Evaluation of Simple Algorithms for Spectral Parameter Analysis of the Electroencephalogram

Simple autoregressive moving-average (ARMA) and autoregressive (AR) algorithms were tested for use in spectral parameter analysis (SPA) of the background electroencephalogram (EEG). In studies on simulated EEG, both algorithms successfully extracted estimates of the spectral component parameters, and their performance was relatively independent of assumed model order. The ARMA algorithm was unbiased. The AR algorithm, though biased, was simpler and more precise and, thus, may be the most suitable for on-line use. The test results on simulated data were supported by the successful application of the algorithms to human EEG recorded during surgery.