{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230409","sets":["6504:11436:11441"]},"path":["11441"],"owner":"44499","recid":"230409","title":["脳波信号のノイズの有無の判別において有効な脳波指標の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"a707738f-a91f-4e2d-b9e4-939f0bf6d5fb"},"_deposit":{"id":"230409","pid":{"type":"depid","value":"230409","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"脳波信号のノイズの有無の判別において有効な脳波指標の検討","author_link":["620730","620729"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"脳波信号のノイズの有無の判別において有効な脳波指標の検討"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2023-02-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"芝浦工大"},{"subitem_text_value":"芝浦工大"}]},"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/230409/files/IPSJ-Z85-1ZB-08.pdf","label":"IPSJ-Z85-1ZB-08.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-1ZB-08.pdf","filesize":[{"value":"427.7 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"c0563859-a259-46d5-afba-385d1f015bc0","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"鈴木, 圭"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"菅谷, みどり"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"脳波信号の解析の際に，ノイズを除去することが重要である．その手法として，脳波信号の特徴を定量化した指標を算出し，その指標をもとにノイズの有無を判別することで，当該データを分析対象から外す手法が存在する．しかし，どの指標がノイズの有無の判別に有効であるのか評価が十分ではない．そこで，本研究ではこれを明らかにすることを目的とした．評価にあたり、複数の種類や強さのノイズを作成し，これらのノイズが含まれているか否かを判別するモデルを機械学習により構築した．そして，構築したモデルを分析することで，ノイズの有無の判別に有効である指標を分析した．その結果、Hjorth complexityなどの指標が有効であることが示唆された．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"286","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"285","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230409,"updated":"2025-01-19T11:09:35.257817+00:00","links":{},"created":"2025-01-19T01:30:09.414633+00:00"}