EEG Complexity and Functional Connectivity During Precise Timing Prediction.

Abstract

Predictive timing in millisecond-scale is crucial for human beings to efficiently accomplish ongoing perceptions and actions. It is believed that the predictive timing process is an implementation of the predictive coding model in time domain, which can flexibly deal with both the matching and mismatching predictions. However, it's still far from being understood how the neural signature differs between the two conditions during precise timing prediction. As the brain is a complex system, it is necessary to use nonlinear measures and functional connectivity to investigate the brain function of timing prediction. Here, we probe into the EEG signatures during predictive timing process by the sample entropy (SampEn), Lempel-Ziv complexity (LZC) and partial directed coherence methods. Significant lower EEG complexity and stronger brain functional connectivity were observed when the stimulus matches the timing prediction. The current observation may shed light on the modeling of the precise predictive timing process.