Image-Evoked Affect and its Impact on Eeg-Based Biometrics

Electroencephalography (EEG) signals provide a representation of the brain's activity patterns and have been recently exploited for user identification and authentication due to their uniqueness and their robustness to interception and artificial replication. Nevertheless, such signals are commonly affected by the individual's emotional state. In this work, we examine the use of images as stimulus for acquiring EEG signals and study whether the use of images that evoke similar emotional responses leads to higher identification accuracy compared to images that evoke different emotional responses. Results show that identification accuracy increases when the system is trained with EEG recordings that refer to similar emotional states as the EEG recordings that are used for identification, demonstrating an up to 5.3% increase on identification accuracy compared to when recordings referring to different emotional states are used. Furthermore, this improvement holds independently of the features and classification algorithms employed.