{"created":"2023-07-27T06:24:57.789674+00:00","id":8067,"links":{},"metadata":{"_buckets":{"deposit":"6884f904-eed9-48e8-86e5-a0ca96545d08"},"_deposit":{"created_by":3,"id":"8067","owners":[3],"pid":{"revision_id":0,"type":"depid","value":"8067"},"status":"published"},"_oai":{"id":"oai:kanazawa-u.repo.nii.ac.jp:00008067","sets":["934:935:936"]},"author_link":["353","377","11165","11164"],"item_8_biblio_info_8":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2008-11-01","bibliographicIssueDateType":"Issued"},"bibliographicPageStart":"A3-5","bibliographic_titles":[{"bibliographic_title":"第23回信号処理シンポジュウム（金沢）"}]}]},"item_8_description_21":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"脳波のフーリエ変換（FFT）と階層形ニューラルネッ トワークを使ったブレイン・コンピュータ・インターフェ イス（BCI）に関して，前処理の方法を提案し，メンタ ルタスクの分類性能を向上させる手法が報告されている． 　本稿では，横河電機株式会社の脳磁計測システムMEGVi- sion を使って被験者の脳活動を測定する．MEGVision は160 個のセンサーを持つ全頭型脳磁計測システムで あり，使用するセンサー位置は「前頭葉・頭頂葉・側頭 葉・後頭葉」から左右合わせて8 チャネル選択する．最 適チャネルを最も高い分類性能が得られるように初期状 態から移動させることで探索する．2 人の被験者につい て4 つのメンタルタスク（リラックス，暗算，体を動か す，回転体のイメージ）を測定する． 　分類テストの結果，初期状態では分類性能は77.5 86.88 ％だったのに対し，チャネル位置の最適化を行うことで， 88.75 93.75 ％まで分類性能は向上した．加えて，8 つ の部位間の特徴解析を行った．Multilayer neural network(MLNN) and the FFT amplitude of brain waves have been applied to 'Brain Computer Interface'(BCI)． In this paper，a magnetoencephalograph(MEG) system， 'MEGvision' developed by Yokogawa Corporation，is used to measure brain activities．MEGvision is a160-channel whole-head MEG system．Channels are selected from 8 main regions， a frontal lobe， a temporal lobe， a parietal lobe and a occipital lobe， located at the central point in the 8 lobes， are initially selected．Optimum channels are searched for in the same lobe as the initial channels in order to achieve high classification accuracy． Two subjects and four mental tasks， including relaxed situation， multiplication， playing sport and rotating an object， are used．The brain waves are measured 10 times for one subject and one mental task． Among them， 8 data sets are used for training the MLNN， and the remaining 2 data sets are used for testing．5 kinds of combinations of 2 data sets are selected for testing．Rates of correct classi cation by using the initial channels are 77.5 86.88 %．By optimizing the channels， the accuracy is improved up to 88.75 93.75 %， which is very high accuracy．Furthermore， contributions of the brain waves in the 8 lobes are analyzed．","subitem_description_type":"Abstract"}]},"item_8_description_5":{"attribute_name":"提供者所属","attribute_value_mlt":[{"subitem_description":"金沢大学理工研究域　電子情報学系","subitem_description_type":"Other"}]},"item_8_publisher_17":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"IEICE 電子情報通信学会 / 信号処理研究専門委員会 / 第23回 信号処理シンポジウム"}]},"item_8_relation_28":{"attribute_name":"関連URI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"http://www.ieice.org/~sip/symp/2008/","subitem_relation_type_select":"URI"}}]},"item_8_version_type_25":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"金田, 泰明"},{"creatorName":"ナカヤマ, ケンジ","creatorNameLang":"ja-Kana"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中山, 謙二"}],"nameIdentifiers":[{},{},{}]},{"creatorNames":[{"creatorName":"平野, 晃宏"}],"nameIdentifiers":[{},{},{}]},{"creatorNames":[{"creatorName":"春田, 康博"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2017-10-03"}],"displaytype":"detail","filename":"TE-PR-NAKAYAMA-K-A35.pdf","filesize":[{"value":"814.3 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"TE-PR-NAKAYAMA-K-A35.pdf","url":"https://kanazawa-u.repo.nii.ac.jp/record/8067/files/TE-PR-NAKAYAMA-K-A35.pdf"},"version_id":"5e2c416c-e3ca-42f6-94c8-308f3fa418d4"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"conference paper","resourceuri":"http://purl.org/coar/resource_type/c_5794"}]},"item_title":"MEGと階層形ニューラルネットワークによるBCIにおけるチャンネル最適化と特徴解析","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"MEGと階層形ニューラルネットワークによるBCIにおけるチャンネル最適化と特徴解析"},{"subitem_title":"A BCI Using MEGvision and Multilayer Neural Network - Channel Optimization and Main Lobe Contribution","subitem_title_language":"en"}]},"item_type_id":"8","owner":"3","path":["936"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-10-03"},"publish_date":"2017-10-03","publish_status":"0","recid":"8067","relation_version_is_last":true,"title":["MEGと階層形ニューラルネットワークによるBCIにおけるチャンネル最適化と特徴解析"],"weko_creator_id":"3","weko_shared_id":-1},"updated":"2023-07-28T02:14:58.836237+00:00"}