Emotion detection from EEG recordings

Human brain behavior is very complex and it is difficult to interpret. Human emotion might come from brain activities. However, the relationship between human emotion and brain activities is far from clear. In recent years, more and more researchers are trying to discover this relationship by recording brain signals such as electroencephalogram (EEG) signals with the associated emotion information extracted from other modalities such as facial expression. In this paper, machine learning based methods are used to model this relationship in the publicly available dataset DEAP (Database for Emotional Analysis using Physiological Signals). Different features are extracted from raw EEG recordings. Then Maximum Relevance Minimum Redundancy (mRMR) was used for feature selection. These features are fed into machine learning methods to build the prediction models to extract the emotion information from EEG signals. The models are evaluated on this dataset and satisfactory results are achieved.