## 4 Experiment and Results
### Exp. #5: Comparison of BPNN and PNN Classifiers  
To find the best classifier for the automatic emotion recognition system, two types of ANN classifiers were compared, the BPNN and the PNN. The drawback of the BPNN was the computing  
Figure 5: Recognition rate for different amounts of data training samples. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Emotion Type** & **F1 (\%)** & **F2 (\%)** & **F3 (\%)** & **F4 (\%)** & **F5 (\%)** & **F6 (\%)** & **F7 (\%)** & **F8 (\%)** & **F9 (\%)** & **F10 (\%)** & **Mean (\%)** \\ \hline Arousal & 92.66 & 92.50 & 92.42 & 93.13 & 93.98 & 93.44 & 92.50 & 93.44 & 92.66 & 92.11 & 92.92 \\ Valence & 93.44 & 93.13 & 93.59 & 93.52 & 93.83 & 93.20 & 93.59 & 93.88 & 93.44 & 93.28 & 93.51 \\ Arousal (LSO) & 54.69 & 51.56 & 50.00 & 57.03 & 57.03 & 53.13 & 55.47 & 51.56 & 54.69 & 47.66 & 53.28 \\ Valence (LSO) & 55.47 & 59.38 & 55.47 & 57.03 & 51.56 & 60.16 & 60.16 & 58.89 & 55.47 & 58.59 & 57.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Result of the 10-fold cross-validation recognition rate for mean percentage feature type from 8 EEG channels with 5 \(\times\) 5 non-overlapped filters. time to conduct the training phase for the classifier; thus, the PNN, which is generally faster than the BPNN, was used in this experiment. The BPNN setting parameter was similar to that of the previous experiments (Exp. #1-Exp. #4), whereas for the PNN, it is difficult to find a good choice of smoothing parameter (\(\sigma\)), because in the original PNN classifier, there is no mathematical calculation to determine the best smoothing parameter. In this experiment, the PNN smoothing parameter (\(\sigma\)) was chosen randomly, and the trial was repeated 60 times to find the highest recognition rate. In this experiment, we used only eight channels from the EEG signals, as the previous experiment showed that using eight channels would be sufficient. The PNN recognition result for various randomly-generated smoothing parameters (\(\sigma\)) can be seen in Figure 6.  
From the randomly-generated smoothing parameter (\(\sigma\)), we could find the optimum PNN recognition rate, which is 76.09% when \(\sigma\) = 0.4505 for arousal and 75.31% when \(\sigma\) = 0.3517 for valence, as depicted in Figure 6. The comparison of the recognition rate and the computing time between the PNN and the BPNN is presented in Table 6.  
From Table 6, we can see that the recognition rate from both the BPNN and the PNN for arousal and valence emotion was comparable, with both values hovering around 75%, with the \(p\)-value of 0.761 (\(p>0.05\)). However, the specificity of PNN is lower than BPNN, and the sensitivity was higher than BPNN, meaning that PNN detected more positive values (the higher emotion level) compared to BPNN, but PNN has a lower ability to detect the negative values (the lower emotion level) compared to BPNN. As for the positive predictive value (PPV) value, it can be seen that both classifiers are comparable with a slightly higher value of PPV from BPNN. If PPV is used as the \"gold standard\" as mentioned by [PERSON] [51], then BPNN is superior to PNN in classifying emotion from EEG signals. As depicted in the receiver operating characteristics (ROC) graph in Figure 7, the BPNN is more \"conservative\" than the PNN because for both emotion, i.e., the arousal and the valence, the BPNN position is on the left side of the PNN. From this ROC analysis, it can be stated that BPNN makes positive classifications with strong evidence, so it makes few false positive errors. According to [PERSON] [52], in a real-word classification problem, there are more negative instance than the positive