# Emotion Recognition: An Evaluation of ERP Features Acquired from Frontal EEG Electrodes
## 2 Recent Studies  
The classification of emotions from the parameters acquired from bodily signals has become possible with the establishment of models describing the changes in physiological signals with the occurrence of an emotion [17; 18; 19]. [PERSON] et al. [20] acquired brain activity using Functional Near-Infrared Spectroscopy (fNIRS), face video, and brain signals using EEG and peripheral signals such as respiration rate, cardiac rate, and GSR with the primary objective of creating a multimodal database. The data were acquired in an eNTERFACE workshop in 2006 and are available online. This database has been used by various researchers for analyzing emotions, wherein it was found that the emotion classification accuracy improved as the number of classes to be classified was reduced [21]. An important study which forms the basis of our work is the study performed by [PERSON] et al. [22]. The researchers classified neurophysiological data into four emotional classes, namely, high-valence high-arousal (HVHA), low-valence high-arousal (LVHA), high-valence low-arousal (HVLA) and low-valence low-arousal (LVLA), using average ERP and event-related oscillation features from the acquired EEG signals. For developing a subject-independent emotion classifier, the average ERP features were acquired from Fz, Cz and Pz electrodes only. The emotions were classified into four classes by first classifying emotions into low arousal and high arousal classes and then using two separate classifiers to classify low arousal and high arousal data along the valence axis. The researchers used three emotion classifiers to classify four classes of emotions. It is a common belief that the number of wrong indices incorrectly classified by each classifier must be specified when more than one classifier is used for classifying more than two classes. Additionally, since high accuracy results (81.3%) were reported in this study, the methodology of their data acquisition and analysis has been used in our study as well for the comparison and validation of results. However, when validating this classifier model, we found that the critical discussion of results is necessary before proposing this accuracy. [PERSON] and [PERSON] [23] used images from the Pictures of Facial Affect database [24] to acquire EEG. The classification of emotions was performed in a one versus all approach using SVM polynomial classifiers and Higher Order Crossing (HOC) features. Therefore, analyzing the data of 16 subjects on a single electrode would mean lower samples for training and testing. [PERSON] et al. [25] validated that emotion classification results are better with HOC features, but the classification accuracies were not as high as reported in [23]. We have clearly described the total number of test samples for each class of emotion in our proposed study, which we found missing in [23]. Further, if we go by the emotional states chosen, happiness could be an emotion that may be a mixture of two or more emotions such as joy and bliss and may thus lie in the first or fourth quadrant in the arousal-valence plane. Thus, we feel an initial classification into the arousal-valence plane can form the basis of classification of emotions further in a particular quadrant. The features determined from EEG signals by decomposing them into different frequency bands have been used by a number of researchers to classify emotions [26; 27; 28; 29; 30; 31]. [PERSON] et al. [25], however, found no major difference among the attributes used in [28]. [PERSON] et al. [32] in their seminal work not only analyzed the single trial EEG signals for the classification of emotions along the arousal and valence axis, but also generated the Database for Emotion Analysis using Physiological Signals (DEAP database) for the classification of emotions. While analyzing EEG signals, the authors found a high correlation between valence and brain signals.