  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.
