EEG Feature Selection in Emotion Recognition Using a Fuzzy Information-Theoretic Based Optimization Approach

For electroencephalogram (EEG)-based emotion recognition, various EEG features are extracted from frequency, time, and time-frequency domains for modeling. Nevertheless, there is no a standard subset of EEG features widely accepted in this research field, giving rise to the challenge of curse dimensionality. To cope with the challenge, many EEG feature selection (FS) methods have been put forward based on information theory. Generally, these methods suffer from the issue of delivering a suboptimal result with heuristic search, and they are also inefficient in balancing the influence of different terms like feature relevance and feature redundancy. Based on this, we present a new fuzzy information-theoretic based optimization approach to attain the goal. To be specific, fuzzy mutual information is unitized to evaluate EEG features from the relevance and redundancy perspective, and data structure information is captured to exploit feature manifold simultaneously. Then, a unified optimization framework is designed to take all of them into consideration, thereby inducing a globally optimal result of EEG FS. Extensive empirical studies on three EEG emotional datasets reveal that our method is able to found out a discriminative and nonredundant feature subset from different domains, and therefore achieves the performance improvement of emotion recognition.