# Emotion Recognition: An Evaluation of ERP Features Acquired from Frontal EEG Electrodes
## 2 Recent Studies
From the classification results, it was found that arousal was better classified with EEG features, valence with peripheral and like/dislike with video features. It was easier to classify valence as compared to arousal. Some recent studies conducted on DEAP data are by [PERSON] et al. [33], [PERSON] et al. [34] and [PERSON] et al. [35]. In [33], the DEAP EEG database was used to classify emotions into four classes (HVHA, HVLA, LVLA and LVHA). The study was also subject-dependent and high results on the data were reported only at the cost of computational complexity for acquiring attributes and time inefficient algorithms [34]. [PERSON] et al. [35] used three types of emotion classifiers on the attributes acquired from the DEAP EEG database to classify emotions into two classes along arousal and valence domains. [PERSON] and [PERSON] [36] performed the affective tagging of multimedia by using a multimodal approach. For emotion classification, the EEG signals and video data acquired in the MAHNOB HCI interface were used [37]. The authors added Fp1 and Fp2 electrodes in the described set of electrodes for the classification of arousal, valence and control and used the Recursive Feature Elimination (RFE) technique and Independent Component Analysis (ICA) technique for feature selection. However, some of the conclusions related to this study were countered in another study [38]. [PERSON] et al. [25] tested features collected as per the 33 previous studies on EEG-based emotion recognition and found that the features such as HOC [23], fractal dimension and Hjorth parameters and features obtained from frequency bands \(\beta\) and \(\gamma\) gave better emotion classification accuracies [29; 39]. Considering that the ERP extraction requires the averaging of multiple numbers of EEG signals, the ERP feature was not considered in this subject-dependent study [25]. It was found that classifying emotions on the basis of attributes determined from facial expressions produced higher accuracy results as compared to the classification of emotions based on EEG attributes [38]. These results are in contrast to the study which showed better emotion classification results with EEG as compared to facial expressions [36]. Another important observation was that the facial expressions on account of the emotional stimulus affect the emotional sensitive features of EEG, and a strong correlation was found between the features obtained from facial expressions and the features from EEG used in the detection of valence [38]. [PERSON] et al. [40] used the SEED dataset for developing a subject-dependent emotion classifier. The classification of emotions was carried out into three classes, namely, neutral, positive and negative, using Convolutional Neural Networks (CNN). These studies are too computationally intensive and less depictive about subject-dependent emotion classification studies, making it very difficult to validate [33; 34; 40]. [PERSON] and [PERSON] [41] used average ERP attributes obtained from central electrodes Fz, Cz and Pz and followed the same preprocessing and feature extraction methods as in [22]. The subject-independent emotion classification accuracy lied between 75 and 76.8% using a Support Vector Machine (SVM) polynomial classifier on 14 male subjects. A review of some major studies performed recently is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**S.No**} & \begin{tabular}{c} **Reference Number**, \\ **Stimulus, Number** \\ **of Subjects** \\ \end{tabular}