Facial expression awareness based on multi-scale permutation entropy of EEG

Electroencephalogram (EEG) is a comprehensive manifestation of the dynamic activity of human brain neurons and has been proven to have the potential to serve as an effective biomarker for identifying subtle emotion- or cognition-related changes. This paper focuses on facial expression awareness and proposes Multi-scale permutation Entropy (MPE) of EEG data with the aim of finding a convenient and accurate method for identifying different facial expressions. First, the principle and computational procedure of MPE is introduced. Then, MPE analysis of EEG for facial expression awareness is detailed. Finally, computational analysis is conducted. In the first experiment, the influence of the scale factor on the MPE values is investigated in which the entropy value tends to be augmented with an increase in the scale factor when the scale factor is less than five. In the second experiment, the analysis results show that the MPE of the angry expression EEG is higher than that of the happy expression EEG. Furthermore, we analysed the MPE in the form of a boxplot and found that the two expressions of anger and happiness can be distinguished clearly and that MPE can be used to predict angry and happy expressions based on EEG signals.