A symbolic dynamics approach to the complexity analysis of event-related potentials
基于符号动力学的认知事件相关电位的复杂度分析

The symbolic dynamics was introduced to analyze the complexity of cognitive event-related potentials (ERPs). This method was first applied to the case of the MIX(p) system, and then was compared with the approximate entropy which had been successfully used to analyze the biological and medical experimental data. The complexity of ERPs in the Oddball paradigm was calculated with the symbolic dynamics. It was found that the complexity of ERPs over the frontal, central, and parietal areas declined significantly during the processing of tasks, reached at the minimum corresponding to the latency of P300 component, and rose back after the response was performed. In addition, this method was sensitive to the discrimination between target stimuli and non-target stimuli. The results indicate that the complexity based on the symbolic dynamics can sensitively reflect the processing of the cognitive task, and will be a promising tool to analyze the neural activity associated with the cognitive task.