Supporting data for "EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms: An Investigation into BCI Illiteracy"

Dataset type: Neuroscience, Software
Data released on January 24, 2019

Lee M; Kwon O; Kim Y; Kim H; Lee Y; Williamson J; Fazli S; Lee S (2019): Supporting data for "EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms: An Investigation into BCI Illiteracy" GigaScience Database. http://dx.doi.org/10.5524/100542

DOI10.5524/100542

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). In this paper, 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.
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 pro ciently perform all three paradigms. Interestingly, we found no universally illiterate BCI user, i.e. all participants were able able to control at least one type of BCI system.
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 ndings on the phenomenon of BCI illiteracy.

Additional details

Read the peer-reviewed publication(s):

(PubMed: 30698704)

Additional information:

https://github.com/PatternRecognition/OpenBMI





File NameSample IDData TypeFile FormatSizeRelease Date 
MatLabUNKNOWN905.88 MB2018-12-14
GitHub archivearchive107.56 MB2018-12-14
MatLabUNKNOWN20.95 KB2018-12-14
MatLabUNKNOWN0.72 KB2018-12-14
ReadmeTEXT48.98 KB2018-12-14
MatLabUNKNOWN22.78 MB2018-12-14
MatLabUNKNOWN580.8 MB2018-12-14
MatLabUNKNOWN612.04 MB2018-12-14
MatLabUNKNOWN24.07 MB2018-12-14
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Funding body Awardee Award ID Comments
Washington Grain Commission Z Zhang 126593
National Institute of Food and Agriculture Z Zhang 2016-68004-24770
National Institute of Food and Agriculture Z Zhang 2015-05798
Date Action
January 24, 2019 Dataset publish
October 7, 2022 Manuscript Link updated : 10.1093/gigascience/giz002
November 15, 2022 Data type for File OpenBMI-master.zip updated