Evidence of Task-Independent Person-Specific Signatures in EEG using Subspace Techniques

Electroencephalography (EEG) signals are promising as a biometric owing to the increased protection they provide against spoofing. Previous studies have focused on capturing individual variability by analyzing task/condition-specific EEG. This work attempts to model biometric signatures independent of task/condition by normalizing the associated variance. Toward this goal, the paper extends ideas from subspace-based text-independent speaker recognition and proposes novel modification for modeling multi-channel EEG data. The proposed techniques assume that biometric information is present in entirety of the EEG signal. They accumulate statistics across time in a higher dimension space and then project it to a lower-dimensional space such that the biometric information is preserved. The embeddings obtained in the proposed approach are shown to encode task-independent biometric signatures by training and testing on different tasks or conditions. The best subspace system recognizes individuals with an equal error rate (EER) of 5.81% and 16.5% on datasets with 30 and 920 subjects using just nine EEG channels. The paper also provides insights into the scalability of the subspace model to unseen tasks and individuals during training and the number of channels needed for subspace modeling.