SPECTRAL ESTIMATION OF EEG SIGNALS USING CASCADED INVERSE FILTERS

While most of the studies on application of autoregressive (AR) method s to EEG signals have considered direct modelling of EEG data, this pa per considers the inverse problem of passing the EEG signal through an inverse filter and shows how such inverse filters when cascaded give an improved spectral estimate of the input data. It is shown how a pro per choice of model orders of such cascaded inverse filters leads to b etter spectral estimation of an EEG signal than by conventional AR fil ters. An EEG signal, when first passed through a low order inverse fil ter, actually results in a signal with reduced dynamic range and thus a second inverse filter with higher order gives much better spectral p eaks. In fact, such cascading operation reduces the problem of ill con ditioning of the autocorrelation matrix thus yielding better results. The analysis has been performed using real EEG data.