{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T22:07:40Z","timestamp":1778623660540,"version":"3.51.4"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2017,5,4]],"date-time":"2017-05-04T00:00:00Z","timestamp":1493856000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background:<\/jats:title>\n                  <jats:p>Most investigators of brain\u2013computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)\u2013based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Findings:<\/jats:title>\n                  <jats:p>We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization\/synchronization (ERD\/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions:<\/jats:title>\n                  <jats:p>Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/gigascience\/gix034","type":"journal-article","created":{"date-parts":[[2017,5,4]],"date-time":"2017-05-04T05:28:31Z","timestamp":1493875711000},"source":"Crossref","is-referenced-by-count":296,"title":["EEG datasets for motor imagery brain\u2013computer interface"],"prefix":"10.1093","volume":"6","author":[{"given":"Hohyun","family":"Cho","sequence":"first","affiliation":[{"name":"1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea"}]},{"given":"Minkyu","family":"Ahn","sequence":"additional","affiliation":[{"name":"2School of Computer Science and Electrical Engineering, Handong Global University, 558 Handong-ro Buk-gu, Pohang Gyeongbuk 37554, Korea"}]},{"given":"Sangtae","family":"Ahn","sequence":"additional","affiliation":[{"name":"3Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, 115 Mason Farm Road, Chapel Hill, NC 27514, USA"}]},{"given":"Moonyoung","family":"Kwon","sequence":"additional","affiliation":[{"name":"1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea"}]},{"given":"Sung Chan","family":"Jun","sequence":"additional","affiliation":[{"name":"1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea"}]}],"member":"286","published-online":{"date-parts":[[2017,5,4]]},"reference":[{"key":"2024111706475094600_bib1","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1016\/S1388-2457(99)00141-8","article-title":"Event-related EEG\/MEG synchronization and desynchronization: basic principles","volume":"110","author":"Pfurtscheller","year":"1999","journal-title":"Clin Neurophysiol"},{"key":"2024111706475094600_bib2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/978-3-642-36083-1_5","article-title":"A review of performance variations in SMR-based brain-computer interfaces (BCIs)","volume-title":"Brain\u2013Computer Interface Research.","author":"Grosse-Wentrup","year":"2013"},{"key":"2024111706475094600_bib3","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1016\/j.neuroimage.2010.03.022","article-title":"Neurophysiological predictor of SMR-based BCI performance","volume":"51","author":"Blankertz","year":"2010","journal-title":"Neuroimage"},{"key":"2024111706475094600_bib4","doi-asserted-by":"crossref","first-page":"e80886","DOI":"10.1371\/journal.pone.0080886","article-title":"High theta and low alpha powers may be indicative of BCI-Illiteracy in motor imagery","volume":"8","author":"Ahn","year":"2013","journal-title":"PLoS One"},{"key":"2024111706475094600_bib5","doi-asserted-by":"crossref","first-page":"66009","DOI":"10.1088\/1741-2560\/12\/6\/066009","article-title":"Increasing session-to-session transfer in a brain\u2013computer interface with on-site background noise acquisition","volume":"12","author":"Cho","year":"2015","journal-title":"J Neural Eng"},{"key":"2024111706475094600_bib6","doi-asserted-by":"crossref","unstructured":"Cho H, Ahn M, Ahn S, Supporting data for \u201cEEG datasets for motor imagery brain computer interface.\u201d \u00a0GigaScience Database \u00a02017; 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