Adaptive Filterng of Evoked Potentials

The AF technique shows promise for requiring fewer ensembles than averaging to attain adequate signal quality, and theoretically that the filter output converges to the least square estimate of the underlying signal.

Abstract

We present an adaptive filtering (AF) technique for rapid processing of evoked potentials (EP). The AF algorithm minimizes the mean-square error (MSE) between successive ensembles. We demonstrate theoretically that the filter output converges to the least square estimate of the underlying signal. Computer simulations with known signal and added noise show that AF produces lower MSE than ensemble averaging. We also compare results of AF to those obtained by ensemble averaging for some EP recorded from animals and humans. For very noisy EP recordings, we propose techniques that combine AF and ensemble averaging. The AF technique shows promise for requiring fewer ensembles than averaging to attain adequate signal quality.