Recently, electroencephalogram-based emotion detection has become crucial to make the Human Computer Interaction (HCI) system smarter. Due to the outstanding applications of emotion detection, e.g., human decision making, brain-computer interfaces, cognitive interactions, affect detection etc, emotion detection has successfully attracted the attention of AI-assisted research recently. Therefore, numerous studies have been carried outthrough a variety of approaches that require a systematic review of the methods used for this task, with their characteristics and techniques. It will serve as a guide for beginners to create an effective emotion recognition system. In this article, we have conducted a thorough review of the latest emotion detection systems published in recent literature and summarized some of the common steps in emotion detection with relevant definitions, theory, and analysis to provide the key insights to develop an appropriate framework . Furthermore, the studies included here were dichotomized based on two categories: i) emotion recognition systems based on deep learning and ii) based on shallow machine learning. The reviewed systems were compared based on methods, classifier, number of emotions classified, accuracy, and data set used. An informative comparison, current research trends, and some recommendations for future research are also provided.