Emotion Recognition From EEG Signal Focusing on Deep Learning and Shallow Learning Techniques

Recently, electroencephalogram-based emotion recognition has become crucial in enabling the Human-Computer Interaction (HCI) system to more intelligent. Due outstanding applications of recognition, e.g., person-based decision making, mind-machine interfacing, cognitive interaction, affect detection, feeling etc., successful attracting recent hype AI-empowered research. Therefore, numerous studies have been conducted driven by a range approaches, which demand systematic review methodologies used for this task with their feature sets and techniques. It will facilitate beginners as guidance towards composing an effective system. In article, we rigorous on state-of-the-art systems, published literature, summarized some common steps relevant definitions, theories, analyses provide key knowledge develop proper framework. Moreover, included here were dichotomized based two categories: i) deep learning-based, ii) shallow machine learning-based systems. The reviewed systems compared methods, classifier, number classified emotions, accuracy, dataset used. An informative comparison, research trends, recommendations are also provided future directions.