Emotion plays a pivotal role in human-computer interaction, making accurate recognition and effective induction of emotions crucial for developing systems that can understand and respond to human emotions. This paper surveys 31 existing papers in the literature, focusing on emotion induction techniques, data collection types, emotion models, and machine learning methods employed in emotion recognition. According to evaluations, researchers commonly rely on visual stimuli and dimensional emotion models. EEG signals enjoy considerable popularity among various modalities, and the prevalent trend in machine learning approaches involves the use of Support Vector Machines (SVM). This paper aims to contribute to the field by analyzing the recent trends in emotion recognition and induction and be a guide for future research.