Discriminating Familiarity In Face Perception Through Eeg And Erp Analysis With Confident Learning Techniques

M Subathra

Abstract


Despite the time-consuming nature of data gathering, EEG is an essential tool for researching human perception. FUFP, the largest publicly available EEG dataset for familiar versus unfamiliar face perception, is presented in this paper. It includes 6,400 samples from 8 participants across 66 channels. Five basic machine learning models demonstrate that familiarity categorization is feasible. Event-related potential (ERP) research supports this claim by showing higher N400 components for unfamiliar faces. A deep learning strategy is proposed to enhance feature emphasis and data quality by combining ERP insights with confident learning (ECL). This approach outperforms current models, and FUFP is recommended for further research.Keywords: EEG; face perception; familiarity classification; neurobiological signal processing

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