Quantification and Discriminant Evaluation of Unpleasant Emotions by Frequency Analysis Using EEG and ECG
The purpose of this study is to verify whether machine learning using electroencephalogram (EEG) and electrocardiogram (ECG) as inputs improves accuracy. The participants, 16 healthy adults, were given two stimulations: resting and unpleasant stimuli. Their EEG was measured 180 s immediately after t...
Gespeichert in:
| Veröffentlicht in: | Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 2022/08/15, Vol.34(3), pp.654-662 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng ; jpn |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 662 |
|---|---|
| container_issue | 3 |
| container_start_page | 654 |
| container_title | Journal of Japan Society for Fuzzy Theory and Intelligent Informatics |
| container_volume | 34 |
| creator | YAMAMOTO, Yusuke TANAKA, Saya HARACHI, Kento MURAMATSU, Ayumi TAKEMURA, Noriko NAGAHARA, Hajime MIZUNO-MATSUMOTO, Yuko SHIMOJO, Shinji |
| description | The purpose of this study is to verify whether machine learning using electroencephalogram (EEG) and electrocardiogram (ECG) as inputs improves accuracy. The participants, 16 healthy adults, were given two stimulations: resting and unpleasant stimuli. Their EEG was measured 180 s immediately after the stimuli. The beta band of EEG and LF, HF, and LF/HF of ECG were calculated. The accuracy of the neural network was then compared using an EEG-only, ECG-only, and combined EEG and ECG dataset. The accuracy of the neural network using the combined EEG and ECG dataset was 79.51%, which was higher than that of the other datasets. The results suggest that emotional responses to resting and unpleasant stimuli may differ in brain and autonomic nervous system activity, and that combining both EEG and ECG indices may allow for more accurate discrimination. |
| doi_str_mv | 10.3156/jsoft.34.3_654 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2709084634</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2709084634</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1234-ea304d3b385075f36dd5fe54ce26c7173fb48c3f6c00ff04edec3bea7df23c213</originalsourceid><addsrcrecordid>eNpNUN1LwzAQD6LgnL76HPC5M-mlH3uSMbspDERwzyFNk5nSpTNphf73Zq2IL3fH_T64-yF0T8kCaJI-1r7V3QLYAniasAs0o3lOoywmcBlmYFmULfP0Gt14XxOSLklCZ8i898J2RhspOtNaLGyFn42XzhyNDQguvkXTT1ir8d6eGiX8CBzb89bjcsAbp756ZeWAV1Y0gzce772xB1wU29GyWG9v0ZUWjVd3v32O9pviY_0S7d62r-vVLpI0BhYpAYRVUEKekCzRkFZVolXCpIpTmdEMdMlyCTqVhGhNmKqUhFKJrNIxyJjCHD1MvifXhqN8x-u2d-Esz-OMLEnOUmCBtZhY0rXeO6X5Kbws3MAp4ec4-RgnB8bHOIPgaRLUvhMH9UcXrjOyUf_poQTFHyI_hePKwg-fM4IL</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2709084634</pqid></control><display><type>article</type><title>Quantification and Discriminant Evaluation of Unpleasant Emotions by Frequency Analysis Using EEG and ECG</title><source>J-STAGE (Japan Science & Technology Information Aggregator, Electronic) Freely Available Titles - Japanese</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>YAMAMOTO, Yusuke ; TANAKA, Saya ; HARACHI, Kento ; MURAMATSU, Ayumi ; TAKEMURA, Noriko ; NAGAHARA, Hajime ; MIZUNO-MATSUMOTO, Yuko ; SHIMOJO, Shinji</creator><creatorcontrib>YAMAMOTO, Yusuke ; TANAKA, Saya ; HARACHI, Kento ; MURAMATSU, Ayumi ; TAKEMURA, Noriko ; NAGAHARA, Hajime ; MIZUNO-MATSUMOTO, Yuko ; SHIMOJO, Shinji</creatorcontrib><description>The purpose of this study is to verify whether machine learning using electroencephalogram (EEG) and electrocardiogram (ECG) as inputs improves accuracy. The participants, 16 healthy adults, were given two stimulations: resting and unpleasant stimuli. Their EEG was measured 180 s immediately after the stimuli. The beta band of EEG and LF, HF, and LF/HF of ECG were calculated. The accuracy of the neural network was then compared using an EEG-only, ECG-only, and combined EEG and ECG dataset. The accuracy of the neural network using the combined EEG and ECG dataset was 79.51%, which was higher than that of the other datasets. The results suggest that emotional responses to resting and unpleasant stimuli may differ in brain and autonomic nervous system activity, and that combining both EEG and ECG indices may allow for more accurate discrimination.