{"created":"2025-01-19T01:19:10.380558+00:00","updated":"2025-01-19T15:01:08.850331+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218827","sets":["1164:3925:10844:10942"]},"path":["10942"],"owner":"44499","recid":"218827","title":["超音波による誘発脳波を用いた個人識別-電極間相互特徴の導入-"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-07-12"},"_buckets":{"deposit":"1bcd5ceb-97a4-4dac-86b6-3235bca8b87b"},"_deposit":{"id":"218827","pid":{"type":"depid","value":"218827","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"超音波による誘発脳波を用いた個人識別-電極間相互特徴の導入-","author_link":["569920","569919","569921","569922"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"超音波による誘発脳波を用いた個人識別-電極間相互特徴の導入-"},{"subitem_title":"Person Verification Using EEG Evoked by Ultrasound Introduction of a mutual feature between electrodes","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"BioX","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-07-12","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"鳥取大学大学院持続性社会創成科学研究科"},{"subitem_text_value":"鳥取大学学術研究院工学系部門"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Sustainability Sciences, Tottori University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Engineering, Tottori 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/218827/files/IPSJ-CSEC22098022.pdf","label":"IPSJ-CSEC22098022.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CSEC22098022.pdf","filesize":[{"value":"1.6 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"30"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"7040f735-6b86-4161-b386-07254982cfa4","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"向井, 宏太朗"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中西, 功"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kotaro, Mukai","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Isao, Nakanishi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11235941","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":"2188-8655","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"システムの利用者管理における継続認証を実現するため，人が知覚できない超音波を提示した際の誘発脳波を用いて個人を識別する研究を行っている．これまでの研究では，脳波のスペクトル分布と 4 つの非線形量を特徴としてサポートベクターマシン（SVM）により学習・識別を行い，全脳波電極での結果を多数決判定することで，EER= 0 % を実現したが，それには多くの SVM モデルの構築が必要であり，モデル数削減が課題であった．本論文では，従来とは異なる性質を持つ電極間相互特徴を導入することで，より少ない SVM モデル数で EER = 0 % が実現できることを示す．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In user management, to realize continuous authentication of users, we have studied to use an electroencephalogram (EEG) evoked by an ultrasound as biometrics. In the previous studies, using a spectrum and four nonlinear quantities in EEG as individual features and a support vector machine (SVM) as a verification method achieved EER = 0 %, but it required many SVM models, in which a large amount of computation was consumed for learning. In this paper, we introduce a mutual feature between electrodes and confirm that it is effective for achieving EER = 0 % with less number of SVM models.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータセキュリティ（CSEC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-07-12","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"22","bibliographicVolumeNumber":"2022-CSEC-98"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":218827,"links":{}}