Stability of EEG-Biometrics: The Role of Artifacts in Within vs. Cross Session Authentication

Abstract

Artifacts in the electroencephalogram (EEG) have not been investigated systematically regarding their effect on EEG biometric performance. Since artifacts vary over time, they can be expected to lower EEG biometric performance in cross session cross-validation. However, because of their presumed uniqueness, they might increase within session cross-validation performance of an EEG biometric system. We examined the EEG biometric performance of 15 features within and across 6 sessions recorded over 3 days, including 3 different cognitive conditions, before and after artifact exclusion in a sample of up to 90 individuals. Our results revealed significant effects of artifact exclusion depending on the cross-validation scenario and the feature. Overall, within session cross-validation was not systematically affected differently by artifact exclusion as compared to cross session cross-validation, but depending on the feature and the type of cognitive task employed, artifact exclusion led to better or worse results, with some frequency-dependent features performing better when artifacts were excluded. Our results emphasize the overestimation of biometric performance in within session cross-validation (with equal error rates (EERs) down to 0) as compared to cross session cross-validation (with EERs for the same feature above 30) and the low reliability of results obtained when evaluating EEG biometric performance in small samples. We recommend that future developments of EEG biometrics must test their robustness against artifacts in the data.