Abstract
The stochastic process assumed to generate the electroencephalogram is modelled by a filter that is described by poles only. Digital Wiener filtering provides a linear predictor, whereas the inverse of the prediction-error filter serves as a model. The accuracy of the fitting procedure is assessed by the prediction error. Application of the prediction filter to the electroencephalogram demonstrates the admissibility of the model and, moreover, the weak statistical inference of the underlying process. Diffusion processes seem to be an adequate description in the continuoustime domain.
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