Development of a Steady-State Visually Evoked Potential (SSVEP)-Based Brain-Computer Interface for Typing Persian Texts

For disabled patients who are unable to use their muscles, brain-computer interface (BCI) systems can be used to establish a channel between their brain and outside world. Steady-state visually evoked potentials (SSVEP)-based interfaces are of brain-computer interface-spellers noted in recent years.
In this study, stimulation patterns based on Braille code with eight flickering cues were used. MATLAB psychtoolbox was used for construction of the visual stimulation. Fast Fourier transform (FFT) method and maximum classifier were used for feature extraction and classification, respectively.
Findings: We achieved 96.67% of classification accuracy and information transfer rate of 19.632 bit per minute using Steady-state visually evoked potentials brain response and Braille code.
Because of advantages such as single electrode signal recording, low number of excitation frequencies and adjustable parameters such as rest time between the stimulations, designed system is highly efficient and user friendly.