## 3 Results
### Performance Evaluation of the Proposed Complex Network Method Using the Simulated Signals
Figure 6: Experimental results of extracting NDE and AND from Lorenz signals and random signals using the proposed method. (**a**) AND results based on Lorenz signals, (**b**) AND results based on random signals, (**c**) NDE results based on Lorenz signals and random signals. Performance Evaluation of EEG Emotion Recognition Based on the Proposed Complex Network Construction Method  
In Figure 7, the EEG data utilized in this study span a duration of 2 min, carefully selected from the midpoint of the 62-channel EEG signals (specifically, from 60 s to 180 s). The SEED dataset encompasses EEG signals from 15 subjects, each with 62 channels. For each channel, we embarked on constructing a complex network using three distinct methods. Subsequently, we extracted the network node degree entropy, employed it as the input for the machine-learning models, and garnered the ensuing classification results. Figure 7 and Table 1 elucidate the comparative outcomes of the three methods for classifying positive and negative emotions within the SEED dataset. Figure 7(a) contrasts the outcomes for positive and negative emotions based on the AND features, while Figure 7(b) compares the results based on the NDE features. Upon scrutinizing the classification results in Figure 7, it becomes evident that the proposed method's performance in classifying positive and negative emotions outshines significantly when compared to the outcomes of the other two conventional complex network construction methods. In this study, we conducted a comparative analysis of the impact of various data lengths on classification outcomes. Figure 8 presents our exploration using data spans of 30 s (from 60 to 90 s), 45 s (from 60 to 105 s), 60 s (from 60 to 120 s), 75 s (from 60 to 135 s), 90 s (from 60 to 150 s), 105 s (from 60 to 175 s), and 120 s (from 60 to 180 s) extracted from the SEED dataset. We employed our proposed methodology to construct complex networks for each of these seven data lengths. Subsequently, we derived the NDE feature from the constructed complex networks and inputted them into machine-learning models for classification. The outcomes depicted in Figure 8 reveal that, concerning the NDE \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Method** & **Sensitivity (\%)** & **Specificity (\%)** & **Accuracy (\%)** \\ \hline \multirow{3}{*}{AND} & OPVG & 90.96 \(\pm\) 4.06 & 91.24 \(\pm\) 6.46 & 91.39 \(\pm\) 4.69 \\  & VG & 84.69 \(\pm\) 8.63 & 86.54 \(\pm\) 9.04 & 86.51 \(\pm\) 3.19 \\  & PSR & 79.3 \(\pm\) 8.49 & 80.08 \(\pm\) 8.40 & 79.16 \(\pm\) 3.69 \\ \hline \multirow{3}{*}{NDE} & OPVG & 84.84 \(\pm\) 6.97 & 85.99 \(\pm\) 7.29 & 85.39 \(\pm\) 7.09 \\  & VG & 82.40 \(\pm\) 7.98 & 83.36 \(\pm\) 8.79 & 82.84 \(\pm\) 8.35 \\  & PSR & 81.01 \(\pm\) 7.23 & 82.40 \(\pm\) 8.01 & 81.66 \(\pm\) 7.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The performance of the ComNet-PSR-VG method in constructing both AND and NDE features to identify positive and negative emotions from EEG signals in the SEED dataset is detailed. The values are presented as means \(\pm\) standard deviations with positive emotions specified as positive instances and negative emotions as negative instances. Figure 7: The intra-individual comparison of classification results (both binary and triple) among the different methods is based on the SEED dataset. (**a**) Comparison of outcomes for positive and negative emotions based on the AND features; (**b**) Comparison of outcomes for positive and negative emotions based on the NDE features. feature, the classification performance for the 2 min data surpasses that of other durations. Our experimental findings indicate that selecting longer-duration data yields improved classification outcomes compared to shorter durations. Notably, with a data duration of 45 s, the proportion of redundant information increases, resulting in a slight decline in classification accuracy.