## 3 Experimental Results
### Investigation of Critical Frequency Bands
The following subsection presents comprehensively an analysis including the EEG neural patterns associated with emotions in previous studies [27; 47; 48], the critical frequency bands and the informative features for emotion recognition. Specific frequency ranges are highly related to certain brain activities. There are neuroscience findings [68; 69] revealing that the Alpha bands in EEG signals associate with attentional processing, while the Beta bands associate with emotional and cognitive progress. In the previous studies on the DEAP dataset by [PERSON] et al. [27], they reported negative correlations in the Theta, Alpha, and Gamma bands for arousal, and strong correlations in all investigated frequency bands for valence. Similarly, [PERSON] et al. [70] found a positive correlation of valence and the Beta and Gamma bands. From Figure 6 and Table 4, we found that the Gamma and Beta bands obtained higher values of the Fisher ratio than the other frequency bands. This means that the features extracted over Gamma and Beta bands are more effective for discriminating different emotions, as is in accordance with the classification accuracies by the compared classifiers illustrated in Figure 5. Similarly, [PERSON] and [PERSON] [71] showed that the EEG Gamma bands are appropriate for emotion recognition when images are used for emotion elicitation. The studies by [47] [PERSON] and [PERSON] found specific neural patterns in high-frequency bands for distinguishing negative, neutral, and positive emotions. For negative and neutral emotions, the energy of Beta and Gamma frequency bands decreases, while positive emotions present higher energy of these two frequency bands. Their experimental results [47] on SJTU Emotion EEG Dataset (SEED) showed that the KNN, LR_L2, SVM, and DBN classifiers performed better on Gamma and Beta frequency bands than other bands for the PSD, DE, DASM, RASM, and DCAU features. This showed the informativeness of EEG Gamma and Beta bands for emotion recognition with film clips as stimuli in the SEED dataset. The emotion elicitation materials in the DEAP dataset are one-minute videos [27]. Our experimental results in DEAP and findings were in accordance with these previous studies [47; 48]. Additionally, the total frequency bands concatenating all the original five frequency bands can further improve the emotion recognition performance, and the LORSAL obtained the highest mean accuracies in valence and arousal dimensions from the DE, DASM, and DCAU features, as is consistent with the results in studies [47; 48]. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Emotion** & \multicolumn{6}{c}{**Valence**} & \multicolumn{6}{c}{**Arousal**} \\ \hline
**Frequency** & **Delta** & **Theta** & **Alpha** & **Beta** & **Gamma** & **Delta** & **Theta** & **Delta** & **Beta** & **Gamma** \\ \hline
**PSD** & 0.046 & 0.005 & 0.014 & 0.155 & **0.414** & 0.045 & 0.005 & 0.013 & 0.124 & **0.418** \\
**DE** & 0.047 & 0.052 & 0.077 & 0.242 & **0.256** & 0.051 & 0.048 & 0.062 & 0.173 & **0.197** \\
**DASM** & 0.057 & 0.063 & 0.070 & 0.240 & **0.317** & 0.068 & 0.073 & 0.067 & 0.186 & **0.243** \\
**RASM** & 0.004 & 0.050 & 0.037 & **0.131** & 0.115 & 0.004 & 0.065 & 0.052 & **0.095** & 0.080 \\
**DCAU** & 0.059 & 0.062 & 0.064 & 0.224 & **0.307** & 0.062 & 0.064 & 0.067 & 0.195 & **0.244** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fisher ratio of different frequency bands by averaging the values over all features and subjects in valence and arousal dimensions, respectively.