Abstract
Brain-Computer Interfaces (BCIs) transfer human brain activities into computer commands and enable a communication channel without requiring movement. Among other BCI approaches, steady-state visual evoked potential (SSVEP)-based BCIs have the potential to become accurate, assistive technologies for persons with severe disabilities. Those systems require customization of different kinds of parameters (e.g., stimulation frequencies). Calibration usually requires selecting predefined parameters by experienced/trained personnel, though in real-life scenarios an interface allowing people with no experience in programming to set up the BCI would be desirable. Another occurring problem regarding BCI performance is BCI illiteracy (also called BCI deficiency). Many articles reported that BCI control could not be achieved by a non-negligible number of users. In order to bypass those problems we developed a SSVEP-BCI wizard, a system that automatically determines user-dependent key-parameters to customize SSVEP-based BCI systems. This wizard was tested and evaluated with 61 healthy subjects. All subjects were asked to spell the phrase “RHINE WAAL UNIVERSITY” with a spelling application after key parameters were determined by the wizard. Results show that all subjects were able to control the spelling application. A mean (SD) accuracy of 97.14 (3.73)% was reached (all subjects reached an accuracy above 85% and 25 subjects even reached 100% accuracy).
Highlights
Brain-Computer Interfaces (BCIs) transfer electroencephalographic (EEG) brain signals collected by non-invasive electrodes and elicited by the user into computer commands, without using the brain’s normal output pathways of peripheral nerves and muscles (Wolpaw et al, 2002)
We further explored BCI demographics based on the data of this large number of subjects
Female subjects performed with an information transfer rate of 25.35 (6.54) bpm while males performed with a rate of 20.12 (7.34) bpm
Summary
Brain-Computer Interfaces (BCIs) transfer electroencephalographic (EEG) brain signals collected by non-invasive electrodes and elicited by the user into computer commands, without using the brain’s normal output pathways of peripheral nerves and muscles (Wolpaw et al, 2002). Usability challenges have impeded BCI usage in everyday scenarios for a long time. This issue has been addressed by various research groups. Millán et al (2010) predicted in their review that the time is ripe for developing practical BCI prototypes that will have a real impact in improving life quality of disabled people.
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