## 4 Discussion  
Although the area of affective computing has developed a lot over the past years, the topic of EEG-based emotion recognition is still a challenging problem. This paper introduced LR with Gaussian kernel and Laplacian prior for EEG-based emotion recognition. The Gaussian kernel enhances the EEG data separability in the transformed space, and the Laplacian prior controls the complexity of the learned LR regressor in the training process. The LORSAL algorithm was introduced to optimize the LR with Gaussian kernel and Laplacian prior for its low computational complexity. Various spectral power features in the frequency domain and features by combining the asymmetrical electrodes, PSD, DE, DASM, RASM, and DCAU, were extracted for the Delta, Theta, Alpha, Beta, Gamma and Total frequency bands using 256-point STFT from the segmented 1 s EEG epochs. The experiments were conducted on the publicly available DEAP dataset, and the performance of the introduced LORSAL methods was compared with the NB, SVM, LR_L1, and LR_L2 classifiers. The experimental results showed that LORSAL presented the best accuracies of 77.17% and 77.03% in valence and arousal dimensions, respectively, on the DE features from the total frequency bands, while the SVM classifiers obtained the second-highest accuracies of 69.55% and 69.92%. The other evaluation metrics obtained by LORSAL, SVM, and NB were also tabulated in the paper. The introduced LORSAL method also presented the best Recall (76.79% and 77.03% in valence and arousal, respectively) and F1 (76.90% and 76.47% in valence and arousal, respectively). The previous experimental results showed the superiority of the introduced LORSAL method for EEG-based emotion recognition compared to the NB, SVM, LR_L1, and LR_L2 approaches. This paper also showed an investigation of the critical frequency bands for EEG-based emotion recognition. In this study, the informative features are captured from different frequency bands: Delta, Theta, Alpha, Beta, Gamma, and Total. The previous neuroscience studies showed that specific frequency band ranges are associated with specific brain activities. For example, the EEG Alpha frequency bands are related to attentional processing, whereas the Beta bands are a reflection of emotional and cognitive processing. The experimental results showed that the LORSAL, SVM, and NB classifiers performed better on the Gamma and Beta frequency bands than other bands for different features. The comparison of the Fisher ratio also showed the effectiveness of Gamma and Beta bands in emotion recognition. The findings in this study are in accordance with the previous work about critical bands investigation [47; 48]. Additionally, the effects of different features, PSD, DE, DASM, RASM, and DCAU, on the emotion classification results were also analyzed in this paper. Experimental results show that the compared approaches, LORSAL, SVM, and NB obtained superior precision metrics on the DE features over other features. This shows the effectiveness of the DE features in distinguishing low- and high-frequency energy in EEG sequences. Meanwhile, the DASM and DCAU features presented relatively ideal classification accuracies compared to the PSD features. It is noted that DASM and DCAU have the advantages of less time consumption for their lower dimensionality than PSD and DE. For a more comprehensive analysis, -Table 5 showed a comparison of the introduced LORSAL methods, the other shallow classifiers, and the deep learning approaches for EEG-based emotion recognition of LV/HV and LA/HA on DEAP dataset. In single-trial classification by [PERSON] et al. [27], the NB after feature selection using [PERSON]'s linear discrimination, obtained the accuracies of 57.65% and 62.0% in valence and arousal dimensions. In [72], the Bayesian weighted-log-posterior function optimized with the perceptron convergence algorithm presented average precisions of 70.9% and 70.1% for valence and arousal. For within-subject emotion recognition of LV/HV and LA/HA, [PERSON] et al.