</description><identifier>ISSN: 1347-7986</identifier><identifier>EISSN: 1881-7203</identifier><identifier>DOI: 10.3156/jsoft.34.3_654</identifier><language>eng ; jpn</language><publisher>Iizuka: Japan Society for Fuzzy Theory and Intelligent Informatics</publisher><subject>Autonomic nervous system ; Datasets ; ECG ; EEG ; Electrocardiography ; Electroencephalography ; Emotional factors ; Frequency analysis ; Machine learning ; neural network ; Neural networks ; Stimuli</subject><ispartof>Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 2022/08/15, Vol.34(3), pp.654-662</ispartof><rights>2022 Japan Society for Fuzzy Theory and Intelligent Informatics</rights><rights>Copyright Japan Science and Technology Agency 2022</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c1234-ea304d3b385075f36dd5fe54ce26c7173fb48c3f6c00ff04edec3bea7df23c213</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,1877,27901,27902</link.rule.ids></links><search><creatorcontrib>YAMAMOTO, Yusuke</creatorcontrib><creatorcontrib>TANAKA, Saya</creatorcontrib><creatorcontrib>HARACHI, Kento</creatorcontrib><creatorcontrib>MURAMATSU, Ayumi</creatorcontrib><creatorcontrib>TAKEMURA, Noriko</creatorcontrib><creatorcontrib>NAGAHARA, Hajime</creatorcontrib><creatorcontrib>MIZUNO-MATSUMOTO, Yuko</creatorcontrib><creatorcontrib>SHIMOJO, Shinji</creatorcontrib><title>Quantification and Discriminant Evaluation of Unpleasant Emotions by Frequency Analysis Using EEG and ECG</title><title>Journal of Japan Society for Fuzzy Theory and Intelligent Informatics</title><addtitle>J. SOFT</addtitle><description>The purpose of this study is to verify whether machine learning using electroencephalogram (EEG) and electrocardiogram (ECG) as inputs improves accuracy. The participants, 16 healthy adults, were given two stimulations: resting and unpleasant stimuli. Their EEG was measured 180 s immediately after the stimuli. The beta band of EEG and LF, HF, and LF/HF of ECG were calculated. The accuracy of the neural network was then compared using an EEG-only, ECG-only, and combined EEG and ECG dataset. The accuracy of the neural network using the combined EEG and ECG dataset was 79.51%, which was higher than that of the other datasets. The results suggest that emotional responses to resting and unpleasant stimuli may differ in brain and autonomic nervous system activity, and that combining both EEG and ECG indices may allow for more accurate discrimination.</description><subject>Autonomic nervous system</subject><subject>Datasets</subject><subject>ECG</subject><subject>EEG</subject><subject>Electrocardiography</subject><subject>Electroencephalography</subject><subject>Emotional factors</subject><subject>Frequency analysis</subject><subject>Machine learning</subject><subject>neural network</subject><subject>Neural networks</subject><subject>Stimuli</subject><issn>1347-7986</issn><issn>1881-7203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNpNUN1LwzAQD6LgnL76HPC5M-mlH3uSMbspDERwzyFNk5nSpTNphf73Zq2IL3fH_T64-yF0T8kCaJI-1r7V3QLYAniasAs0o3lOoywmcBlmYFmULfP0Gt14XxOSLklCZ8i898J2RhspOtNaLGyFn42XzhyNDQguvkXTT1ir8d6eGiX8CBzb89bjcsAbp756ZeWAV1Y0gzce772xB1wU29GyWG9v0ZUWjVd3v32O9pviY_0S7d62r-vVLpI0BhYpAYRVUEKekCzRkFZVolXCpIpTmdEMdMlyCTqVhGhNmKqUhFKJrNIxyJjCHD1MvifXhqN8x-u2d-Esz-OMLEnOUmCBtZhY0rXeO6X5Kbws3MAp4ec4-RgnB8bHOIPgaRLUvhMH9UcXrjOyUf_poQTFHyI_hePKwg-fM4IL</recordid><startdate>20220815</startdate><enddate>20220815</enddate><creator>YAMAMOTO, Yusuke</creator><creator>TANAKA, Saya</creator><creator>HARACHI, Kento</creator><creator>MURAMATSU, Ayumi</creator><creator>TAKEMURA, Noriko</creator><creator>NAGAHARA, Hajime</creator><creator>MIZUNO-MATSUMOTO, Yuko</creator><creator>SHIMOJO, Shinji</creator><general>Japan Society for Fuzzy Theory and Intelligent Informatics</general><general>Japan Science and Technology Agency</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20220815</creationdate><title>Quantification and Discriminant Evaluation of Unpleasant Emotions by Frequency Analysis Using EEG and ECG</title><author>YAMAMOTO, Yusuke ; TANAKA, Saya ; HARACHI, Kento ; MURAMATSU, Ayumi ; TAKEMURA, Noriko ; NAGAHARA, Hajime ; MIZUNO-MATSUMOTO, Yuko ; SHIMOJO, Shinji</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1234-ea304d3b385075f36dd5fe54ce26c7173fb48c3f6c00ff04edec3bea7df23c213</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng ; jpn</language><creationdate>2022</creationdate><topic>Autonomic nervous system</topic><topic>Datasets</topic><topic>ECG</topic><topic>EEG</topic><topic>Electrocardiography</topic><topic>Electroencephalography</topic><topic>Emotional factors</topic><topic>Frequency analysis</topic><topic>Machine learning</topic><topic>neural network</topic><topic>Neural networks</topic><topic>Stimuli</topic><toplevel>online_resources</toplevel><creatorcontrib>YAMAMOTO, Yusuke</creatorcontrib><creatorcontrib>TANAKA, Saya</creatorcontrib><creatorcontrib>HARACHI, Kento</creatorcontrib><creatorcontrib>MURAMATSU, Ayumi</creatorcontrib><creatorcontrib>TAKEMURA, Noriko</creatorcontrib><creatorcontrib>NAGAHARA, Hajime</creatorcontrib><creatorcontrib>MIZUNO-MATSUMOTO, Yuko</creatorcontrib><creatorcontrib>SHIMOJO, Shinji</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology 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><jtitle>Journal of Japan Society for Fuzzy Theory and Intelligent Informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>YAMAMOTO, Yusuke</au><au>TANAKA, Saya</au><au>HARACHI, Kento</au><au>MURAMATSU, Ayumi</au><au>TAKEMURA, Noriko</au><au>NAGAHARA, Hajime</au><au>MIZUNO-MATSUMOTO, Yuko</au><au>SHIMOJO, Shinji</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantification and Discriminant Evaluation of Unpleasant Emotions by Frequency Analysis Using EEG and ECG</atitle><jtitle>Journal of Japan Society for Fuzzy Theory and Intelligent Informatics</jtitle><addtitle>J. SOFT</addtitle><date>2022-08-15</date><risdate>2022</risdate><volume>34</volume><issue>3</issue><spage>654</spage><epage>662</epage><pages>654-662</pages><issn>1347-7986</issn><eissn>1881-7203</eissn><abstract>The purpose of this study is to verify whether machine learning using electroencephalogram (EEG) and electrocardiogram (ECG) as inputs improves accuracy. The participants, 16 healthy adults, were given two stimulations: resting and unpleasant stimuli. Their EEG was measured 180 s immediately after the stimuli. The beta band of EEG and LF, HF, and LF/HF of ECG were calculated. The accuracy of the neural network was then compared using an EEG-only, ECG-only, and combined EEG and ECG dataset. The accuracy of the neural network using the combined EEG and ECG dataset was 79.51%, which was higher than that of the other datasets. The results suggest that emotional responses to resting and unpleasant stimuli may differ in brain and autonomic nervous system activity, and that combining both EEG and ECG indices may allow for more accurate discrimination.</abstract><cop>Iizuka</cop><pub>Japan Society for Fuzzy Theory and Intelligent Informatics</pub><doi>10.3156/jsoft.34.3_654</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1347-7986 |
| ispartof | Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 2022/08/15, Vol.34(3), pp.654-662 |
| issn | 1347-7986 1881-7203 |
| language | eng ; jpn |
| recordid | cdi_proquest_journals_2709084634 |
| source | J-STAGE (Japan Science & Technology Information Aggregator, Electronic) Freely Available Titles - Japanese; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Autonomic nervous system Datasets ECG EEG Electrocardiography Electroencephalography Emotional factors Frequency analysis Machine learning neural network Neural networks Stimuli |
| title | Quantification and Discriminant Evaluation of Unpleasant Emotions by Frequency Analysis Using EEG and ECG |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-13T04%3A21%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Quantification%20and%20Discriminant%20Evaluation%20of%20Unpleasant%20Emotions%20by%20Frequency%20Analysis%20Using%20EEG%20and%20ECG&rft.jtitle=Journal%20of%20Japan%20Society%20for%20Fuzzy%20Theory%20and%20Intelligent%20Informatics&rft.au=YAMAMOTO,%20Yusuke&rft.date=2022-08-15&rft.volume=34&rft.issue=3&rft.spage=654&rft.epage=662&rft.pages=654-662&rft.issn=1347-7986&rft.eissn=1881-7203&rft_id=info:doi/10.3156/jsoft.34.3_654&rft_dat=%3Cproquest_cross%3E2709084634%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2709084634&rft_id=info:pmid/&rfr_iscdi=true |