EEG-based emotion recognition during watching movies

EEG-based emotion recognition during watching movies

This study aims at finding the relationship between EEG signals and human emotions. EEG signals are used to classify two kinds of emotions, positive and negative. First, we extracted features from original EEG data and used a linear dynamic system approach to smooth these features. An average test a...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Dan Nie, Xiao-Wei Wang, Li-Chen Shi, Bao-Liang Lu
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Accuracy
Electroencephalography
Emotion recognition
Humans
Manifolds
Motion pictures
Trajectory
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 670
container_issue
container_start_page 667
container_title
container_volume
creator Dan Nie
Xiao-Wei Wang
Li-Chen Shi
Bao-Liang Lu
description This study aims at finding the relationship between EEG signals and human emotions. EEG signals are used to classify two kinds of emotions, positive and negative. First, we extracted features from original EEG data and used a linear dynamic system approach to smooth these features. An average test accuracy of 87.53% was obtained by using all of the features together with a support vector machine. Next, we reduced the dimension of features through correlation coefficients. The top 100 and top 50 subject-independent features were achieved, with average test accuracies of 89.22% and 84.94%, respectively. Finally, a manifold model was applied to find the trajectory of emotion changes.
doi_str_mv 10.1109/NER.2011.5910636
format Conference Proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5910636</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5910636</ieee_id><sourcerecordid>5910636</sourcerecordid><originalsourceid>FETCH-LOGICAL-c288t-1a485c84b3d7c3c2164930063678e9d48ca17c143911572ffea119aff56ba1f3</originalsourceid><addsrcrecordid>eNo9kF1LwzAYhePHwG32XvCmf6D1fZM3bXIpo5vCUJDdjzRNZsS20lTFfy_V6dV54IED5zB2hZAjgr55qJ5yDoi51AiFKE7YAokTERLyUzZHTSoTUtIZS3Sp_hzw839HxYwtOIDWMIkLlsT4AgCCA6FWc0ZVtclqE12TurYfQ9-lg7P9oQs_3LwPoTukn2a0zxO0_Udw8ZLNvHmNLjnmku3W1W51l20fN_er221muVJjhoaUtIpq0ZRWWI4FaQHTklI53ZCyBkuLJDSiLLn3ziBq470saoNeLNn1b21wzu3fhtCa4Wt_PEN8A0QPSoY</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>EEG-based emotion recognition during watching movies</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Dan Nie ; Xiao-Wei Wang ; Li-Chen Shi ; Bao-Liang Lu</creator><creatorcontrib>Dan Nie ; Xiao-Wei Wang ; Li-Chen Shi ; Bao-Liang Lu</creatorcontrib><description>This study aims at finding the relationship between EEG signals and human emotions. EEG signals are used to classify two kinds of emotions, positive and negative. First, we extracted features from original EEG data and used a linear dynamic system approach to smooth these features. An average test accuracy of 87.53% was obtained by using all of the features together with a support vector machine. Next, we reduced the dimension of features through correlation coefficients. The top 100 and top 50 subject-independent features were achieved, with average test accuracies of 89.22% and 84.94%, respectively. Finally, a manifold model was applied to find the trajectory of emotion changes.</description><identifier>ISSN: 1948-3546</identifier><identifier>ISBN: 9781424441402</identifier><identifier>ISBN: 1424441404</identifier><identifier>EISSN: 1948-3554</identifier><identifier>EISBN: 1424441412</identifier><identifier>EISBN: 9781424441419</identifier><identifier>DOI: 10.1109/NER.2011.5910636</identifier><identifier>LCCN: 2009901402</identifier><language>eng</language><publisher>IEEE</publisher><subject>Accuracy ; Electroencephalography ; Emotion recognition ; Humans ; Manifolds ; Motion pictures ; Trajectory</subject><ispartof>2011 5th International IEEE/EMBS Conference on Neural Engineering, 2011, p.667-670</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c288t-1a485c84b3d7c3c2164930063678e9d48ca17c143911572ffea119aff56ba1f3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5910636$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5910636$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Dan Nie</creatorcontrib><creatorcontrib>Xiao-Wei Wang</creatorcontrib><creatorcontrib>Li-Chen Shi</creatorcontrib><creatorcontrib>Bao-Liang Lu</creatorcontrib><title>EEG-based emotion recognition during watching movies</title><title>2011 5th International IEEE/EMBS Conference on Neural Engineering</title><addtitle>NER</addtitle><description>This study aims at finding the relationship between EEG signals and human emotions. EEG signals are used to classify two kinds of emotions, positive and negative. First, we extracted features from original EEG data and used a linear dynamic system approach to smooth these features. An average test accuracy of 87.53% was obtained by using all of the features together with a support vector machine. Next, we reduced the dimension of features through correlation coefficients. The top 100 and top 50 subject-independent features were achieved, with average test accuracies of 89.22% and 84.94%, respectively. Finally, a manifold model was applied to find the trajectory of emotion changes.