## 1 Introduction
It operates as a metric for gauging the complexity of the network with highly structured networks exhibiting higher node degree entropy. Additionally, NDE can be harnessed to scrutinize the correlation between node degrees, uncovering connections between nodes with specific degrees. This aspect proves instrumental in capturing features of degree correlation, providing valuable insights into the network's organization. In essence, NDE, by encapsulating the diversity and uncertainty present in degree distribution, contributes supplementary information for a more profound and nuanced analysis of complex networks. The phase space reconstruction method involves deriving a set of multidimensional vectors from the original time series using embedding dimensions and delay time estimation techniques [5]. These vectors serve as nodes in the complex network, and the edges connecting these nodes are determined based on the similarity between vectors. However, this method faces instability issues during the embedding dimensions and delay time estimation process. Additionally, establishing the optimal threshold for edge relationship judgment proves challenging, resulting in diminished robustness in practical applications [4; 6]. On the other hand, the visibility graph construction method regards data points in the original time series as nodes in the network with the visual relationships between these data points serving as edges [7; 8]. In contrast to the phase space reconstruction method, the visibility graph construction method boasts fewer parameters and enhanced algorithmic robustness [9; 10; 11]. However, it is important to note that the size of the network in this approach is directly proportional to the length of the time series. Consequently, when analyzing longer time series, the complexity of the network increases correspondingly, leading to heightened computational complexity in extracting subsequent features from the complex network [12; 13; 14; 15]. In our research, we propose a pioneering method for constructing complex networks, which diverges from traditional approaches. The novelty of our method lies in the fusion of phase space reconstruction techniques and visibility graph methods, enabling the simultaneous depiction and analysis of complex network structures and dynamic behaviors from both temporal and spatial viewpoints. Phase space reconstruction delves into the internal relationships and dynamic behaviors of network nodes, while visibility graph construction highlights the overarching structure and connectivity patterns of nodes [16; 17]. By amalgamating the phase space reconstruction and visibility graph methods, we harness the advantages of both approaches, thereby enhancing the accuracy and robustness of complex network construction. The specific implementation can be tailored and fine-tuned according to practical needs [18]. Through the integration of these two methods, we attain a more comprehensive comprehension of network properties and patterns. By concurrently leveraging the benefits of phase space reconstruction and visibility graph construction, we augment the efficiency and precision of our analyses. In summary, the main contributions of our work include the following:  
1. A novel method for constructing complex networks from EEG signals, named ComNet-PSR-VG, is introduced by exploiting both the phase space reconstruction method and the visibility graph method;
2. Employing the proposed ComNet-PSR-VG method to effectively identify EEG emotion states, obtaining outstanding classification outcomes of emotion recognition. The remainder of the paper is as follows: The second part presents the proposed new method for constructing complex networks and the extracted network structure features; the third part presents the results of the data analysis and EEG emotion classification experiments; the fourth part compares our method with existing related research through experiments and results; and the last part is the conclusion of the article.