# A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG
## 4 Materials and Experiments
### Performances on SEED IV Database  
In this section, the five-fold cross-verification prediction results of a four-category classification problem are verified through the comparison of HCRC-CGEL with SVM, KNN, RF, and HCRC-SMV on the SEED IV database. In Figure 8, the overall accuracies of HCRC-CGEL and HCRC-SMV are higher than those of SVM, KNN, and RF, and RF outperforms KNN and SVM. The prediction results of HCRC-CGEL are better than those of HCRC-SMV for most subjects in the three sessions.  
Table 4 shows the average prediction accuracy rates of the five methods. The accuracy rates of HCRC-CGEL in the three sessions are 96.36%, 96.97%, and 97.61%, which are better than those of SVM, KNN, RF, and HCRC-SMV. In addition, Figure 9 shows the confusion matrices of HCRC-CGEL on the SEED IV database. In the four classification problems, except for the neutral emotion classification accuracy of session one, which is 94.41%, the neutral emotion in the other session and all emotions in all the sessions are all over 96%. The standard deviations of HCRC-SMV and HCRC-CGEL are 2.18 and 2.01, respectively. This means our framework has good performance on the SEED IV database.  
As shown in Table 5, our framework is compared with the SOTA methods of other researchers on the SEED IV database. For the sake of fairness, only the emotion recognition results of the four-classification problem are considered, and the final results are represented by the average prediction accuracy of all subjects in each session and the corresponding standard deviation. The results show that, compared with the SOTA methods, our framework has higher classification accuracy and lower standard deviations on the SEED IV database.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **RF** & **KNN** & **SVM** & **HCRC-SMV** & **HCRC-CGEL** \\ \hline DEAP(A) & 92.86 & 88.94 & 87.81 & 93.88 & 94.93 \\ DEAP(V) & 93.14 & 88.13 & 87.48 & 94.26 & 95.09 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance (%) of each method on DEAP database.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Arousal (\%)** & **Valence (\%)** \\ \hline
3 DCNER ([PERSON] et al.) [42] & 84.53 & 83.83 \\ ERDL ([PERSON] et al.) [43] & 85.27 & 84.81 \\ ERHGCN ([PERSON] et al.) [44] & 88.79 & 90.56 \\ SFE-Net ([PERSON] et al.) [45] & 91.94 & 92.49 \\ CR-GCN ([PERSON] et al.) [46] & 93.46 & 94.78 \\ Our Approach (HCRC-SMV) & 93.88 & 94.26 \\ Our Approach (HCRC-CGEL) & 94.93 & 95.09 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean accuracy in arousal and valence of DEAP database.  
Figure 7: The confusion matrix in the arousal and valence of DEAP databases.  
As this section only discusses the decision fusion results of 15 combinations of all adjacent frequency bands, we have published the prediction results of each combination in some used datasets (the arousal of the DEAP database and the third session of the SEED IV database) on Github ([[https://github.com/zipore/HCRC-CGEL](https://github.com/zipore/HCRC-CGEL)]([https://github.com/zipore/HCRC-CGEL](https://github.com/zipore/HCRC-CGEL)), accessed on 21 August 2022). The 5-fold cross-validation results indicate that the accuracy of the prediction  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **RF** & **KNN** & **SVM** & **HCRC-SMV** & **HCRC-CGEL** \\ \hline SEED IV 1 & 91.64 & 84.94 & 86.84 & 96.25 & 96.36 \\ SEED IV 2 & 91.31 & 84.53 & 87.67 & 96.80 & 96.97 \\ SEED IV 3 & 93.51 & 87.89 & 90.28 & 97.52 & 97.61 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance (%) of different methods in the three sessions of SEED IV database.  
Figure 8: Results of each method on SEED IV database.  
Figure 9: The confusion matrix in the three trials of SEED IV databases.  
increases with the number of adjacent frequency bands used. This can also illustrate that the frequency bands have complementary information to each other.