Abstract
Emotion classification plays a critical role in the development of human-computer interaction. EEG (Electroencephalogram) signal is an important information source, from which different features have been extracted for the study of emotion states. However, there is a large amount of redundant and irrelevant information in EEG signals, which directly interferes with emotion classification. Aiming at selecting EEG emotional features precisely and efficiently, this paper proposed a feature selection framework, termed MIGA (Mutual Information and Genetic Algorithm). It combined mutual information and genetic algorithm to improve the quality of feature subset based on three perspectives, that is correlation, contribution and synergistic effect between features. A new emotional feature IQI (Intensity Quantity of Information) was also designed in this paper. IQI is able to mine the intensity information of EEG signals, and enlarge the samples’ distance as a result. Experiments were carried on DEAP (A Database for Emotion Analysis using Physiological Signals). Results show that the classification accuracy of IQI is 5% higher than that of the traditional frequency domain feature, and MIGA reduces feature amount by 2/3 while ensuring classification accuracy. For DEAP, classification accuracy of MIGA in valence and arousal reached 88.55% and 88.14% respectively. It is indicated that, compared with existing methods, MIGA improves emotion recognition with much fewer features from EEG.