## 4 Discussion  
The aim of this study is to construct a complex network using multichannel EEG signals to enable the assessment of cognitive load. The method of constructing the network directly affects the reliability of the assessment. In the process of using CPE suitable for the analysis of short time series to construct complex networks, it was found that the CPE method suffered from the lack of differentiation ability caused by considering only the probability distribution of symbols and ignoring the transition relationship between symbols in the temporal domain. In addition, as a nonlinear analysis method, the choice of parameters in the CPE had a large impact on the analysis results. In order to alleviate these issues, the CBTN is proposed to measure the coupling relationship between two time series from the perspective of cross-transition networks. The innovation of the CBTN is that it combines the advantages of the transition network and the bubble entropy on the basis of the principle of CPE. The introduction of the transition network solved the problem of ignoring the transition relationship between symbols in the CPE method. The symbolization method with reference to bubble entropy made the analysis result less affected by the embedding dimension. The effectiveness of the method was verified via a comparison with the CPE method on the unidirectional coupled Henon model. Firstly, the results show that the CBTN method could achieve satisfactory results when the signal  
Figure 5: The average clustering coefficient (**a**) and global network efficiency (**b**) of the average adjacency matrix constructed by the CBTN method for each subject. Figure 6: The average clustering coefficient (**a**) and global network efficiency (**b**) of the average adjacency matrix constructed by the CPE method for each subject. length reached 2000 samples, although this was slightly larger than that needed in the CPE method. This finding suggests that the CBTN is equally suitable for the analysis of short time series. Secondly, in the experiment study, the result tended to be stable for any coupling strength as long as the embedding dimension of the CBTN was greater than 10. This result indicates that the CBTN method is less affected by the coupling strength. Last, under weak coupling conditions, the CPE method failed to achieve the right results, while the CBTN could still obtain reliable results, indicating that the CBTN was able to uncover the weak coupling relationships between time series. These three properties ensure that the complex network constructed by the CBTN method outperformed the CPE in its ability to analyze cognitive load using EEG datasets. The results of the above analysis clearly demonstrate that the proposed method shows several advantages. 1. This method involves few parameters in use, and the value setting of the parameters has little influence on the analysis results. 2. The cross-transition network allows the method to be more sensitive to weak changes in the information interaction between two time series and is more suitable for analysis in weakly coupled conditions. 3. The normalization measures in the definition of node-wise out-link entropy minimize the impact of intersubject variation on the analysis results. 4. The implementation of the algorithm only involves the ranking of numbers and the probability distribution statistics of symbols, which is easy to be processed and implemented by a computer. Although the study showed promising results, the limitation of this work should be considered. Firstly, the adjacency matrix of the cross-transition network was a static representation of information interaction between two time series in a period of time. This means that the method was explicitly time-dependent. The analysis of excessively long time series may have caused a reduction in the variation in the adjacency matrix, making identification less effective. This needs further study.