Table 2: Performance estimates of various methods using EEG as a biometrics under different validations and test conditions. EO: eye open and EC: eye closed, VEP: visual evoked potential, NN: neural network, MAP: maximum a posteriori model, and HTER: half total error rate defined as ( F A R + F R R ) / 2 .

Methods Data size Data representation techniques Classification techniques Classification accuracy Test conditions

Poulos et al. [12] 4 subjects 255 EEG epochs Autoregressive (AR) model Linear vector quantizer (LVQ) NN 72%–80% Resting with EO
Paranjape et al. [13] 40 subjects 349 EEG epochs AR model Discriminant analysis 80% Resting with EO, resting with EC
Palaniappan and Mandic [14] 102 subjects 3,560 VEP signals Multiple signal classification k-Nearest neighbors (kNN), Elman NN 9 8 . 1 2 % ± 1 . 2 6 %
9 6 . 1 3 % ± 1 . 0 3 %
Eye-blink-free VEP signals
Marcel and Millan [16] 9 subjects Power spectral density (PSD) Gaussian mixture model and MAP N/R HTER: 7.1%–36.0% Evaluations of hand movements and words generation