## 3 Experimental Results
### Overall Classification Accuracy  
The mean accuracies and standard deviations obtained by different classifiers in valence dimension for different features extracted from five frequency bands (Delta, Theta, Alpha, Beta, and Gamma) and the total frequency bands are tabulated in Table 1. It should be noted that 'Total' in Table 1 denotes the features by concatenating all different features from all frequency bands. Given the same features extracted from the EEG signals, the accuracy metrics of the black bold font in Table 1 indicate the highest accuracies obtained by different classifiers for each frequency band, while the precision metrics with gray background denote the highest precisions obtained by compared methods for all kinds of frequency bands. The LORSAL methods obtained the highest accuracy of 77.17% for the DE feature from the total frequency bands among all the compared classifiers. Under the same case, the highest classification accuracy obtained by SVM is 69.55%, while the best accuracy by NB is 62.36% for the DASM feature from the total frequency bands. The SVM classifier is the most widely applied approach based on spectral features, especially PSD. Table 1 shows that the performance of SVM is second only to LORSAL on all features extracted from total frequency bands, and the corresponding accuracies are 69.04%, 69.55%, 64.48%, 48.17%, and 63.48% for the PSD, DE, DASM, RASM, and DCAU features. In study [27], the NB classifier obtained an accuracy of 57.6% in the valence dimension. In this study, the mean precisions obtained by NB are approximately between 60% and 62% for the PSD, DE, DASM, and DCAU features from the total frequency bands. However, the best accuracies obtained by LR_L1 and LR_L2 are approximately 46%, which is significantly lower than those obtained by NB, SVM, and LORSAL. Although the LR_L1 and LR_L2 adopted L1-regularization and L2-regularization during the optimization of LR regressors to avoid over-specification, the assumption of linear separability does not hold for the extracted features from EEG signals. The average precisions obtained by the LORSAL have significant improvement over these by LR_L1 after incorporating the Gaussian kernel. The Gaussian kernel can enhance the data separability in the transformed space and meanwhile, the Laplacian prior can promote sparsity on the learned LR regressor and avoid over-specification of the selected training EEG epochs. For the classification task of LV/HV emotions, the LORSAL methods present the best classification accuracies, 77.17%, 71.63%, and 69.89% on the DE, DASM, and DCAU features from total frequency bands, which are higher than SVM by about 8%, 7%, and 6%. The SVM and NB classifiers perform best by accuracies 69.04% and 55.65% on the PSD and RASM features from 'Total' bands, respectively. As is shown in Table 2, the performance of the five classifiers on classifying LA/HA emotions is similar to the case of LV/HV classification. The introduced LORSAL method performed best on the DE, DASM, and DCAU features from 'Total' bands, and the corresponding accuracies outperformed these of SVM by about 7%, 9%, and 8%. In addition, the performance of LORSAL was obviously better than that