Time-frequency analysis of visual evoked potentials by means of matching pursuit with chirplet atoms

Detection of visual evoked potentials (VEP) elicited by repetitive stimuli is valuable in both laboratorial research and clinical practice. Therefore, knowing the characteristics of VEPs is of fundamental importance for adequate design of a signal detector. Usually, the signal is modeled as a steady-state VEP (ssVEP) consisting of the fundamental frequency and the higher harmonics, while ignoring the information contained in its transients (tVEP). We propose here to characterize both tVEP and ssVEP by chirplet time-frequency representation of VEP signal using a matching pursuit (MP) algorithm. Compared to the time-frequency analysis with short-time-Fourier-transform (STFT) and linear-prediction-coding (LPC) method, MP with chirplet shows not only clear characteristics of ssVEP, but a clear spindle-like time-frequency component of tVEP as well, which is not obvious in the other two methods.