Brain signal analysis for human emotion recognition plays important role in psychology, management and human machine interface design. Electroencephalogram (EEG) is the reflection of brain activity – by studying and analysing these signals we are able to perceive emotional state changes. In order to do so, it is necessary to select the appropriate EEG channels that are placed mostly on the frontal part of the head. In this paper we used video stimuli to induce happy and sad mood of 20 participants. To classify the emotions experienced by the volunteers we used five different classification methods to obtain optimal result taking into account all features that were extracted from signals. We observed that the Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) obtained the highest accuracy of emotion recognition.