## 3 Experimental Results
### Overall Classification Accuracy
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Feature** & **Classifier** & **Delta** & **Theta** & **Alpha** & **Beta** & **Gamma** & **Total** \\ \hline \multirow{8}{*}{**PSD**} & **NB** & 54.18/5.39 & 53.94/5.24 & 54.25/5.08 & 58.85/7.63 & 61.23/8.48 & 60.51/6.71 \\  & **SVM** & 51.14/14.31 & 47.57/15.75 & 52.35/16.04 & 62.97/12.18 & 64.15/12.19 & **69.04/5.91** \\  & **LR_L1** & 44.80/4.63 & 45.23/4.72 & 44.00/5.51 & 38.41/8.75 & 36.18/9.67 & 34.43/9.52 \\  & **LR_L2** & 44.57/4.88 & 45.11/4.92 & 44.14/5.49 & 38.47/8.63 & 36.38/9.58 & 34.38/9.57 \\  & **LRGSAL** & **56.45/5.79** & **56.19/5.34** & **58.29/5.56** & **64.95/6.78** & **68.30/7.98** & 63.29/6.54 \\ \hline \multirow{8}{*}{**DE**} & **NB** & 54.27/3.62 & 53.90/3.53 & 54.62/4.07 & 59.19/5.59 & 61.52/6.51 & 61.04/5.76 \\  & **SVM** & 52.08/13.54 & 49.76/14.14 & 52.04/13.41 & 62.91/10.23 & 65.88/9.93 & 69.55/6.58 \\  & **LR_L1** & 44.04/4.80 & 44.67/4.73 & 43.16/5.34 & 38.22/8.67 & 36.08/9.52 & 33.69/9.88 \\  & **LR_L2** & 43.86/4.88 & 44.44/4.69 & 43.15/5.41 & 38.15/8.64 & 36.06/9.52 & 33.81/9.59 \\  & **LORSAL** & **61.79/4.55** & **58.34/3.90** & **59.20/4.04** & **67.06/6.95** & **72.93/7.30** & **72.17/6.37** \\ \hline \multirow{8}{*}{**DASM**} & **NB** & 54.81/6.61 & 53.97/5.53 & 54.07/6.12 & 60.18/6.16 & 62.27/6.88 & 62.36/5.66 \\  & **SVM** & 45.64/14.11 & 44.66/14.77 & 44.83/14.66 & 56.24/13.18 & 60.95/12.14 & 64.48/10.21 \\  & **LR_L1** & 45.32/4.11 & 45.77/3.89 & 45.41/4.86 & 40.68/7.45 & 38.83/8.79 & 36.90/8.82 \\  & **LR_L2** & 45.35/4.04 & 46.03/3.95 & 45.59/4.66 & 40.77/7.53 & 38.84/8.68 & 37.11/8.80 \\  & **LORSAL** & **58.38/3.58** & **55.21/3.55** & **55.42/3.46** & **60.31/5.39** & **64.63/6.22** & **71.63/4.94** \\ \hline \multirow{8}{*}{**RASM**} & **NB** & **51.17/6.55** & 51.61/7.37 & 51.02/6.98 & **54.90/8.57** & **58.08/9.47** & **55.65/6.77** \\  & **SVM** & 37.41/15.45 & 37.23/14.33 & 37.35/13.87 & 40.56/16.24 & 47.29/17.02 & 48.17/17.72 \\ \cline{1-1}  & **LR_L1** & 49.39/6.07 & 48.19/5.55 & 48.17/6.24 & 45.29/6.11 & 42.39/4.44 & 42.71/6.64 \\ \cline{1-1}  & **LR_L2** & 49.45/4.75 & 47.92/4.30 & 47.95/4.73 & 45.44/5.87 & 42.53/6.97 & 42.69/6.37 \\ \cline{1-1}  & **LORSAL** & 49.31/8.79 & **51.68/8.71** & **51.68/8.98** & 52.55/10.36 & 57.38/9.69 & 51.67/7.53 \\ \hline \multirow{8}{*}{**DCAU**} & **NB** & 53.98/6.22 & 52.97/6.73 & 53.72/6.03 & **59.61/5.70** & 61.97/6.52 & 61.95/5.52 \\ \cline{1-1}  & **SVM** & 43.40/13.46 & 41.11/13.93 & 43.91/14.39 & 55.47/13.83 & 59.57/12.67 & 63.48/9.93 \\ \cline{1-1}  & **LR_L1** & 45.94/4.24 & 46.26/4.11 & 45.82/4.41 & 41.38/7.32 & 39.19/8.29 & 37.41/8.21 \\ \cline{1-1}  & **LR_L2** & 46.03/4.23 & 46.45/4.11 & 45.74/4.38 & 41.33/7.24 & 39.21/8.35 & 37.45/8.22 \\ \cline{1-1}  & **LORSAL** & **57.01/3.38** & **54.68/3.59** & **55.20/3.55** & 59.38/5.61 & **63.54/6.21** & **69.89/4.89** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The mean precisions and standard deviations (%) of the classification of LV/HV emotions obtained by the compared classifiers for different features extracted from different frequency bands. of the compared LR_L1, and LR_L2 methods in the arousal dimension. The incorporated Gaussian kernel and Laplacian prior improved the distinguishing ability of LORSAL in emotion recognition task based on EEG signals. For a more comprehensive comparison of the NB, SVM, and LORSAL approaches, Table 3 tabulated the average values and standard deviations of precision, recall, and F1, for the binary emotion classification problem of LV/HV and LA/HA, respectively, when different features were extracted from the total frequency bands. The introduced LORSAL method obtained the best precisions (77.17%/6.37% for LV/HV, and 77.03%/6.20% for LA/HA), the best recalls (76.79%/6.21% for LV/HV, and 76.15%/6.14% for LA/HA), and the best F1 values (76.90%/6.27% for LV/HV, and 76.47%/6.14% for LA/HA), for EEG-based emotion recognition.