This study investigates the discrimination between calm, exciting positive and exciting negative emotional states using EEG signals. Towards this direction, a publicly available dataset from eNTERFACE Workshop 2006 was used having as stimuli emotionally evocative images. At first, EEG features were extracted based on literature review. Then, a computational framework is proposed using machine learning techniques, performing feature selection and classification into two at a time emotional states. The procedure described in this paper investigates and assess the effectiveness of selection and classification techniques providing improved classification accuracy. The proposed methodology is able to obtain high accuracy of 75.12% in classifying the two emotional states comparing with similar studies using the same dataset.