{"_buckets": {"deposit": "38cc1f4e-f49d-4ae3-9fd6-56ac638081a5"}, "_deposit": {"id": "12033", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "12033"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00012033"}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2009-09-03", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "6460", "bibliographicPageStart": "6457", "bibliographic_titles": [{"bibliographic_title": "Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2009)"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "A brain-computer interface (BCI) is a system that could enable patients like those with amyotrophic lateral sclerosis to control some equipment and to communicate with other people, and has been anticipated to be achieved. One of the problems in BCI research is a trade-off between speed and accuracy, and it is practically important to  adjust those two performance measures effectively. So far the authors have considered BCIs as communications between users and computers, and have proposed an error control method, reliability-based automatic repeat request  (RB-ARQ). It has been shown that, with linear discriminant analysis (LDA) as a classifier, RB-ARQ is more effective than other error control methods. In this paper, support vector machines (SVMs), one of the most popular classifiers, are applied to  RB-ARQ. A quantitative comparison showed that there was no significant difference between LDA and SVM. Also, it was demonstrated that RB-ARQ improved the accuracy from the one acquired by the top ranked methods in the BCI competition to 100 percents, with less loss of the speed.", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/13909"}, {"subitem_identifier_type": "DOI", "subitem_identifier_uri": "http://dx.doi.org/10.1109/IEMBS.2009.5333543"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "IEEE"}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "\u00a92009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE."}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_10_source_id_7": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1557-170X", "subitem_source_identifier_type": "ISSN"}]}, "item_10_text_14": {"attribute_name": "\u30d5\u30a9\u30fc\u30de\u30c3\u30c8", "attribute_value_mlt": [{"subitem_text_value": "application/pdf"}, {"subitem_text_value": "application/pdf"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Takahashi, Hiromu"}], "nameIdentifiers": [{"nameIdentifier": "38159", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Yoshikawa, Tomohiro"}], "nameIdentifiers": [{"nameIdentifier": "38160", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Furuhashi, Takeshi"}], "nameIdentifiers": [{"nameIdentifier": "38161", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-20"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "kokusai-0909.pdf", "filesize": [{"value": "294.7 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 294700.0, "url": {"label": "kokusai-0909.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/12033/files/kokusai-0909.pdf"}, "version_id": "bdbcacf9-909f-4d36-9ed2-ae26199f842c"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Application of support vector machines to reliability-based automatic repeat request for brain-computer interfaces", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "Application of support vector machines to reliability-based automatic repeat request for brain-computer interfaces"}]}, "item_type_id": "10", "owner": "1", "path": ["320/321/322"], "permalink_uri": "http://hdl.handle.net/2237/13909", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2010-08-02"}, "publish_date": "2010-08-02", "publish_status": "0", "recid": "12033", "relation": {}, "relation_version_is_last": true, "title": ["Application of support vector machines to reliability-based automatic repeat request for brain-computer interfaces"], "weko_shared_id": null}