For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. This experiment has been conducted with five healthy participants and five patients with tetraplegia. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. Published by Oxford University Press.One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Furthermore, our results support previous but disjointed findings on the phenomenon of BCI illiteracy.īCI illiteracy EEG datasets OpenBMI toolbox brain-computer interface event-related potential motor-imagery steady-state visually evoked potential. 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. 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. 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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