Novel method for measuring the complexity of schizophrenic EEG based on symbolic entropy analysis

Symbolic dynamics is a useful tool in several fields of complexity analysis in nonlinear science. In order to investigate complexities of the human brain electrical activities under different brain functional states, a novel method in terms of symbolic entropy is defined and proposed in this paper. The novel algorithm based on symbolic dynamics is developed for quantitatively measuring the complexity of the EEG data. Simulated signals derived from chaotic systems and several real EEG data under normal and pathological brain functional states are examined and compared. The experimental results show that the proposed method can distinguish not only the complexities of simulated signals but also the complexities of two groups of EEG data under different brain functional states. It is superior to the traditional entropy methods. Moreover, the algorithm can be easily completed and fast computed.