Wavelet Filtering of the P300 Component in Event-Related Potentials

This paper presents an application of wavelet filtering to single-trial P300 component analysis. The objective of this study is to introduce a new method for analyzing the P300 component, when performing a given cognitive task, in this case, a two-choice reaction time task. The discrete wavelet transform with Daubechies wavelet is employed to detect the presence of P300 in individual trials. Wavelet filtering is applied to remove noise and unwanted frequency components from discrete wavelet transform (DWT) coefficients based on prior knowledge of event-related potentials (ERPs). The filtering mask is computed from the grand-average of wavelet coefficients over all participants. With this filtering, the P300 component is accurately localized in both time and scale. The findings suggest the procedure to have considerable potential for the analysis of time-series data in the behavioral neurosciences