## 4 Experiment and Results
### Exp. #1: Comparison of Bispectrum-Based Feature Extraction Modes  
The following experiments in Exp. #1 were carried out to compare and select the best feature extraction mode from the bispectrum value. There were four types of features to consider: the entropy and moment (NBE, NBSE, MMOB, FOSM and SOSM as in [20]) with additional standard deviation (STD) value as Mode #1, the percentage value of Mode #1 as Mode #2, the mean as Mode #3 and the percentage of the mean as Mode #4. A 5 \(\times\) 5 3D pyramid filter was used in this experiment, producing 480 dimensions of feature vectors for Mode #3 and Mode #4. The dimensions of feature vectors for Mode #1 and Mode #2 are 2880 because there are six features from each area of the filters (480 \(\times\) 6 = 2880). To reduce the large dimension, each feature vector was dimensionally reduced using PCA with 99% Eigen values. The comparison result of the feature extraction type Mode #1-Mode #4 is presented in Table 1.  
Table 1 shows that the mean percentage feature (Mode #4) provides the highest recognition rate (74.22%) for the arousal and 77.58% for the valence. From the recognition rate in Table 1, it can be seen that for both arousal and valence emotion, Mode #1 and Mode #4 gave the best recognition rate compared to Mode #2 and Mode #3, with Mode #4 providing a slightly better recognition rate than Mode #1. To investigate whether any of the feature types is significantly different, one-way ANOVA was used to compare the recognition rate of four feature type (Mode #1-Mode #4), and the resultant \(p\)-values of both separate and combined arousal and valence were all \(<\)10\({}^{-4}\). This result suggests [PERSON]'s HSD test to see which of the feature types is significantly different.  
Table 2 presents the Tukey HSD test result for pair-wise comparisons. For arousal emotion, Mode #1 is statistically different from Mode #4 with the \(p\)-value = 0.0114 (\(p<\) 0.05); while for valence emotion, Mode #1 did not have significant difference with Mode #4 with the \(p\)-value = 0.9716 (\(p>\) 0.05). Moreover, Mode #4 also has shortcomings due to the more complicated feature calculation compared to Mode #1. From this result, we can conclude that for the arousal emotion, the mean percentage of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Feature Type**} & \multirow{2}{*}{**Features**} & \multicolumn{2}{c}{**Maximum Recognition Rate 1**} & \multicolumn{2}{c}{**Mean \(\pm\) SD Recognition Rate**} \\ \cline{3-6}  & & **Arousal (\%)** & **Valence (\%)** & **Arousal (\%)** & **Valence (\%)** \\ \hline Mode \#1 & NBE, NBSE, MMOB, FOSM, SOSM [20], STD & 73.36 & 74.77 & 64.88 \(\pm\) 5.32 & 69.36 \(\pm\) 5.98 \\ Mode \#2 & NBE\%, NBSE\%, MMOB\%, FOSM\%, SOSM\%, STD\% & 55.24 & 56.72 & 54.70 \(\pm\) 0.41 & 56.23 \(\pm\) 0.79 \\ Mode \#3 & Mean & 67.27 & 71.80 & 56.11 \(\pm\) 1.97 & 54.91 \(\pm\) 0.96 \\ Mode \#4 & Mean\% & 74.22 & 77.58 & 72.83 \(\pm\) 4.04 & 70.95 \(\pm\) 9.86 \\ \hline \end{tabular} 1  
\end{table}
Table 1: Recognition rate comparison of various feature type. MMOB, mean-magnitude of bispectrum; FOSM, first order spectral moment; SOSM, second order spectral moment; STD, standard deviation.  
the bispectrum (Mode #4) is a better feature type than entropy and moment (Mode #4), while for the valence emotion, both Mode #1 and Mode #4 are similar. However, it can be noted that both Mode #1 and Mode #4 are significantly different with Mode #2 and Mode #3 (\(p<0.05\)). This experiment also confirmed our hypothesis that the mean percentage of the bispectrum (Mode #4) provides more accurate information than the absolute mean value (Mode #3). This finding implies that for the same emotion, the relative/percentage bispectrum values from the EEG signals may have similarities among people, although different people may have different amplitude in the bispectrum values.