Affective recognition from EMG signal: An approach based on correlation analysis and adaptive Tabu search

A novel feature selection method was proposed for electromyography (EMG)-based affective recognition. First of all, correlation analysis was used to reduce the dimension of original feature subset; then adaptive Tabu search algorithm combined with intensification and diversification strategies was adopted for feature selection, and mutation operator of genetic algorithm (GA) was implemented as the diversification strategy. The experimental results show that the method we proposed can achieve high recognition rates with low feature dimensions, and obtain stable and effective features for the establishment of affective recognition system.