Event-related potentials (ERPs) reflect the brain activities related to specific behavioral events, and are obtained by averaging across many trial repetitions with individual trials aligned to the onset of a specific event, e.g., the onset of stimulus (s-aligned) or the onset of the behavioral response (r-aligned). However, the s-aligned and r-aligned ERP waveforms do not purely reflect, respectively, underlying stimulus (S-) or response (R-) component waveform, due to their cross-contaminations in the recorded ERP waveforms. Zhang [J. Neurosci. Methods, 80, pp. 49-63, 1998] proposed an algorithm to recover the pure S-component waveform and the pure R-component waveform from the s-aligned and r-aligned ERP average waveforms-however, due to the nature of this inverse problem, a direct solution is sensitive to noise that disproportionally affects low-frequency components, hindering the practical implementation of this algorithm. Here, we apply the Wiener deconvolution technique to deal with noise in input data, and investigate a Tikhonov regularization approach to obtain a stable solution that is robust against variances in the sampling of reaction-time distribution (when number of trials is low). Our method is demonstrated using data from a Go/NoGo experiment about image classification and recognition.