Performance and Measures of Performance for Estimators of Brain Potentials Using Real Data

Abstract

A number of filtering and smoothing procedures have been proposed for estimating evoked brain potentials. Their common goal is to reduce noise further than averaging. A statistical method is proposed for comparing these estimators for real data, thereby avoiding the use of simulated data, which usually are not representative of the shapes encountered for signal and noise and might also rely on some artificial assumptions. This approach is applied to visual evoked potential (both flash and pattern reversal). Filtering methods offer substantial gains in mean square error, but most of the gain is obtained trivially by attenuating the average. This points out the need for also using other loss functions. In terms of these, adaptive filtering brings at best gains equivalent to smoothing.