In recent times, we have seen extensive research in the field of EEG-based emotion identification. The majority of solutions suggested by current literature use sophisticated deep learning techniques for the identification of human emotions. These models are very complex and need huge resources to implement. Hence, in this work, a method for human emotion recognition is proposed which is based on much simpler architecture. For that, two publicly available datasets SEED and DEAP are used to perform experiments. First, the EEG signals of the two datasets are segmented into epochs of 1second duration. The epochs are also decomposed into different brain rhythms. The features computation is performed in two different ways, one is directly from the epochs and the other way is from the brain rhythms obtained after the decomposition of the epochs. Several features and their combination are examined with different classifiers. For the DEAP dataset baseline features are also utilised. It is observed that the support vector machine (SVM) has shown the best performance for the DEAP dataset when baseline feature correction and epoch decomposition are implemented together. The best achieved average accuracy is 96.50% and 96.71% for high versus low valence classes and high versus low arousal classes, respectively. For the SEED dataset, the best average accuracy of 86.89% is achieved using the multilayer perceptron (MLP) with 2 hidden layers.Clinical relevance- This work can be further explored to develop an automated mental health monitor which can assist doctors in their primary screening.