EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy

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dc.contributor.authorLee, Min-Ho
dc.contributor.authorKwon, O-Yeon
dc.contributor.authorKim, Yong-Jeong
dc.contributor.authorKim, Hong-Kyung
dc.contributor.authorLee, Young-Eun
dc.contributor.authorWilliamson, John
dc.contributor.authorFazli, Siamac
dc.contributor.authorLee, Seong-Whan
dc.date.accessioned2019-12-12T09:22:59Z
dc.date.available2019-12-12T09:22:59Z
dc.date.issued2019-05
dc.descriptionhttps://academic.oup.com/gigascience/article/8/5/giz002/5304369en_US
dc.description.abstractBackground Electroencephalography (EEG)-based brain-computer interface (BCI) systems are mainly divided into three major paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). Here, we present a BCI dataset that includes the three major BCI paradigms with a large number of subjects over multiple sessions. In addition, information about the psychological and physiological conditions of BCI users was obtained using a questionnaire, and task-unrelated parameters such as resting state, artifacts, and electromyography of both arms were also recorded. We evaluated the decoding accuracies for the individual paradigms and determined performance variations across both subjects and sessions. Furthermore, we looked for more general, severe cases of BCI illiteracy than have been previously reported in the literature. Results Average decoding accuracies across all subjects and sessions were 71.1% (± 0.15), 96.7% (± 0.05), and 95.1% (± 0.09), and rates of BCI illiteracy were 53.7%, 11.1%, and 10.2% for MI, ERP, and SSVEP, respectively. Compared to the ERP and SSVEP paradigms, the MI paradigm exhibited large performance variations between both subjects and sessions. Furthermore, we found that 27.8% (15 out of 54) of users were universally BCI literate, i.e., they were able to proficiently perform all three paradigms. Interestingly, we found no universally illiterate BCI user, i.e., all participants were able to control at least one type of BCI system. Conclusions Our EEG dataset can be utilized for a wide range of BCI-related research questions. All methods for the data analysis in this study are supported with fully open-source scripts that can aid in every step of BCI technology. Furthermore, our results support previous but disjointed findings on the phenomenon of BCI illiteracy.en_US
dc.identifier.citationLee, M.-H., Kwon, O.-Y., Kim, Y.-J., Kim, H.-K., Lee, Y.-E., Williamson, J., … Lee, S.-W. (2019). EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy. GigaScience, 8(5). https://doi.org/10.1093/gigascience/giz002en_US
dc.identifier.issn2047-217X
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/4448
dc.language.isoenen_US
dc.publisherOxford University Pressen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectElectroencephalographyen_US
dc.subjectEEGen_US
dc.subjectOpenBMI Toolboxen_US
dc.subjectbrain-computer interfaceen_US
dc.subjectBCIen_US
dc.subjectmotor imageryen_US
dc.subjectMIen_US
dc.subjectevent-related potentialen_US
dc.subjectERPen_US
dc.subjectsteady-state visually evoked potentialen_US
dc.subjectSSVEPen_US
dc.titleEEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracyen_US
dc.typeArticleen_US
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