This paper proposes a scheme to utilize the unaltered direct outcome of brain’s activity viz. EEG signals for emotion detection that is a prerequisite for the development of an emotionally intelligent system. The aim of this work is to classify the emotional states experimentally elicited in different subjects, by extracting their features for the alpha, beta, and theta frequency bands of the acquired EEG data using PSD, EMD, wavelet transforms, statistical parameters, and Hjorth parameters and then classifying the same using LSVM, LDA, and kNN as classifiers for the purpose of categorizing the elicited emotions into the emotional states of neutral, happy, sad, and disgust. The experimental results being a comparative analysis of the different classifier performances equip us with the best accurate means of emotion recognition from the EEG signals. For all the eight subjects, neutral emotional state is classified with an average classification accuracy of 81.65%, highest among the other three emotions. The negative emotions including sad and disgust have better average classification accuracy of 76.20 and 74.96% as opposed to the positive emotion i.e., happy emotional state, the average classification accuracy of which turns out to be 73.42%.