EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy
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Date
2019-05
Authors
Lee, Min-Ho
Kwon, O-Yeon
Kim, Yong-Jeong
Kim, Hong-Kyung
Lee, Young-Eun
Williamson, John
Fazli, Siamac
Lee, Seong-Whan
Journal Title
Journal ISSN
Volume Title
Publisher
Oxford University Press
Abstract
Background
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.
Description
https://academic.oup.com/gigascience/article/8/5/giz002/5304369
Keywords
Electroencephalography, EEG, OpenBMI Toolbox, brain-computer interface, BCI, motor imagery, MI, event-related potential, ERP, steady-state visually evoked potential, SSVEP
Citation
Lee, 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/giz002