A New Approach for EEGBased Biometric Authentication Using Auditory Stimulation In this paper, a new approach is followed for the human recognition task using brainwave responses to auditory stimulation. A system based on this class of brainwaves benefits extra features over conventional traits being more secure, harder to spoof, and cancelable. For this purpose, EEG signals were recorded from 21 subjects while listening to modulated auditory tones in a single and twosession setups. Three different types of features were evaluated based on the energy and the entropy estimation of the EEG subband rhythms using narrow band Gaussian filtering and wavelet packet decomposition. These features are classified using discriminant analysis in identification and verification modes of authentication. Based on the achieved results, high recognition rates up to 97.18% and low error rates down to 4.3% were achieved in single session setup. Moreover, in a twosession setup, the proposed system in this paper is shown to be more timepermanent in comparison to previous works.

Eventrelated potential from EEG for a twostep Identity Authentication System Current problems related to highlevel security access are increasing, leaving organizations and persons unsafe. A recent good candidate to create a robust identity authentication system is based on brain signals recorded with electroencephalograms (EEG). In this paper, EEGbased brain signals of 56 channels, from eventrelated potentials (ERPs), are used for Subject identification. The ERPs are from positive or negative feedbackrelated responses of a P300speller system. The feature extraction part was done with empirical mode decomposition (EMD) extracting 2 intrinsic mode functions (IMFs) per channel, that were selected based on the Minkowski distance. After that, 4 features are computed per IMF; 2 energy features (instantaneous and teager energy) and 2 fractal features (Higuchi and Petrosian fractal dimension). Support vector machine (SVM) was used for the classification stage with an accuracy index computed using 10folds crossvalidation for evaluating the classifierxe2x80x99s performance. Since highdensity EEG information was available, the wellknown backwardelimination and forwardaddition greedy algorithms were used to reduce or increase the number of channels, step by step. Using the proposed method for subject identification from a positive or negative feedbackrelated response and then identify the subject will add a layer to improve the security system. The results obtained show that subject identification is feasible even using a low number of channels: E.g., 0.89 of accuracy using 5 channels with a mixed population and 0.93 with a maleonly population.