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

TL;DR

This paper demonstrates that task-independent biometric signatures can be extracted from EEG signals using subspace techniques, enabling reliable person identification across different tasks and conditions.

Contribution

It introduces novel subspace-based methods for modeling EEG biometrics that are independent of specific tasks or conditions, extending ideas from speaker recognition.

Findings

Achieves 86.4% accuracy on 30 subjects with 9 channels

Achieves 35.9% accuracy on 920 subjects with 9 channels

Shows scalability to unseen tasks and individuals

Abstract

Electroencephalography (EEG) signals are promising as alternatives to other biometrics owing to their protection 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 modifications for modeling multi-channel EEG data. The proposed techniques assume that biometric information is present in the entire EEG signal and accumulate statistics across time in a high dimensional space. These high dimensional statistics are then projected to a lower dimensional space where the biometric information is preserved. The lower dimensional embeddings obtained using the proposed approach are shown to be task-independent. The best subspace system identifies individuals with accuracies of 86.4% and 35.9% on datasets with 30 and 920 subjects, respectively, using just nine EEG channels. The paper also provides insights into the subspace model's scalability to unseen tasks and individuals during training and the number of channels needed for subspace modeling.