Prototyping a c-VEP-based BCI Implementation on Mobile Phones
Brain-computer interfaces (BCI) establish a direct communication pathway between the human brain and the outside world by translating human intentions, measured by electrical activity in the brain, into control signals . Patients with severe motor disabilities and highly limited means of verbal expression could benefit immensely from such a novel communication channel. BCIs based on code visually evoked potentials (c-VEPs) have gained significant interest recently, primarily because they require little user training, are easy to use, and possess high information transfer rates resulting in more accurate and faster signal processing. The c-VEP signal is generated by presenting a flickering stimulus on the central retina, resulting in an electroencephalogram (EEG) pattern that matches the flickering rate . Typically, multiple targets with distinct flicker frequencies are presented to the user, each associated to a specific command. The user's intended target can then be determined by matching the c-VEP signal to the command associated with that particular flicker frequency.
Scalp EEG, which is a portable, low-cost brain monitoring technology, already has the potential to be commercialized for the public . If the scalp EEG could potentially be combined with commonly used mobile phones for stimuli presentation, this could become a highly useable communication channel for patients in everyday life. What is however still unclear; are mobile phone screens technologically suitable for the demonstration of stimuli and consequently the generation of consistent c-VEP signals? To answer this question the current research project is concerned with the development of a prototypical mobile application that generates patterns flickering at (pseudo)random frequencies. The key functionalities of the application involve: 1. A layout of four word boxes flickering at different frequencies, 2. A user feedback system for training purposes, 3. Compatibility with widely used mobile operating systems and different mobile phone hardware and 4. An intuitive and easily understandable interface to minimize the amount of required training. In the end, the performance of the app will be tested in terms of consistency and accuracy of flicker frequencies across various mobile devices and over multiple trials. Lastly, a successful implementation of the application prototype would be succeeded by testing the generation of c-VEPs with EEG recording, however this is outside the scope of the current project.
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 R. Abiri, S. Borhani, E. W. Sellers, Y. Jiang, and X. Zhao, “A comprehensive review of EEG-based brain–computer interface paradigms,” Journal of Neural Engineering, vol. 16, no. 1, p. 011001, 2019. doi:10.1088/1741-2552/aaf12e