Complexity analysis of spontaneous EEG

The aim of the present paper is the assessment of the overall complexity of spontaneous and non-paroxysmal EEG signals obtained from three groups of h uman subjects, e.g., healthy, seizure and mania. Linear complexity measure suitable for multi-variate signals, along with nonlinear measures such as a pproximate entropy (ApEn) and Taken's estimator are considered. The degree of linear complexity is significantly reduced for the pathological groups c ompared with healthy group. The nonlinear measures of complexity are signif icantly decreased in the seizure group for most of the electrodes, whereas a distinct discrimination between the maniac and healthy groups based on th ese nonlinear measures is not evident.