To evaluate the synchronization of bivariate time series has been a hot topic and a number of measures have been proposed. In this work, by introducing the ordinal pattern transition network (OPTN) into the crossplot, a new method for measuring the synchronisation of bivariate time series is proposed. After the crossplot been partitioned and coded, the coded partitions are defined as network nodes and a directed weighted network is constructed based on the temporal adjacency of the nodes. The crossplot transition entropy (CPTE) of the network is proposed as an indicator of the synchronization between two time series. To test the characteristics and performance of the method, it is used to analyse the unidirectional coupled Lorentz model and  compared it with existing methods. The results showed the new method had the advantages of easy parameter setting, efficiency, robustness, good consistency and suitable for short time series. Finally, EEG data from auditory evoked potential EEG-Biometric dataset are investigated, and some useful and interesting results are obtained.

1. Two Lorenz dynamic models with coupling relationships were used.

2. Using sliding window method for processing.

3. Add white noise to see the impact on the analysis of the proposed method.