Complexity analysis of EEG signals

Electroencephalogram (EEG) is known to be a complex time series signal and also is important in clinical neurophysiology. Here the technique of easily calculable measure of complexity C was applied to the analysis of EEG signals. Sixteen-channel EEG signals of 20 normal and 20 apoplectic subjects between 55 and 78 years of age were measured monopolarly according to the international ten-twenty system with the ipsilateral ear (A1 or A2) as the reference. The results show that the C values for the normal population are significantly higher than those of the apoplectic population at 0.01 confidence level for all channels and the authors propose that complexity C could help determine if EEG is normal or abnormal.