A Universal Framework For Disentangling Subject-Specific Signatures In Eeg Signals
Pei, Z.
Show 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.
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