## 5 Results and Discussion
### Results and Discussion  
In this section, we present the results of our experiments. Due to a variation of the parameters in the classification methods, we only present the best results obtained by each method. First,a comparison of the classification accuracies between CLRNN and the baseline methods is presented in Figure 8. We present a boxplot of the mean emotion recognition accuracies with the different time windows for each subject in Figure 8. The comparison shows the effectiveness of our method. The average emotion classification accuracy of each subject with CLRNN is 75.21%, whereas the average accuracies of other classification methods are 69.58% with CNN + RNN, 67.45% with SVM, 45.47% with Random Decision Forest, and 62.84% with k-NN, respectively. The highest accuracy is obtained from Subject 4 with CLRNN, which is 90.54%. After a comparison with the baseline methods, we chose relevant studies listed in Table 2 to compare with our method. The selection of the previous studies is based on two aspects: (1) the emotion analysis is based on EEG signals; and (2) the emotion label is produced by the scales of Valence and Arousal. We found that most studies in Table 2 classified emotions into two classes: Pleasant/Unpleasant or Positive/Negative. Some studies [7; 43; 44] classified emotion into three categories: Pleasant, Neutral, and Unpleasant. In our study, we classify emotion into four types (HVHA, HVLA, LVLA, and LVHA). Two emotion classification problems are relatively simple, and the highest accuracy reached 82%. Multiple (more than two) emotion classification problems are complex, and the accuracy of our method reaches 75.21%, which is higher than the results presented in [43; 44]. The studies in [43; 44] also employ DEAP as a dataset to recognize human emotions. This shows the effectiveness of our method. In addition, [41; 43; 44] and this study all employed DEAP as the dataset to undertake the emotion analysis. The performance of our method is better than the others. A similar research method is used in [41], which also built Neural Networks by CNNs and LSTM RNNs, with the difference being that the two-dimensional EEG feature images constructed in [41] ignored the spatial characteristics of EEG signals. In this paper, the spatial features of EEG signals are considered very important for emotion recognition. Through the experiments in this study, we proved the correctness of this point. To further validate the effectiveness of our method, we investigated the effect of the time window size on the classification analysis. The MFIs with different time windows are trained and tested in CLRNN and CNN + RNN, respectively. For comparison, the features trained in the baseline method are also extracted from the raw EEG signals with the same time window size and are presented in Table 5, which shows the average of the emotional classification accuracy obtained by 32 subjects under different time windows. As seen from the results in Table 5, CLRNN showed sensitivity to the time window size. With an increase in the time window, the classification accuracy showed a decreasing trend. The accuracies of the classification from other methods did not change significantly with the  
Figure 8: Emotion recognition accuracies with different classification methods. increase of the time window. This further confirmed that the LSTM unit played a role in capturing long-term critical features during the classification process. The study in [45] analyzed the emotion classification with DEAP EEG signals from the perspective of time window size and wavelet features and obtained the highest accuracy using the wavelet entropy of three-second signal segments, which is similar to the results we obtained. Further intuitive investigation is conducted with a graphical representation of the results from CLRNN and 'CNN + RNN', as illustrated in Figure 9. It can be seen from Figure 9 that with the same small time window (before 12 s) CLRNN had higher classification accuracies than CNN + RNN without LSTM units.