A Learnable Entropy-Power Scaling Transform with a Radial Basis Function Network for Electroencephalography-Based Familiar and Unfamiliar Face Classification

Familiar and unfamiliar face recognition is an important cognitive process with potential applications in brain-computer interface (BCI) systems and neurocognitive assessment. Classification of familiar and unfamiliar faces based on electroencephalography (EEG) remains challenging because discriminative information is distributed across multiple frequency bands, temporal windows, and scalp channels. This study proposes LEPST-RBFNet, which combines a Learnable Entropy-Power Scaling Transform (LEPST) and a radial basis function (RBF) network for EEG-based familiar and unfamiliar face classification. The model first segments the EEG into multi-scale time-frequency segments and then extracts local standard-deviation features. After that, a learnable entropy-power-inspired scaling transformation is applied using the LEPST module to obtain adaptive local time-frequency EEG entropy features. The transformed features are classified by an RBF prototype module with learnable centers and an adaptive kernel-width parameter. Experiments using a five-fold leave-one-block-out validation protocol show that LEPST-RBFNet achieves a superior average classification accuracy of 73.60%. Ablation and visualization analyses further indicate that the proposed model provides a competitive and interpretable framework for EEG-based familiar and unfamiliar face recognition.