![]() Our EEG dataset can be utilized for a wide range of BCI-related research questions. Interestingly, we found no universally illiterate BCI user, i.e., all participants were able to control at least one type of BCI system. 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. Compared to the ERP and SSVEP paradigms, the MI paradigm exhibited large performance variations between both subjects and sessions. Furthermore, we looked for more general, severe cases of BCI illiteracy than have been previously reported in the literature.Īverage 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. We evaluated the decoding accuracies for the individual paradigms and determined performance variations across both subjects and 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. Here, we present a BCI dataset that includes the three major BCI paradigms with a large number of subjects over multiple sessions. Furthermore, our results support previous but disjointed ndings on the phenomenon of BCI illiteracy.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). 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. all participants were able able to control at least one type of BCI system. Interestingly, we found no universally illiterate BCI user, i.e. they were able to pro ciently perform all three paradigms. Furthermore, we found that 27.8% (15 out of 54) of users were universally BCI literate, i.e. Compared to the ERP and SSVEP paradigms, the MI paradigm exhibited large performance variations between both, subjects and sessions. ![]() Furthermore, we looked for more general, severe cases of BCI illiteracy than have been previously reported in the literature.Īverage decoding accuracies across all subjects and sessions were 71.1% (☐.15), 96.7% (☐.05), and 95.1% (☐.09), and rates of BCI illiteracy were 53.7%, 11.1%, and 10.2% for MI, ERP, and SSVEP, respectively. We evaluated the decoding accuracies for the individual paradigms and determined performance variations across both, subjects and sessions. In this paper, we present a BCI dataset that includes the three major BCI paradigms with a large number of subjects over multiple sessions. 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).
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