An optimal linear filter for the reduction of noise superimposed to the EEG signal.

In the present paper a procedure for the reduction of super-imposed noise on EEG tracings is described, which makes use of linear digital filtering and identification methods. In particular, an optimal filter (a Kalman filter) has been developed which is intended to capture the disturbances of the electromyographic noise on the basis of an a priori modelling which considers a series of impulses with a temporal occurrence according to a Poisson distribution as a noise generating mechanism. The experimental results refer to the EEG tracings recorded from 20 patients in normal resting conditions: the procedure consists of a preprocessing phase (which uses also a low-pass FIR digital filter), followed by the implementation of the identification and the Kalman filter. The performance of the filters is satisfactory also from the clinical standpoint, obtaining a marked reduction of noise without distorting the useful information contained in the signal. Furthermore, when using the introduced method, the EEG signal generating mechanism is accordingly parametrized as AR/ARMA models, thus obtaining an extremely sensitive feature extraction with interesting and not yet completely studied pathophysiological meanings. The above procedure may find a general application in the field of noise reduction and the better enhancement of information contained in the wide set of biological signals.