{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00226481","sets":["1164:2735:11166:11285"]},"path":["11285"],"owner":"44499","recid":"226481","title":["LSTMによる脳波の周波数および時間領域特徴量を用いた感情推定"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-06-22"},"_buckets":{"deposit":"08cc1ec8-d6f1-4fcd-90c3-1f101447bcf9"},"_deposit":{"id":"226481","pid":{"type":"depid","value":"226481","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"LSTMによる脳波の周波数および時間領域特徴量を用いた感情推定","author_link":["601391","601392","601390"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"LSTMによる脳波の周波数および時間領域特徴量を用いた感情推定"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"数理モデル化と問題解決1","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2023-06-22","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"同志社大学理工学研究科"},{"subitem_text_value":"同志社大学理工学研究科"},{"subitem_text_value":"同志社大学理工学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Science and Engineering, Doshisha University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Science and Engineering, Doshisha University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Science and Engineering, Doshisha University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/226481/files/IPSJ-MPS23143013.pdf","label":"IPSJ-MPS23143013.pdf"},"date":[{"dateType":"Available","dateValue":"2025-06-22"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS23143013.pdf","filesize":[{"value":"966.4 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"0000fbaf-3e89-49f3-ace1-d54d4f4e4dc3","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"岡, 颯人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"谷, 祐貴"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小野, 景子"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"機械学習を用いた感情分類に関する研究が活発化している．本研究では，生理信号の一種である脳波データを用いて感情分類を試みる．具体的に，脳波データの前処理として，基本的な情動の脳波データと特定の感情が誘発された際の脳波データの振幅変化量を算出し，時間領域特徴量として用い，感情分類モデルとして，時系列データを分析することが可能な Long Short-Term Memory（以下，LSTM）を用いて分類精度を検証する．さらに，前処理後の脳波データを Wavelet 変換を用いて周波数領域特徴量を抽出し，LSTM を用いて分類精度を検証する．結果，時間領域特徴量用いた提案モデルの検証実験では，快，不快の指標となる Valence を用いた場合において 85.59% の分類精度を記録し，感情の強度を示す Arousal を用いた場合は 85.27% の分類精度を得た．また，周波数領域特徴量を用いた提案モデルの検証実験では，Valence を用いた場合において 86.85% の分類精度を記録し，Arousal を用いた場合は 86.65% の分類精度を得た．実験結果より，脳波データの前処理および感情分類モデルは感情分類において有効性を示した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"2","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-06-22","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"13","bibliographicVolumeNumber":"2023-MPS-143"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:25:55.820237+00:00","updated":"2025-01-19T12:27:49.900988+00:00","id":226481}