This thesis is an investigation of the Outlier Processing Method, which was developed to eliminate the dependence of conventional techniques on a priori information for the detection of event-related potentials (ERPs) from EEG signals. Instead of attempting to model the ERP, the OPM assumes the ERP to be an outlier signal which is imbedded in a background EEG signal. The background EEG is modelled as an auto-regressive signal and approximated using robust statistical parameter and signal

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... ion. The ERP estimate is obtained by subtracting the estimate of the background EEG signal from the observed EEG signal. The basis of the robust statistical parameter estimation used in OPM was the generalized maximum likelihood (GM) estimate. It required the generation of initial parameter estimates to start the process. MEM, a least squares estimator used in the original OPM, was found to be a more consistent estimator when compared to median regression estimation. Also, a method of automatically generating the tuning constants used by the influence functions in the GM estimate was proposed. , The robust signal estimation was a robust variation of a Kalman filter which required a cleaner function to minimize the effect of outliers such as ERPs. Of the three different cleaners that were evaluated, the fixed-lag cleaner had the best overall performance. A method of detecting the location of the outlier was developed and was found to be effective for signals with low SNRs and a large amount of spectral overlap between the outlier and the background EEG signal.