Functional Connectivity Methods for EEG-based Biometrics on a Large, Heterogeneous Dataset
This study examines the utility of functional connectivity (FC) and
graph-based (GB) measures with a support vector machine classifier for use in
electroencephalogram (EEG) based biometrics. Although FC-based features have
been used in biometric applications, studies assessing the identification
algorithms on heterogeneous and large datasets are scarce. This work
investigates the performance of FC and GB metrics on a dataset of 184 subjects
formed by pooling three datasets recorded under different protocols and
acquisition systems. The results demonstrate the higher discriminatory power of
FC than GB metrics. The identification accuracy increases with higher frequency
EEG bands, indicating the enhanced uniqueness of the neural signatures in beta
and gamma bands. Using all the 56 EEG channels common to the three databases,
the best identification accuracy of 97.4% is obtained using phase-locking value
(PLV) based measures extracted from the gamma frequency band. Further, we
investigate the effect of the length of the analysis epoch to determine the
data acquisition time required to obtain satisfactory identification accuracy.
When the number of channels is reduced to 21 from 56, there is a marginal
reduction of 2.4% only in the identification accuracy using PLV features in the
gamma band. Additional experiments have been conducted to study the effect of
the cognitive state of the subject and mismatched train/test conditions on the
performance of the system.