  Event Related Potentials (ERPs) are very feeble alterations in the ongoing
Electroencephalogram (EEG) and their detection is a challenging problem. Based
on the unique time-based parameters derived from wavelet coefficients and the
asymmetry property of wavelets a novel algorithm to separate ERP components in
single-trial EEG data is described. Though illustrated as a specific
application to N170 ERP detection, the algorithm is a generalized approach that
can be easily adapted to isolate different kinds of ERP components. The
algorithm detected the N170 ERP component with a high level of accuracy. We
demonstrate that the asymmetry method is more accurate than the matching
wavelet algorithm and t-CWT method by 48.67 and 8.03 percent respectively. This
paper provides an off-line demonstration of the algorithm and considers issues
related to the extension of the algorithm to real-time applications.
