Extracting single trial event related potentials

Abstract

Established techniques for the analysis of event related potentials (ERPs) involve averaging of time-locked sections of the EEG signal over many trials to extract the ERP waveform from the ongoing EEG noise. Several methods have been developed to enable extraction of single trial ERPs. A quantitative method was developed for testing the accuracy of single trial ERP estimates. ERP signals were simulated by using a piece-wise model of a well known ERP. A large number of unique ERPs were generated by randomly varying parameters of the model. Each of these was embedded in simulated EEG noise modelled as an auto-regressive process driven by white noise. Both stationary and nonstationary noise was simulated. The known simulated ERPs were then compared to the corresponding estimates produced by single trial ERP extraction techniques in terms of the amount of distortion introduced. The techniques tested were time sequence adaptive filtering, singularity detection using wavelets, adaptive multi-resolution analysis and a modification of the multi-resolution analysis technique. None of the methods was found to extract sufficiently accurate waveforms from single trial ERPs contaminated with realistic EEG noise. Improved, but still unsatisfactory, ERP estimates were obtained when the AR EEG noise was replaced by Gaussian noise.