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

Min Ho Lee, O. Yeon Kwon, Yong Jeong Kim, Hong Kyung Kim, Young Eun Lee, John Williamson, Siamac Fazli, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

234 Citations (Scopus)


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.

Original languageEnglish
Issue number5
Publication statusPublished - 2019 May 1

Bibliographical note

Funding Information:
This research was supported by the MSIT (Ministry of Science and Information Technology), Korea, under the SW (Software) Starlab support program (IITP-2015-1107) supervised by the IITP (Institute for Information & Communications Technology Promotion) and funded by the Korea government (2017-0-00451, Development of BCI (Brain Computer Interface) based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning).

Publisher Copyright:
© The Author(s) 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.


  • BCI illiteracy
  • Brain-computer interface
  • EEG datasets
  • Event-related potential
  • Motor-imagery
  • OpenBMI toolbox
  • Steady-state visually evoked potential

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications


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