265252$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Pant, Sudarshan</creatorcontrib><creatorcontrib>Yang, Hyung-Jeong</creatorcontrib><creatorcontrib>Lim, Eunchae</creatorcontrib><creatorcontrib>Kim, Soo-Hyung</creatorcontrib><creatorcontrib>Yoo, Seok-Bong</creatorcontrib><title>PhyMER: Physiological Dataset for Multimodal Emotion Recognition With Personality as a Context</title><title>IEEE access</title><addtitle>Access</addtitle><description>Physiological signals are widely used in the recognition of affective status. Recording of such physiological signals involves elicitation of emotions through different stimuli including video-based stimulus. Considering that the same stimulus videos often induce different emotions in different individuals, emotion recognition in such a scenario requires consideration of the individual differences in the consumption of the stimulus content. With this as our goal, we present a Physiological dataset for Multimodal Emotion Recognition (PhyMER) for studying emotion through physiological response with personality as a context. The PhyMER dataset consists of electroencephalogram (EEG), electrodermal activity (EDA), blood volume pulse (BVP), and skin temperature along with the personality traits of 30 participants. We collected the video-based stimulus dataset for emotion elicitation and developed a web-based annotation tool for labeling felt emotions. We compared the stimulus labels and the self-annotation of felt emotions labeled during physiological data recording. Correlation among personalities was analyzed to study the impact of personality on the intensity of emotions in arousal and valence dimensions. Finally, we proposed a baseline model for the classification of emotions using physiological signals. The dataset is publicly available to the academic community for analysis of affective states and the development of emotion recognition models.</description><subject>Annotations</subject><subject>Arousal</subject><subject>Blood volume</subject><subject>Context</subject><subject>Data recording</subject><subject>Datasets</subject><subject>EEG</subject><subject>Electrocardiography</subject><subject>Electroencephalography</subject><subject>emotion classification</subject><subject>Emotion recognition</subject><subject>Emotions</subject><subject>Labels</subject><subject>Motion pictures</subject><subject>Personality</subject><subject>personality traits</subject><subject>Physiological signals</subject><subject>Physiology</subject><subject>Skin temperature</subject><subject>Videos</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>DOA</sourceid><recordid>eNpNUdtKAzEUXETBon6BPgR8bk1yNrsb32StF7AoXvDNkObSpmwbTVKwf2_qivS8zGGYmcNhiuKU4BEhmF9cte345WVEMYURAMWYwV4xoKTiQ2BQ7e_sh8VJjAucp8kUqwfFx9N8Mxk_X6KM0fnOz5ySHbqWSUaTkPUBTdZdckuvMz1e-uT8Cj0b5Wcr97u_uzRHTyZEv5KdSxskI5Ko9atkvtNxcWBlF83JHx4Vbzfj1_Zu-PB4e99ePQxViXkaTnHdVJJOp6QklAPnUIO1oEpNOAFLmaaEkNoCb5hiulKyarbc1Da4pprDUXHf52ovF-IzuKUMG-GlE7-EDzMhQ3KqM6KymGtdamaUKrNZNpbSpiZc18ZiDDnrvM_6DP5rbWISC78O-bkosg5YU_KaZBX0KhV8jMHY_6sEi20vou9FbHsRf71k11nvcsaYHQetGGUUfgCAiYhJ</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Pant, Sudarshan</creator><creator>Yang, Hyung-Jeong</creator><creator>Lim, Eunchae</creator><creator>Kim, Soo-Hyung</creator><creator>Yoo, Seok-Bong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-3575-5035</orcidid><orcidid>https://orcid.org/0000-0003-3339-4192</orcidid><orcidid>https://orcid.org/0000-0003-3024-5060</orcidid><orcidid>https://orcid.org/0000-0002-2385-9673</orcidid><orcidid>https://orcid.org/0000-0002-6528-701X</orcidid></search><sort><creationdate>2023</creationdate><title>PhyMER: Physiological Dataset for Multimodal Emotion Recognition With Personality as a Context</title><author>Pant, Sudarshan ; Yang, Hyung-Jeong ; Lim, Eunchae ; Kim, Soo-Hyung ; Yoo, Seok-Bong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c409t-b0786a2bb14129399373ff3c4d1913f25d21117f3985c5d6ca685d21bf8072d93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Annotations</topic><topic>Arousal</topic><topic>Blood volume</topic><topic>Context</topic><topic>Data recording</topic><topic>Datasets</topic><topic>EEG</topic><topic>Electrocardiography</topic><topic>Electroencephalography</topic><topic>emotion classification</topic><topic>Emotion