IMGWOFS: A Feature Selector With Trade-Off Between Conflict Objectives for EEG-Based Emotion Recognition

Abstract

Feature selection is a crucial step in EEG emotion recognition. However, it was often used as a single objective problem to either reduce the number of features or maximize classification accuracy, while neglecting their balance. To address the issue, we proposed Improved Multi-objective Grey Wolf Optimization Feature Selection (IMGWOFS). First, we designed a population initialization operator via discriminability and independence of features to accelerate search speed. Second, we employed a two-stage update strategy to improve the global search capabilities of the EEG feature subsets. Finally, we incorporated an adaptive mutation operator to escape the local optima. We conducted experiments on SEED and DEAP datasets, and the accuracy were 86.87 $\pm$ 1.62 % and 60.65 $\pm$ 1.51 % in the beta band using a smaller number of EEG features. In addition, the frontal lobe was related to emotion processing. In conclusion, IMGWOFS is an effective and feasible feature selection method for EEG-based emotion recognition.