A wavelet based algorithm for the identification of oscillatory event-related potential components

•We present a novel method for detecting specific event related potential (ERP) components from single trial EEG data.•We provide evidence that wavelet asymmetry is unique for a specific ERP component and hence can be used for detecting it in single trial EEG data.•Our results indicate high detection accuracy in offline mode and we discuss a few of the method's potential applications. 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.