Electroencephalogram-Based Familiar and Unfamiliar Face Perception Classification Underlying Event-Related Potential Analysis and Confident Learning

Electroencephalogram (EEG), as a kind of neurobiological signal, is an essential tool for studying human perception, yet its acquisition is often time-consuming and laborious. Accordingly, this paper presents the largest publicly available EEG dataset to date for familiar and unfamiliar face perception analysis (FUFP). The EEG signals of 66 channels were recorded from 8 participants, each exposed to 8 familiar faces (FFs) and 32 unfamiliar faces (UFs) randomly, repeated 20 times, yielding 6400 samples. Inspired by the inherent slight symmetry exhibited by the 2D position of EEG electrodes and EEG data, we employed five baseline machine learning methods, proving the feasibility of classifying familiarity through EEG. There are indeed neural features related to face familiarity in EEG signals. Event-related potential (ERP) analysis towards FFs and UF responses reveals that UFs induce larger N400 component amplitudes than FFs. Therefore, we propose a deep learning method based on ERP analysis and confident learning (ECL) for familiarity classification, which can effectively focus the model’s attention on more discriminative features and clean the data. Experimental results show that our model’s accuracy outperforms other existing familiarity classification models. We encourage researchers to utilize FUFP for algorithm tests and face perception analysis.