Journals Proceedings

Abstract

This paper aims to recognize emotion through EEG. The EEG signals are acquired from the wireless wearable 8- channel Enobio device for the emotions such as smile and anger. The acquired EEG signals are processed to recognize selected emotions. The features like mean, standard deviation, median, entropy, variance, minimum, maximum and skewness have been extracted from sub-signals obtained by multiwavelet decomposition of EEG signals. These features are used as an input to support vector machine (SVM) for classification of human emotions. The proposed method has provided classification accuracy of 87.5% using SVM.