Dynamical recursive feature elimination technique for neurophysiological signal-based emotion recognition

The machine learning-based classification model can predict the operator emotional states in human-machine system based on nonlinear, multidimensional neurophysiological features. However, the dynamical properties of the testing physiological data regarding the time series may influence the feature distribution variation and inter-class discrimination across different time steps. To overcome this shortcoming, we propose a novel EEG feature selection method, dynamical recursive feature elimination (D-RFE), to find the optimal but different feature rankings at each time instant for arousal and valence recognition. With the classification framework implemented via a model-selected least square support vector machine, the participant-specific classification performance has been significantly improved against conventional RFE model and several common classifiers. The optimal classification accuracy and F1-score elicited by the proposed method are 0.7896, 0.7991, 0.7143, and 0.7257 for arousal and valence dimensions, respectively, which are quite competitive among recent reported works on the same EEG database.