recognition</topic><topic>Emotions</topic><topic>Labels</topic><topic>Motion pictures</topic><topic>Personality</topic><topic>personality traits</topic><topic>Physiological signals</topic><topic>Physiology</topic><topic>Skin temperature</topic><topic>Videos</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pant, Sudarshan</creatorcontrib><creatorcontrib>Yang, Hyung-Jeong</creatorcontrib><creatorcontrib>Lim, Eunchae</creatorcontrib><creatorcontrib>Kim, Soo-Hyung</creatorcontrib><creatorcontrib>Yoo, Seok-Bong</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore (IEEE/IET Electronic Library - IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pant, Sudarshan</au><au>Yang, Hyung-Jeong</au><au>Lim, Eunchae</au><au>Kim, Soo-Hyung</au><au>Yoo, Seok-Bong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>PhyMER: Physiological Dataset for Multimodal Emotion Recognition With Personality as a Context</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2023</date><risdate>2023</risdate><volume>11</volume><spage>107638</spage><epage>107656</epage><pages>107638-107656</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>Physiological signals are widely used in the recognition of affective status. Recording of such physiological signals involves elicitation of emotions through different stimuli including video-based stimulus. Considering that the same stimulus videos often induce different emotions in different individuals, emotion recognition in such a scenario requires consideration of the individual differences in the consumption of the stimulus content. With this as our goal, we present a Physiological dataset for Multimodal Emotion Recognition (PhyMER) for studying emotion through physiological response with personality as a context. The PhyMER dataset consists of electroencephalogram (EEG), electrodermal activity (EDA), blood volume pulse (BVP), and skin temperature along with the personality traits of 30 participants. We collected the video-based stimulus dataset for emotion elicitation and developed a web-based annotation tool for labeling felt emotions. We compared the stimulus labels and the self-annotation of felt emotions labeled during physiological data recording. Correlation among personalities was analyzed to study the impact of personality on the intensity of emotions in arousal and valence dimensions. Finally, we proposed a baseline model for the classification of emotions using physiological signals. The dataset is publicly available to the academic community for analysis of affective states and the development of emotion recognition models.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2023.3320053</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0003-3575-5035</orcidid><orcidid>https://orcid.org/0000-0003-3339-4192</orcidid><orcidid>https://orcid.org/0000-0003-3024-5060</orcidid><orcidid>https://orcid.org/0000-0002-2385-9673</orcidid><orcidid>https://orcid.org/0000-0002-6528-701X</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2169-3536 |
ispartof | IEEE access, 2023, Vol.11, p.107638-107656 |
issn | 2169-3536 2169-3536 |
language | eng |
recordid | cdi_ieee_primary_10265252 |
source | IEEE Open Access Journals |
subjects | Annotations Arousal Blood volume Context Data recording Datasets EEG Electrocardiography Electroencephalography emotion classification Emotion recognition Emotions Labels Motion pictures Personality personality traits Physiological signals Physiology Skin temperature Videos |
title | PhyMER: Physiological Dataset for Multimodal Emotion Recognition With Personality as a Context |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T15%3A36%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=PhyMER:%20Physiological%20Dataset%20for%20Multimodal%20Emotion%20Recognition%20With%20Personality%20as%20a%20Context&rft.jtitle=IEEE%20access&rft.au=Pant,%20Sudarshan&rft.date=2023&rft.volume=11&rft.spage=107638&rft.epage=107656&rft.pages=107638-107656&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2023.3320053&rft_dat=%3Cproquest_ieee_%3E2873584971%3C/proquest_ieee_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c409t-b0786a2bb14129399373ff3c4d1913f25d21117f3985c5d6ca685d21bf8072d93%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2873584971&rft_id=info:pmid/&rft_ieee_id=10265252&rfr_iscdi=true |