EEG-Based Familiar and Unfamiliar Face Classification Using Filter-Bank Differential Entropy Features

The face recognition of familiar and unfamiliar people is an essential part of our daily lives. However, its neural mechanism and relevant electroencephalography (EEG) features are still unclear. In this study, a new EEG-based familiar and unfamiliar faces classification method is proposed. We record the multichannel EEG with three different face-recall paradigms, and these EEG signals are temporally segmented and filtered using a well-designed filter-bank strategy. The filter-bank differential entropy is employed to extract discriminative features. Finally, the support vector machine (SVM) with Gaussian kernels serves as the robust classifier for EEG-based face recognition. In addition, the F-score is employed for feature ranking and selection, which helps to visualize the brain activation in time, frequency, and spatial domains, and contributes to revealing the neural mechanism of face recognition. With feature selection, the highest mean accuracy of 74.10% can be yielded in face-recall paradigms over ten subjects. Meanwhile, the analysis of results indicates that the EEG-based classification performance of face recognition will be significantly affected when subjects lie. The time–frequency topographical maps generated according to feature importance suggest that the delta band in the prefrontal region correlates to the face recognition task, and the brain response pattern varies from person to person. The present work demonstrates the feasibility of developing an efficient and interpretable brain–computer interface for EEG-based face recognition.