Classification of emotions based on audio-visual stimulus by EEG signals

Emotions play an important role in communication between humans. Emotions can be reflected through words, voice intonation, facial expression and body language. In contrast, Brain Computer Interface (BCI) systems have not reached the desired level to interpret the people's emotions. BCI systems need new resources that can be taken from humans and processed by these systems to understand emotions. Electroencephalogram (EEG) signals is one of the most important resources to achieve this target. The aim of this study was to classify EEG signals related to different emotions based on audio-visual stimulus. SAM (Self Assessment Manikins) was used to determine participants' emotional states. Participants rated each audio-visual stimulus in terms of the level of valence, arousal, like/dislike and dominance. EEG signals that related to positive and negative emotion states have been classified according to participants' ratings. Discrete wavelet transform (DWT) was used for feature extraction from EEG signals. Wavelet coefficients of EEG signals were assumed as feature vector and statistical features were used to reduce dimension of feature vector. In this study, different clusters consisting of EEG signals related to positive and negative emotions groups have been classified by artificial neural network and k-nearest neighborhood algorithm. The classification algorithms' performances have been compared.