</description><subject>Accuracy</subject><subject>Electroencephalography</subject><subject>Emotion recognition</subject><subject>Humans</subject><subject>Manifolds</subject><subject>Motion pictures</subject><subject>Trajectory</subject><issn>1948-3546</issn><issn>1948-3554</issn><isbn>9781424441402</isbn><isbn>1424441404</isbn><isbn>1424441412</isbn><isbn>9781424441419</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9kF1LwzAYhePHwG32XvCmf6D1fZM3bXIpo5vCUJDdjzRNZsS20lTFfy_V6dV54IED5zB2hZAjgr55qJ5yDoi51AiFKE7YAokTERLyUzZHTSoTUtIZS3Sp_hzw839HxYwtOIDWMIkLlsT4AgCCA6FWc0ZVtclqE12TurYfQ9-lg7P9oQs_3LwPoTukn2a0zxO0_Udw8ZLNvHmNLjnmku3W1W51l20fN_er221muVJjhoaUtIpq0ZRWWI4FaQHTklI53ZCyBkuLJDSiLLn3ziBq470saoNeLNn1b21wzu3fhtCa4Wt_PEN8A0QPSoY</recordid><startdate>201104</startdate><enddate>201104</enddate><creator>Dan Nie</creator><creator>Xiao-Wei Wang</creator><creator>Li-Chen Shi</creator><creator>Bao-Liang Lu</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201104</creationdate><title>EEG-based emotion recognition during watching movies</title><author>Dan Nie ; Xiao-Wei Wang ; Li-Chen Shi ; Bao-Liang Lu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c288t-1a485c84b3d7c3c2164930063678e9d48ca17c143911572ffea119aff56ba1f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Accuracy</topic><topic>Electroencephalography</topic><topic>Emotion recognition</topic><topic>Humans</topic><topic>Manifolds</topic><topic>Motion pictures</topic><topic>Trajectory</topic><toplevel>online_resources</toplevel><creatorcontrib>Dan Nie</creatorcontrib><creatorcontrib>Xiao-Wei Wang</creatorcontrib><creatorcontrib>Li-Chen Shi</creatorcontrib><creatorcontrib>Bao-Liang Lu</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Dan Nie</au><au>Xiao-Wei Wang</au><au>Li-Chen Shi</au><au>Bao-Liang Lu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>EEG-based emotion recognition during watching movies</atitle><btitle>2011 5th International IEEE/EMBS Conference on Neural Engineering</btitle><stitle>NER</stitle><date>2011-04</date><risdate>2011</risdate><spage>667</spage><epage>670</epage><pages>667-670</pages><issn>1948-3546</issn><eissn>1948-3554</eissn><isbn>9781424441402</isbn><isbn>1424441404</isbn><eisbn>1424441412</eisbn><eisbn>9781424441419</eisbn><abstract>This study aims at finding the relationship between EEG signals and human emotions. EEG signals are used to classify two kinds of emotions, positive and negative. First, we extracted features from original EEG data and used a linear dynamic system approach to smooth these features. An average test accuracy of 87.53% was obtained by using all of the features together with a support vector machine. Next, we reduced the dimension of features through correlation coefficients. The top 100 and top 50 subject-independent features were achieved, with average test accuracies of 89.22% and 84.94%, respectively. Finally, a manifold model was applied to find the trajectory of emotion changes.</abstract><pub>IEEE</pub><doi>10.1109/NER.2011.5910636</doi><tpages>4</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 1948-3546
ispartof 2011 5th International IEEE/EMBS Conference on Neural Engineering, 2011, p.667-670
issn 1948-3546
1948-3554
language eng
recordid cdi_ieee_primary_5910636
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Accuracy
Electroencephalography
Emotion recognition
Humans
Manifolds
Motion pictures
Trajectory
title EEG-based emotion recognition during watching movies
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T22%3A53%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=EEG-based%20emotion%20recognition%20during%20watching%20movies&rft.btitle=2011%205th%20International%20IEEE/EMBS%20Conference%20on%20Neural%20Engineering&rft.au=Dan%20Nie&rft.date=2011-04&rft.spage=667&rft.epage=670&rft.pages=667-670&rft.issn=1948-3546&rft.eissn=1948-3554&rft.isbn=9781424441402&rft.isbn_list=1424441404&rft_id=info:doi/10.1109/NER.2011.5910636&rft_dat=%3Cieee_6IE%3E5910636%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1424441412&rft.eisbn_list=9781424441419&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5910636&rfr_iscdi=true