{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T06:52:39Z","timestamp":1767423159898,"version":"3.37.3"},"reference-count":35,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,1,30]],"date-time":"2022-01-30T00:00:00Z","timestamp":1643500800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82172058","81771926","61763022","62006246"],"award-info":[{"award-number":["82172058","81771926","61763022","62006246"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,1,30]]},"abstract":"<jats:p>The traditional imagery task for brain\u2013computer interfaces (BCIs) consists of motor imagery (MI) in which subjects are instructed to imagine moving certain parts of their body. This kind of imagery task is difficult for subjects. In this study, we used a less studied yet more easily performed type of mental imagery\u2014visual imagery (VI)\u2014in which subjects are instructed to visualize a picture in their brain to implement a BCI. In this study, 18 subjects were recruited and instructed to observe one of two visual-cued pictures (one was static, while the other was moving) and then imagine the cued picture in each trial. Simultaneously, electroencephalography (EEG) signals were collected. Hilbert\u2013Huang Transform (HHT), autoregressive (AR) models, and a combination of empirical mode decomposition (EMD) and AR were used to extract features, respectively. A support vector machine (SVM) was used to classify the two kinds of VI tasks. The average, highest, and lowest classification accuracies of HHT were 68.14\u2009<jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mo>\u00b1<\/a:mo>\n                     <\/a:math>\n                  <\/jats:inline-formula>\u20093.06%, 78.33%, and 53.3%, respectively. The values of the AR model were 56.29\u2009<jats:inline-formula>\n                     <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                        <c:mo>\u00b1<\/c:mo>\n                     <\/c:math>\n                  <\/jats:inline-formula>\u20092.73%, 71.67%, and 30%, respectively. The values obtained by the combination of the EMD and the AR model were 78.40\u2009<jats:inline-formula>\n                     <e:math xmlns:e=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\">\n                        <e:mo>\u00b1<\/e:mo>\n                     <\/e:math>\n                  <\/jats:inline-formula>\u20092.07%, 87%, and 48.33%, respectively. The results indicate that multiple VI tasks were separable based on EEG and that the combination of EMD and an AR model used in VI feature extraction was better than an HHT or AR model alone. Our work may provide ideas for the construction of a new online VI-BCI.<\/jats:p>","DOI":"10.1155\/2022\/1038901","type":"journal-article","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T05:50:14Z","timestamp":1643608214000},"page":"1-10","source":"Crossref","is-referenced-by-count":6,"title":["Identification of Visual Imagery by Electroencephalography Based on Empirical Mode Decomposition and an Autoregressive Model"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4820-6337","authenticated-orcid":true,"given":"Yunfa","family":"Fu","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"},{"name":"Brain Cognition and Brain\u2013Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3842-4672","authenticated-orcid":true,"given":"Zhaoyang","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2715-2296","authenticated-orcid":true,"given":"Anmin","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chinese People\u2019s Armed Police Force Engineering University, Xi\u2019an 710000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1675-3547","authenticated-orcid":true,"given":"Qian","family":"Qian","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"},{"name":"Brain Cognition and Brain\u2013Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0210-6506","authenticated-orcid":true,"given":"Lei","family":"Su","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"},{"name":"Brain Cognition and Brain\u2013Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2775-0031","authenticated-orcid":true,"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Brain Cognition and Brain\u2013Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, China"},{"name":"Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-31472-9"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/5.939829"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/s1388-2457(02)00057-3"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cogbrainres.2005.08.014"},{"key":"5","first-page":"143","article-title":"Measuring movement imagery abilities: a revision of the movement imagery questionnaire","volume":"21","author":"C. 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