# Emotion Recognition: An Evaluation of ERP Features Acquired from Frontal EEG Electrodes
## 6 Results and Discussion  
The analysis has been performed on two types of attributes, i.e., average ERPs with latencies and the difference of average ERPs and latencies, as shown in Tables 2 and 3. In order to make a comparison and for the validation of results, the methodology as used in an existing study for emotion evocation and classification of low arousal and high arousal data into low valence and high valence classes has been used [22]. The flowchart of the three-classifier methodology is shown in Figure 5.  
When using average ERP attributes, the emotions along the arousal domain could be classified with an accuracy of 88% at SVM polynomial order 6. After classifying arousal, two valence classifiers, one classifying low arousal emotional data into low valence low arousal (LVLA) and high valence low arousal (HVLA) classes and the second classifying high arousal emotional data into low valence high arousal (LVHA) and high valence high arousal (HVHA) classes, have been used. In both cases, a classification accuracy of 94% has been obtained at SVM polynomial order 4 and SVM polynomial order 5, respectively. The confusion matrix showing four class classification results is shown in Table 4, whereas the error analysis is shown in Table 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix on Frontal**} & \multicolumn{5}{c}{**Predicted Classes**} \\ \cline{2-6}  & **LVHA** & **HVHA** & **HVLA** & **LVLA** \\ \hline \multirow{4}{*}{Actual Classes} & LVHA & 22 & 2 & 0 & 1 \\ \cline{2-6}  & HVHA & 1 & 23 & 1 & 0 \\ \cline{1-1} \cline{2-6}  & HVLA & 1 & 3 & 21 & 0 \\ \cline{1-1} \cline{2-6}  & LVLA & 6 & 0 & 2 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The confusion matrix using average ERPs with one arousal and two valence classifiers. Figure 5: A flowchart of the three-classifier methodology used for classification of emotions. The results, of course, are better than an existing study [22]. It is noteworthy to mention over here that we did not use ICA and artifact removal techniques in accordance with the conclusion of [PERSON] et al. [25], as these operations did not reportedly impact the classification results considerably but added to offline processing time. However, it must be known that the classification accuracy depends upon the index of trials wrongly classified along arousal and valence domains. If different test samples have been wrongly classified along arousal and valence, the four-class classification may decrease even though the arousal and valence classification may be higher. Thus, if arousal classification is 88% and valence is 94%, then the correctly classified instances (CCI) would lie in the range of 82-88. This means that the four-class classification lies in the range of 82-88% with a mid-range accuracy of 85%, which is better than 75% obtained in an existing study [22]. Further, if arousal and valence classification is conducted considering the orthogonal relationship between arousal and valence domains, then separate classifiers should be used to classify arousal and valence. Therefore, either classification should be performed using two separate arousal and valence classifiers or all four classes should be classified simultaneously. It is important to mention here that when classifying low arousal and high arousal data along the valence domain, the same attribute set as was used when classifying emotions using two classifiers suited the best, though at different orders. In other words, the attribute set that gives the best accuracy for the classification of low arousal data into valence obviously gives the best classification accuracy for the classification of high arousal data along the valence axis. The comparison of results obtained on frontal electrodes with an existing study on central electrodes using average ERP attributes is shown in Figure 6.  
Figure 6: Comparison of existing results (**a**) [22], and (**b**) results obtained on frontal electrodes.