## 4 Discussion  
In our research, we propose an innovative approach to constructing complex networks for EEG analysis, specifically targeting emotion recognition. This method synergizes the features of phase space and visibility, demonstrating remarkable performance in emotion recognition based on EEG signals across two categories. Our proposed method differs from the existing approaches in several key aspects. First, the selection of nodes for constructing complex networks diverges from the conventional methods. While existing approaches typically select ordinal numbers of time series as nodes, our method employs two parameters--dimension and delay--to map time series to phase space. Nodes in this phase space then serve as the foundation for constructing complex networks. Subsequently, these nodes are mapped into complex networks using visibility methods. The rationale behind the success of our method lies in the belief that the amalgamation of temporal and spatial features captures more physiological information than relying solely on temporal features. To elucidate the distinctions in representations of EEG signals in separate time-domain features and in combination with spatiotemporal features, we conducted a comprehensive analysis. Specifically, we performed time-domain feature analysis and spatiotemporal feature analysis on EEG emotional signals and EEG epilepsy signals separately. Subsequently, we compared the results obtained from these analyses. This meticulous approach provides insights into the efficacy of our proposed method, shedding light on its potential advantages in understanding and categorizing EEG signals related to emotions. To demonstrate the superior performance of our proposed method in EEG emotion classification, we conducted a comprehensive comparison with recent studies that utilized the same SEED dataset. Our evaluation involved benchmarking against studies conducted by [PERSON], [PERSON], and [PERSON]. In [PERSON]'s research, the group sparse canonical correlation analysis (GSCCA) method was introduced to perform simultaneous electroencephalogram (EEG) channel selection and emotion recognition. [PERSON]'s study utilized the graph regularized sparse linear regression (GRSLR) approach to address EEG emotion recognition problems, while [PERSON]'s study employed dynamical graph convolutional neural networks (DGCNN) for EEG emotion recognition. Figure 8: Comparison of classification results using the proposed method on different data durations. Upon analyzing the results, as depicted in Table 2, the individual EEG emotion classification accuracies for [PERSON]'s study, [PERSON]'s study, and [PERSON]'s study were 82.96%, 87.39%, and 90.40%, respectively. Notably, our proposed ComNet-PSR-VG method achieved an outstanding individual EEG emotion classification accuracy of 91.39%, signifying a significant enhancement in classification performance. These outcomes suggest that our method outperforms the benchmark studies in EEG emotion classification. The proposed ComNet-PSR-VG method effectively preserves crucial spatial structural information within the EEG, enabling more accurate and efficient classification of emotions. The experimental results underscore the method's robustness and its ability to achieve superior performance in the realm of EEG emotion recognition. In the realm of EEG emotion recognition, the temporal and spatial characteristics of features harbor abundant information, enabling a more comprehensive depiction of brain activity patterns and subsequently enhancing the precision of emotion recognition [23; 24]. [PERSON]'s investigation [25] employs attention-based convolutional recurrent neural networks (ACRNN) to dynamically assign weights to different channels, integrating extended self-attention into the RNN. This methodology yields features that retain rich information across channels and time, demonstrating significant superiority over traditional emotion recognition methods.