Automatic detection for human-machine interfaces of the emotional states of the people is one of the difficult tasks. EEG signals that are very difficult to control by the person are also used in emotion recognition tasks. In this study, emotion analysis and classification study were conducted by using EEG signals for different types of stimuli. The combination of the audio and video information has been shown to be more effective about the classification of positive/negative (high/low) emotion by using wavelet transform from EEG signals, and true positive rate of 81.6% was obtained in valence dimension. Information of audio was found to be more effective than the information of video at classification that is made in arousal dimension, and true positive rate of 73.7% was obtained when both stimuli of audio and audio+video are used. Four class classification performance has also been examined in the space of valence-arousal.