Time-frequency analysis of visual evoked potentials using chirplet transform - Electronics Letters

Introduction: Detecting signals of visual evoked potentials (VEPs) elicited by repetitive stimuli is generally difficult, since the signal-tonoise ratio (SNR) of VEPs embedded in strong background noise and spontaneous EEG is rather low [1]. When the complete information of the signal to be detected is known, the optimal detector (in the Neyman-Pearson sense) is the likelihood ratio test which is usually implemented by a matched filter. Therefore, knowing the properties of the VEP signals related to a visual stimulus is important for designing detectors. Previous studies show that a steady-state VEP (ssVEP) is established if the repetition rate of visual stimuli is higher than some value and the shape of the resulting response becomes sinusoidal [1]. A transient process, however, precedes the formation of steady-state, characterised by abrupt changes of VEP amplitude within a short time interval. Under steady-state condition, the detection task can be reduced to finding a sinusoidal signal in noise by modelling the ssVEP signal as the summation of a fundamental frequency component and the higher harmonics, and ignoring the transient component. But because of the variability in the mental state of the subject (perhaps due to a lack of concentration, tiredness or accommodation), various factors can perturb the steady-state components. Moreover, from a physiological point of view, transient VEP appears to be more appropriate for rapid and reliable signal detection. Efforts have been made recently to characterise VEP signals over both its transient and steady-state portions [2]. Matching pursuit (MP) has been recently proposed as a nonlinear decomposition algorithm to decompose a signal into a very broad class of waveforms [3]. In MP, a sub-family of time-frequency atoms is chosen from the repertoire of the waveforms in such a way as to best match the local signal structure. In this Letter, we propose applying the method of MP algorithm using four-parameter chirplet atoms to do time-frequency analysis of VEPs. The purpose is to characterise both the transient and the steady-state of visual responses.