{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T04:14:49Z","timestamp":1759032889188,"version":"3.37.3"},"reference-count":18,"publisher":"Wiley","license":[{"start":{"date-parts":[[2019,3,3]],"date-time":"2019-03-03T00:00:00Z","timestamp":1551571200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Prospective Joint Research of Science and Technology of Jiangsu","award":["BY2016014-01","15KJB310016","2017M611190","61806174"],"award-info":[{"award-number":["BY2016014-01","15KJB310016","2017M611190","61806174"]}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["BY2016014-01","15KJB310016","2017M611190","61806174"],"award-info":[{"award-number":["BY2016014-01","15KJB310016","2017M611190","61806174"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["BY2016014-01","15KJB310016","2017M611190","61806174"],"award-info":[{"award-number":["BY2016014-01","15KJB310016","2017M611190","61806174"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science Foundation for Young Scientists of China","award":["BY2016014-01","15KJB310016","2017M611190","61806174"],"award-info":[{"award-number":["BY2016014-01","15KJB310016","2017M611190","61806174"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2019,3,3]]},"abstract":"<jats:p>A neurofeedback system adjusting an individual\u2019s attention is an effective treatment for attention-deficit\/hyperactivity disorder (ADHD). In current studies, an accurate measure of the level of human attention is one of the key issues that arouse much interest. This paper proposes a novel optimized complex network method (OCNM) for measuring an individual\u2019s attention level using single-electrode electroencephalography (EEG) signals. A time-delay embedding algorithm was used to reconstruct EEG data epochs into nodes of the OCNM network. Euclidean distances were calculated between each two nodes to decide edges of the network. Three key parameters influencing OCNM, i.e., delaying time, embedding dimension, and connection threshold, were optimized for each individual. The average degree and clustering coefficient of the constructed network were extracted as a feature vector and were classified into two patterns of concentration and relaxation using an LDA classifier. In the offline experiments of six subjects, the classification performance was tested and compared with an attention meter method (AMM) and an <jats:italic>\u03b1<\/jats:italic>\u2009+\u2009<jats:italic>\u03b2<\/jats:italic>\u2009+\u2009<jats:italic>\u03b4<\/jats:italic>\u2009+\u2009<jats:italic>\u03b8<\/jats:italic>\u2009+\u2009<jats:italic>R<\/jats:italic> method. The experimental results showed that the proposed OCNM achieved the highest accuracy rate (80.67% versus 70.58% and 68.88%). This suggests that the proposed method can potentially be used for EEG-based neurofeedback systems with a single electrode.<\/jats:p>","DOI":"10.1155\/2019\/2167871","type":"journal-article","created":{"date-parts":[[2019,3,3]],"date-time":"2019-03-03T18:31:50Z","timestamp":1551637910000},"page":"1-10","source":"Crossref","is-referenced-by-count":4,"title":["Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7696-8024","authenticated-orcid":true,"given":"Zheng-Ping","family":"Wu","sequence":"first","affiliation":[{"name":"School of Innovations, Sanjiang University, Nanjing 210012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8531-3919","authenticated-orcid":true,"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0660-1814","authenticated-orcid":true,"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9715-5476","authenticated-orcid":true,"given":"Chun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Innovations, Sanjiang University, Nanjing 210012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6886-1152","authenticated-orcid":true,"given":"Peng","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Innovations, Sanjiang University, Nanjing 210012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"doi-asserted-by":"publisher","key":"1","DOI":"10.1016\/j.biopsycho.2013.10.008"},{"doi-asserted-by":"publisher","key":"2","DOI":"10.1155\/2017\/1742862"},{"doi-asserted-by":"publisher","key":"3","DOI":"10.1016\/j.clinph.2016.06.020"},{"doi-asserted-by":"publisher","key":"4","DOI":"10.3389\/fnhum.2017.00051"},{"doi-asserted-by":"publisher","key":"5","DOI":"10.1016\/j.neulet.2010.05.026"},{"doi-asserted-by":"publisher","key":"6","DOI":"10.1109\/tbcas.2014.2384017"},{"doi-asserted-by":"publisher","key":"7","DOI":"10.3390\/s130810273"},{"doi-asserted-by":"publisher","key":"9","DOI":"10.1155\/2018\/1943565"},{"doi-asserted-by":"publisher","key":"10","DOI":"10.1016\/j.physrep.2018.10.005"},{"doi-asserted-by":"publisher","key":"11","DOI":"10.1016\/j.bspc.2014.02.002"},{"doi-asserted-by":"publisher","key":"12","DOI":"10.3724\/sp.j.1187.2012.00425"},{"doi-asserted-by":"publisher","key":"13","DOI":"10.1016\/s0167-2789(98)00240-1"},{"doi-asserted-by":"publisher","key":"15","DOI":"10.1088\/1741-2560\/10\/6\/066017"},{"doi-asserted-by":"publisher","key":"16","DOI":"10.1016\/j.neuroimage.2009.10.003"},{"doi-asserted-by":"publisher","key":"17","DOI":"10.1080\/2326263x.2013.869003"},{"doi-asserted-by":"publisher","key":"18","DOI":"10.1109\/tamd.2015.2434951"},{"doi-asserted-by":"publisher","key":"19","DOI":"10.1109\/tcds.2016.2541162"},{"doi-asserted-by":"publisher","key":"20","DOI":"10.1371\/journal.pone.0142168"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2019\/2167871.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2019\/2167871.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2019\/2167871.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,3,3]],"date-time":"2019-03-03T18:31:55Z","timestamp":1551637915000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cin\/2019\/2167871\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,3]]},"references-count":18,"alternative-id":["2167871","2167871"],"URL":"https:\/\/doi.org\/10.1155\/2019\/2167871","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"type":"print","value":"1687-5265"},{"type":"electronic","value":"1687-5273"}],"subject":[],"published":{"date-parts":[[2019,3,3]]}}}