Abstract
Biometric identification (BI) of individuals is a fast-growing field of research that is producing increasingly sophisticated applications in several spheres of everyday life. Previous magnetic resonance imaging (MRI) studies have demonstrated that based on the high inter-individual variability of brain structure and function, it is possible to identify individuals with high accuracy. Otherwise, there is the common belief that electroencephalographic (EEG) data recorded at the surface of the scalp are too noisy for identification purposes with a comparably high hit rate. In the present work, we compared BI quality (F1-scores, accuracy, sensitivity, and specificity) between different types of functional (instantaneous, lagged, and total coherence, phase synchronization, correlation, and mutual information) and effective (Granger causality, phase synchronization, and coherence) connectivity measures. Results revealed that across functional connectivity metrics, identification accuracy was in the range of 0.98–1, whereas sensitivity and F1-scores were between 0.00 and 1 and specificity was between 0.99 and 1. BI was higher for the connectivity metrics that are contaminated by volume conduction (instantaneous connectivity) compared to those that are unaffected by this variable (lagged connectivity). Support vector machine and neural network algorithms yielded the highest BI, followed by random forest and weighted k-nearest neighborhood, whereas linear discriminant analysis was less accurate. These results provide cross-validated counterevidence to the belief that EEG data are too noisy for identification purposes and demonstrate that functional and effective connectivity metrics are particularly suited for BI applications with comparable accuracy to MRI. Our results have important implications for fast, low-cost, and mobile BI applications.
Valizadeh, Seyed Abolfazl