Abstract
Recognition of human emotion states for affective computing based on Electroencephalogram (EEG) signal is an active yet challenging domain of research. In this study we propose an emotion recognition framework based on 2-dimensional valence-arousal model to classify High Arousal-Positive Valence (Happy) and Low Arousal-Negative Valence (Sad) emotions. In total 34 features from time, frequency, statistical and nonlinear domain are studied for their efficacy using Artificial Neural Network (ANN). The EEG signals from various electrodes in different scalp regions viz., frontal, parietal, temporal, occipital are studied for performance. It is found that ANN trained using features extracted from the frontal region has outperformed that of all other regions with an accuracy of 93.25%. The results indicate that the use of smaller set of electrodes for emotion recognition that can simplify the acquisition and processing of EEG data. The developed system can aid immensely to the physicians in their clinical practice involving emotional states, continuous monitoring, and development of wearable sensors for emotion recognition.