An approach to a phase model for steady state visually evoked potentials

Abstract

One of the most used signals in Brain Machine Interfaces (BMI) is the Steady State Visually Evoked Potentials (SSVEP). In a SSVEP-based BMI, a visual stimulus that flickers in a constant frequency is presented to the user, and the system has to detect if the user is gazing the stimulus. Usually the stimulus is a rectangular signal and there are no clear criteria for select the duty cycle, which is generally fixed to 50 %. We propose a model for SSVEP that links the phase and amplitude variations in function of the duty cycle for a specific frequency. This model can be adjusted using only the phase of the SSVEP signal and it could improve the SSVEP-based BMI by selecting the duty cycle. The model was fixed for SSVEP responses in a man who is 39 years old. The mean absolute error below 0.3 rad shows that the model predicts the phase in the majority of the used frequencies.