Detection of Steady-State Visual Evoked Potentials Using Simulated Trains of Transient Evoked Potentials

In this paper, we address the problem of detecting steady-state visual evoked potentials (SSVEPs) in EEG signals by using a set of simulated trains of VEPs instead of the sine-waves basis typically used in Fourier Transform. The detection algorithm is calibrated using the subject's brain response to visual stimulation. The original contribution of the paper is that our detection method automatically takes into account all the spectral content adapted to the steady-state response in terms of harmonic localization, weights, and phase. We show that this method give better results than simple frequency analysis for SSVEP detection while requiring less features, thereby reducing the risk of overfitting the detection model.