WEKO3
インデックスリンク
アイテム
{"_buckets": {"deposit": "6159b3a8-730d-4879-9998-8c8962b6e429"}, "_deposit": {"created_by": 3, "id": "8035", "owners": [3], "pid": {"revision_id": 0, "type": "depid", "value": "8035"}, "status": "published"}, "_oai": {"id": "oai:kanazawa-u.repo.nii.ac.jp:00008035", "sets": ["936"]}, "author_link": ["353", "11118", "11117", "377"], "item_8_biblio_info_8": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2009-02-01", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "319", "bibliographicPageStart": "316", "bibliographic_titles": [{"bibliographic_title": "Intelligent Signal Processing and Communications Systems, 2008. ISPACS 2008. International Symposium on, Bangkok, Thailand"}]}]}, "item_8_description_21": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "Multilayer neural networks (MLNN) and the FFT amplitude of brain waves have been applied to dasiaBrain Computer Interfacepsila (BCI). In this paper, a magnetoencephalograph (MEG) system, dasiaMEGvisionpsila developed by Yokogawa Corporation, is used to measure brain activities. MEGvision is a 160-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 in the left and the right sides of the brain. The 8 channels, 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 classification by using the initial channels are 82:5 ~ 90%. By optimizing the channels, the accuracy is improved up to 85:0 ~97:5%, 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": "IEEE = Institute of Electrical and Electronics Engineers"}]}, "item_8_source_id_9": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "10.1109/ISPACS.2009.4806665", "subitem_source_identifier_type": "ISSN"}]}, "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": "Nakayama, Kenji"}, {"creatorName": "ナカヤマ, ケンジ", "creatorNameLang": "ja-Kana"}], "nameIdentifiers": [{"nameIdentifier": "353", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "00207945", "nameIdentifierScheme": "e-Rad", "nameIdentifierURI": "https://kaken.nii.ac.jp/ja/search/?qm=00207945"}, {"nameIdentifier": "00207945", "nameIdentifierScheme": "研究者番号", "nameIdentifierURI": "https://nrid.nii.ac.jp/nrid/1000000207945"}]}, {"creatorNames": [{"creatorName": "Kaneda, Yasuaki"}], "nameIdentifiers": [{"nameIdentifier": "11117", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Hirano, Akihiro"}], "nameIdentifiers": [{"nameIdentifier": "377", "nameIdentifierScheme": "WEKO"}, {"nameIdentifier": "70303261", "nameIdentifierScheme": "金沢大学研究者情報", "nameIdentifierURI": "http://ridb.kanazawa-u.ac.jp/public/detail.php?kaken=70303261"}, {"nameIdentifier": "70303261", "nameIdentifierScheme": "研究者番号", "nameIdentifierURI": "https://nrid.nii.ac.jp/nrid/1000070303261"}]}, {"creatorNames": [{"creatorName": "Haruta, Yasuhiro"}], "nameIdentifiers": [{"nameIdentifier": "11118", "nameIdentifierScheme": "WEKO"}]}]}, "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", "download_preview_message": "", "file_order": 0, "filename": "TE-PR-NAKAYAMA-K-316.pdf", "filesize": [{"value": "1.9 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 1900000.0, "url": {"label": "TE-PR-NAKAYAMA-K-316.pdf", "url": "https://kanazawa-u.repo.nii.ac.jp/record/8035/files/TE-PR-NAKAYAMA-K-316.pdf"}, "version_id": "14bbaadc-9dac-4052-8716-e8de1e9f1bc4"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "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": "A bci using megvision and multilayer neural network - Channel optimization and main lobe contribution analysis", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "A bci using megvision and multilayer neural network - Channel optimization and main lobe contribution analysis"}]}, "item_type_id": "8", "owner": "3", "path": ["936"], "permalink_uri": "http://hdl.handle.net/2297/18075", "pubdate": {"attribute_name": "公開日", "attribute_value": "2017-10-03"}, "publish_date": "2017-10-03", "publish_status": "0", "recid": "8035", "relation": {}, "relation_version_is_last": true, "title": ["A bci using megvision and multilayer neural network - Channel optimization and main lobe contribution analysis"], "weko_shared_id": -1}
A bci using megvision and multilayer neural network - Channel optimization and main lobe contribution analysis
http://hdl.handle.net/2297/18075
http://hdl.handle.net/2297/18075554b43de-d5e3-4dfa-907a-574ce827aa93
名前 / ファイル | ライセンス | アクション |
---|---|---|
TE-PR-NAKAYAMA-K-316.pdf (1.9 MB)
|
|
Item type | 会議発表論文 / Conference Paper(1) | |||||
---|---|---|---|---|---|---|
公開日 | 2017-10-03 | |||||
タイトル | ||||||
タイトル | A bci using megvision and multilayer neural network - Channel optimization and main lobe contribution analysis | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
資源タイプ | conference paper | |||||
著者 |
Nakayama, Kenji
× Nakayama, Kenji× Kaneda, Yasuaki× Hirano, Akihiro× Haruta, Yasuhiro |
|||||
提供者所属 | ||||||
内容記述タイプ | Other | |||||
内容記述 | 金沢大学理工研究域 電子情報学系 | |||||
書誌情報 |
Intelligent Signal Processing and Communications Systems, 2008. ISPACS 2008. International Symposium on, Bangkok, Thailand p. 316-319, 発行日 2009-02-01 |
|||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 10.1109/ISPACS.2009.4806665 | |||||
出版者 | ||||||
出版者 | IEEE = Institute of Electrical and Electronics Engineers | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Multilayer neural networks (MLNN) and the FFT amplitude of brain waves have been applied to dasiaBrain Computer Interfacepsila (BCI). In this paper, a magnetoencephalograph (MEG) system, dasiaMEGvisionpsila developed by Yokogawa Corporation, is used to measure brain activities. MEGvision is a 160-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 in the left and the right sides of the brain. The 8 channels, 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 classification by using the initial channels are 82:5 ~ 90%. By optimizing the channels, the accuracy is improved up to 85:0 ~97:5%, which is very high accuracy. Furthermore, contributions of the brain waves in the 8 lobes are analyzed. | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 |