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 journalArticle

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). 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.

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 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
JournalGigaScience
DOIs
Publication statusE-pub ahead of print - Jan 30 2019

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Brain-Computer Interfaces
Brain computer interface
Electroencephalography
Bioelectric potentials
Evoked Potentials
Imagery (Psychotherapy)
Computer Systems
Decoding
Datasets
Literacy
Motor Evoked Potentials
Electromyography
Artifacts

Cite this

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. https://doi.org/10.1093/gigascience/giz002

EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms : An Investigation into BCI Illiteracy. / Lee, Min-Ho; Kwon, O-Yeon; Kim, Yong-Jeong; Kim, Hong-Kyung; Lee, Young-Eun; Williamson, John; Fazli, Siamac; Lee, Seong-Whan.

In: GigaScience, 30.01.2019.

Research output: Contribution to journalArticle

Lee, Min-Ho ; Kwon, O-Yeon ; Kim, Yong-Jeong ; Kim, Hong-Kyung ; Lee, Young-Eun ; Williamson, John ; Fazli, Siamac ; Lee, Seong-Whan. / EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms : An Investigation into BCI Illiteracy. In: GigaScience. 2019.
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T2 - An Investigation into BCI Illiteracy

AU - Lee, Min-Ho

AU - Kwon, O-Yeon

AU - Kim, Yong-Jeong

AU - Kim, Hong-Kyung

AU - Lee, Young-Eun

AU - Williamson, John

AU - Fazli, Siamac

AU - Lee, Seong-Whan

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N2 - 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). 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.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 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.

AB - 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). 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.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 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.

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DO - 10.1093/gigascience/giz002

M3 - Article

C2 - 30698704

JO - GigaScience

JF - GigaScience

SN - 2047-217X

ER -