Towards a universal and privacy preserving EEG-based authentication system

EEG-based authentication has gained much interest in recent years. However, despite its growing appeal, there are still various challenges to their practical use, such as lack of universality, lack of privacy-preserving, and lack of ease of use. In this paper, we have tried to provide a model for EEG-based authentication by focusing on these three challenges. The proposed method, employing deep learning methods, can capture the fingerprint of the users' EEG signals for authentication aim. It is capable of verifying any claimed identity just by having a genuine EEG fingerprint and taking a new EEG sample of the user who has claimed the identity, even those who were not observed during the training. The role of the fingerprint function is similar to the hash functions in password-based authentication and it helps preserve the user's privacy by storing the fingerprint, rather than the raw EEG signals. Moreover, for targeting the lack of ease of use challenge, Gram-Schmidt orthogonalization process reduces the required number of channels to just three ones. The experiments show that the proposed method can reach around 98% accuracy in the authentication of completely new users with only three channels of Oz, T7, and Cz.

Automated summary

This paper proposes an EEG-based authentication method that captures the fingerprint of users' EEG signals using deep learning methods. The method can verify claimed identities by comparing genuine EEG fingerprints with new EEG samples, even for users not observed during training. It preserves privacy by storing fingerprints instead of raw EEG signals, and improves ease of use through the Gram-Schmidt orthogonalization process.