A parametric method for the analysis of temporal and spatial variability in the interictal EEG signal

The analysis of variability in the EEG signal is a relatively new field of investigation. This is mainly due to the objective difficulty to develop quantitative methods of analysis. Autoregressive modeling of the EEG signal is proposed to quantify its variability. Model coefficients were computed from adjacent epochs and their temporal behavior was analyzed: background activity produced only very slow temporal changes, while a variability in the EEG provoked sharp changes in the AR sequences. To quantify the variability with a numerical value (Difference Measure, DM), the AR sequences were processed by means of a segmentation algorithm. DMs were derived for all EEG leads and analyzed under visual inspection. Preliminary results show that this approach could be of some help in the study of temporal and spatial characteristics of interictal epileptiform discharges