# Emotion Recognition: An Evaluation of ERP Features Acquired from Frontal EEG Electrodes
## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Error** & **Sensitivity** & **Specificity** & **Precision** & **Negative** & **F1 Score** \\
**Analysis** & **(\%)** & **(\%)** & **(\%)** & **Predictive** & **Value (\%)** & **(\%)** \\ \hline LVHA & 88.0 & 89.3 & 73.3 & 95.7 & 80.0 \\ HVHA & 92.0 & 93.3 & 82.1 & 97.2 & 86.8 \\ HVLA & 84.0 & 96.0 & 87.5 & 94.7 & 85.7 \\ LVLA & 68.0 & 98.7 & 94.4 & 90.2 & 79.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The error analysis for results obtained using three classifiers. We determined the effect of including Fp1 and Fp2 in the set of selected electrodes when classifying emotions along arousal and valence domains. Analysis was performed without including ERP data from Fp1 and Fp2 electrodes. It can be clearly seen from Tables 6 and 7 that the four-class emotion classification accuracy reduced. A four-class classification accuracy of 75% has been obtained, which is considerably lower than 83% obtained with Fp1 and Fp2 data included in the classification. The classification results are shown in Table 6 and error analysis in Table 7. Here, two classifiers have been used in the emotion classification, as shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix**} & \multicolumn{5}{c}{**Predicted Classes**} \\ \cline{2-6}  & **LVHA** & **HVHA** & **HVLA** & **LVLA** \\ \hline \multirow{4}{*}{Actual Classes} & LVHA & 15 & 3 & 0 & 2 \\ \cline{2-6}  & HVHA & 1 & 17 & 2 & 0 \\ \cline{2-6}  & HVLA & 1 & 3 & 14 & 2 \\ \cline{2-6}  & LVLA & 0 & 4 & 2 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The confusion matrix on frontal electrodes except Fp1 and Fp2 using average ERPs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Error Analysis**} & **Sensitivity** & **Specificity** & **Precision** & **Negative Predictive** & **F1 Score** \\ \cline{2-6}  & **(\%)** & **(\%)** & **(\%)** & **Value (\%)** & **(\%)** \\ \hline LVHA & 75.0 & 96.7 & 88.2 & 92.1 & 81.1 \\ HVHA & 85.0 & 83.3 & 63.0 & 94.3 & 72.3 \\ HVLA & 70.0 & 93.3 & 77.8 & 90.3 & 73.7 \\ LVLA & 70.0 & 93.3 & 77.8 & 90.3 & 73.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The error analysis of results on frontal electrodes except Fp1 and Fp2. Figure 7: A two-classifier methodology used. Though definitely higher classification results have been obtained using three emotion classifiers, it is not recommended to use the methodology of [22], as it disobeys the orthogonality principle. Additionally, to find a different order for the SVM polynomial classifier for classifying low arousal/high arousal data along the valence domain is not correct as the attributes used for the classification of valence would remain the same for the data whether belonging to low arousal or high arousal. That is why the four-class emotion classification has also been performed using two emotion classifiers independently along arousal and valence. The SVM polynomial order has been fixed at 3. The emotion classification methodology as shown in Figure 7 has been performed separately on average as well as the difference of average ERP attributes acquired from frontal electrodes of EEG. With average ERP attributes and the SVM polynomial order fixed at 3, the emotion classification along the arousal axis is 79% and along the valence axis is 81%. A four-class classification accuracy thus comes out to be 67%. The results are shown in Table 8 and error analysis in Table 9.  
With the SVM polynomial order fixed at 3 for classifying emotions along the arousal axis and valence domain, a four-class classification accuracy of 74% has been obtained with an arousal classification of 86% and a valence classification of 85%. The results are shown in Tables 10 and 11.