What Your Brain Activity Says about You: EEG-based Subject Verification Across Sessions

Electroencephalography (EEG) signals contain rich person-specific information that can be used for biometric applications. This raises both opportunities and concerns in areas such as personalized health care and data privacy. This paper presents an approach for EEG based subject verification, where a transformer-based model learns to generate subject specific embeddings to later determine whether two EEG recordings belong to the same person or not. The proposed method generates subject-specific embeddings using pre-trained models and performs subject verification using a distance-based matching strategy with threshold tuning. To assess the models generalization, cross-session and cross-dataset experiments are conducted using two EEG datasets with different recording montages with one being made up of subjects with medical pathologies. Results indicate high performance even when trained and evaluated across different subject groups, and recording configurations, reaching accuracies above 80%. These findings show that identity-related information can be extracted from EEG signals independent of health status or recording protocols, while also highlighting the need for privacy preservation of EEG data to protect sensitive subject-specific information.