{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T19:34:54Z","timestamp":1778009694799,"version":"3.51.4"},"reference-count":16,"publisher":"Wiley","license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>Eye motion-based human-machine interfaces are used to provide a means of communication for those who can move nothing but their eyes because of injury or disease. To detect eye motions, electrooculography (EOG) is used. For efficient communication, the input speed is critical. However, it is difficult for conventional EOG recognition methods to accurately recognize fast, sequentially input eye motions because adjacent eye motions influence each other. In this paper, we propose a context-dependent hidden Markov model- (HMM-) based EOG modeling approach that uses separate models for identical eye motions with different contexts. Because the influence of adjacent eye motions is explicitly modeled, higher recognition accuracy is achieved. Additionally, we propose a method of user adaptation based on a user-independent EOG model to investigate the trade-off between recognition accuracy and the amount of user-dependent data required for HMM training. Experimental results show that when the proposed context-dependent HMMs are used, the character error rate (CER) is significantly reduced compared with the conventional baseline under user-dependent conditions, from 36.0 to 1.3%. Although the CER increases again to 17.3% when the context-dependent but user-independent HMMs are used, it can be reduced to 7.3% by applying the proposed user adaptation method.<\/jats:p>","DOI":"10.1155\/2016\/6898031","type":"journal-article","created":{"date-parts":[[2016,9,27]],"date-time":"2016-09-27T17:45:34Z","timestamp":1474998334000},"page":"1-9","source":"Crossref","is-referenced-by-count":6,"title":["Improving Eye Motion Sequence Recognition Using Electrooculography Based on Context-Dependent HMM"],"prefix":"10.1155","volume":"2016","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9332-3735","authenticated-orcid":true,"given":"Fuming","family":"Fang","sequence":"first","affiliation":[{"name":"Department of Information Processing, Tokyo Institute of Technology, Yokohama, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takahiro","family":"Shinozaki","sequence":"additional","affiliation":[{"name":"Department of Information Processing, Tokyo Institute of Technology, Yokohama, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasuo","family":"Horiuchi","sequence":"additional","affiliation":[{"name":"Division of Information Sciences, Chiba University, Chiba, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shingo","family":"Kuroiwa","sequence":"additional","affiliation":[{"name":"Division of Information Sciences, Chiba University, Chiba, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sadaoki","family":"Furui","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toshimitsu","family":"Musha","sequence":"additional","affiliation":[{"name":"Brain Functions Laboratory Inc., Yokohama, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1056\/nejm200105313442207"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1080\/14660820510030031"},{"key":"3","first-page":"254","volume":"46","year":"2008","journal-title":"Japanese Society for Medical and Biological Engineering"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/11\/5\/056018"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1007\/s10633-011-9259-0"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/tassp.1978.1163055"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79576-6_2"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.3233\/ais-2009-0020"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/5.18626"},{"key":"13","first-page":"172","volume":"43","year":"2005","journal-title":"Japanese Society for Medical and Biological Engineering"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2003.10.019"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2009.03.005"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1109\/78.80902"},{"key":"21","year":"2009"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"issue":"1","key":"25","first-page":"31","volume":"18","year":"1992","journal-title":"Computational Linguistics"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/6898031.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/6898031.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2016\/6898031.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2016,9,27]],"date-time":"2016-09-27T17:45:35Z","timestamp":1474998335000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cin\/2016\/6898031\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":16,"alternative-id":["6898031","6898031"],"URL":"https:\/\/doi.org\/10.1155\/2016\/6898031","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016]]}}}