A UNIVERSAL FRAMEWORK FOR DISENTANGLING SUBJECT-SPECIFIC SIGNATURES IN EEG SIGNALS

Abstract

Extracting stable subject-specific features from EEG signals remains challenging due to their entanglement with transient brain states. We propose a universal neural framework that disentangles subject-specific features from state-dependent components in raw EEG signals. Our approach employs a disentanglement module with a cross-reconstruction objective to isolate subject-specific representations. We validate our framework on EEG-based biometric recognition using two public datasets with leave-one-state-out cross-validation. Results demonstrate significant improvements in out-of-distribution identification accuracy across four different backbone models, confirming our methods universality and plug-and-play capability. This work advances reliable extraction of neural signatures for personalized neurotechnology applications.