3 research outputs found
In-ear EEG biometrics for feasible and readily collectable real-world person authentication
The use of EEG as a biometrics modality has been investigated for about a
decade, however its feasibility in real-world applications is not yet
conclusively established, mainly due to the issues with collectability and
reproducibility. To this end, we propose a readily deployable EEG biometrics
system based on a `one-fits-all' viscoelastic generic in-ear EEG sensor
(collectability), which does not require skilled assistance or cumbersome
preparation. Unlike most existing studies, we consider data recorded over
multiple recording days and for multiple subjects (reproducibility) while, for
rigour, the training and test segments are not taken from the same recording
days. A robust approach is considered based on the resting state with eyes
closed paradigm, the use of both parametric (autoregressive model) and
non-parametric (spectral) features, and supported by simple and fast cosine
distance, linear discriminant analysis and support vector machine classifiers.
Both the verification and identification forensics scenarios are considered and
the achieved results are on par with the studies based on impractical on-scalp
recordings. Comprehensive analysis over a number of subjects, setups, and
analysis features demonstrates the feasibility of the proposed ear-EEG
biometrics, and its potential in resolving the critical collectability,
robustness, and reproducibility issues associated with current EEG biometrics
Evidence of Task-Independent Person-Specific Signatures in EEG using Subspace Techniques
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.Comment: \copyright 2021 IEEE. Personal use of this material is permitted.
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