{"created":"2023-07-27T07:53:31.000875+00:00","id":29318,"links":{},"metadata":{"_buckets":{"deposit":"ac025fc3-4dd7-4de1-9a51-39b578a34b2a"},"_deposit":{"created_by":21,"id":"29318","owners":[21],"pid":{"revision_id":0,"type":"depid","value":"29318"},"status":"published"},"_oai":{"id":"oai:doshisha.repo.nii.ac.jp:00029318","sets":["4251:8138:8139:8140:9201","8:3372:3847:9200"]},"author_link":["30801","30802","18373","26005"],"item_1693811493084":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_1694490770713":{"attribute_name":"権利者情報","attribute_value_mlt":[{"nameIdentifiers":[{"nameIdentifier":"DA18202107","nameIdentifierScheme":"AID"}],"rightHolderNames":[{"rightHolderLanguage":"ja","rightHolderName":"同志社大学ハリス理化学研究所"},{"rightHolderLanguage":"en","rightHolderName":"Harris Science Research Institute of Doshisha University"}]}]},"item_1_biblio_info_14":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2022-10-31","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicPageEnd":"153","bibliographicPageStart":"145","bibliographicVolumeNumber":"63","bibliographic_titles":[{"bibliographic_title":"同志社大学ハリス理化学研究報告","bibliographic_titleLang":"ja"},{"bibliographic_title":"The Harris science review of Doshisha University","bibliographic_titleLang":"en"}]}]},"item_1_description_12":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"脳波を用いた感情認識は，様々な機械的手法において注目されている．しかし，脳波は非線形であるため感情認識は困難である．そこで本研究では，Convolutional Neural Network(CNN)を用いた感情推定のための回帰モデルを提案する．脳波のオープンデータであるDeapを用いて，提案手法が正則化を用いることで性能が向上するか検証を行う．また，検証の結果その性能は個人によって異なることが確認された．提案手法は一般的なモデルであるが，個人差を取り入れることで性能が向上する．","subitem_description_language":"ja","subitem_description_type":"Abstract"},{"subitem_description":"Emotion recognition using electroencephalogram (EEG) has been paid much attention based on various machine learning techniques. Recognizing emotion is a difficult task due to the non-linear nature of the EEG signal. In this study, we propose a regression model for emotion estimation using Convolutional Neural Networks (CNN). Using EEG open data set, DEAP, we verify that the proposed method slightly improved the performance by incorporating regularization terms. We also observed that the performance is different in each individual. The proposed method is a general model but would enhance the performance by incorporating individual differences.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_1_description_25":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_1_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.14988/00029317","subitem_identifier_reg_type":"JaLC"}]},"item_1_publisher_15":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"同志社大学ハリス理化学研究所","subitem_publisher_language":"ja"}]},"item_1_publisher_16":{"attribute_name":"出版者（英）","attribute_value_mlt":[{"subitem_publisher":"Harris Science Research Institute of Doshisha 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賢太郎 / 同志社大学大学院理工学研究科"},{"subitem_text_language":"ja","subitem_text_value":"小林, 汰司 / 同志社大学理工学部インテリジェント情報工学科"},{"subitem_text_language":"ja","subitem_text_value":"小野, 景子 / 同志社大学理工学部インテリジェント情報工学科准教授"},{"subitem_text_language":"ja","subitem_text_value":"槇原, 絵里奈 / 同志社大学理工学部インテリジェント情報工学科助教"}]},"item_1_text_9":{"attribute_name":"著者所属（英）","attribute_value_mlt":[{"subitem_text_language":"en","subitem_text_value":"Fukao, Kentaro / Graduate School of Science and Engineering, Doshisha University"},{"subitem_text_language":"en","subitem_text_value":"Kobayashi, Taishi / Graduate School of Science and Engineering, Doshisha University"},{"subitem_text_language":"en","subitem_text_value":"Ono, Keiko / Faculty of Science and Engineering, Doshisha University"},{"subitem_text_language":"en","subitem_text_value":"Makihara, Erina / Faculty of Science and Engineering, Doshisha University"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open 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EEG","subitem_title_language":"en"}]},"item_type_id":"1","owner":"21","path":["9200","9201"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2022-11-09"},"publish_date":"2022-11-09","publish_status":"0","recid":"29318","relation_version_is_last":true,"title":["音楽と脳波に基づく感情推定における個体差の影響"],"weko_creator_id":"21","weko_shared_id":-1},"updated":"2024-01-26T05:50:44.968931+00:00"}