Abstract
This paper proposes DPPN‐Entropy, a dual‐path prototypical pretraining network (DPPN) for few‐shot EEG–based person identification using precomputed multiscale entropy features. Unlike existing approaches that rely on raw EEG signals, the proposed system operates on compact 180‐dimensional entropy feature vectors (30 channels × 6 entropy types), enabling efficient template storage and fast matching in real‐world biometric enrollment with limited data. The architecture introduces two key innovations: (1) a DPPN that leverages supervised contrastive learning and mutual distillation between parallel encoder branches to learn robust discriminative embeddings from scarce training data; and (2) a global–local prototype fusion (GLPF) module that adaptively combines class‐level global prototypes with sample‐level local prototypes via variance‐based weighting, with a fallback to global‐only prototypes when
K
= 1 to prevent variance‐collapse failure. Under a strict subject‐independent evaluation protocol with 16 subjects for meta‐training and 10 unseen subjects for meta‐testing, DPPN achieves 86.6% accuracy in the 5‐way 5‐shot setting, significantly outperforming ProtoNet (85.4%) and traditional classifiers (logistic regression 95.1%). Comprehensive downstream analyses—including channel selection, cognitive state comparison (eyes‐open (EO) significantly higher than eyes‐closed (EC)), N‐way scalability, and biometric metrics (EER 21.2%, AUC 0.843)—establish practical benchmarks for few‐shot EEG person identification. Although the framework is termed MPAN‐entropy, ablation studies identify DPPN as the primary driver of performance; we thus adopt DPPN for all downstream analyses.