Event-related potential from EEG for a two-step Identity Authentication System

Abstract

Current problems related to high-level 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, EEG-based brain signals of 56 channels, from event-related potentials (ERPs), are used for Subject identification. The ERPs are from positive or negative feedback-related responses of a P300-speller 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 cross-validation for evaluating the classifier's performance. Since high-density EEG information was available, the wellknown backward-elimination and forward-addition 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 feedback-related 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 male-only population.

Multi-objective optimization for EEG channel selection and accurate intruder detection in an EEG-based subject identification system

“…In our previous works, we have studied and compared various paradigms, i.e. resting-state potentials and ERPs, using various types of electrodes, a various number of channels, and channel localization [2][3][4]6 . Several parameters are yet to be optimized.…”

“…A number of different paradigms are used to stimulate and record EEG signals in various subject identification approaches: imagined speech [1][2][3] , resting-state potentials 4,5 , and event-related potentials (ERPs) 6 . ERPs are very low-voltage signals emitted by the human brain that appears on the scalp in response to specific events or stimuli.…”

“…In general, resting-state potentials and ERPs have been shown to be good candidates for a new biometric system for which there are several different state-of-the-art approaches 6,[9][10][11][12] , with the localization of the relevant channels differing, depending on the paradigm. Here, we used the ERP approach to stimulate the EEG signals.…”

“…An important element is dimensionality reduction, which can be tackled through channel selection and feature extraction. Several approaches can be used to accomplish this task, including those based on methods such as principal component analysis (PCA) 13 , discrete wavelet transform (DWT) 3 , empirical mode decomposition (EMD) 2,3,6 , and even approaches using raw data as input for different configurations of neural networks (NN) 14,15 . Based on the current state-of-the-art and the results of our previous studies, we used EMD for sub-band extraction and then extracted four features for each: instantaneous and Teager energy and Higuchi and Petrosian fractal dimensions.…”

Towards a minimal EEG channel array for a biometric system using resting-state and a genetic algorithm for channel selection

“…For EEG-based biometric systems, several approaches have been presented using various paradigms to stimulate and record the EEG signals, i.e. imagined speech [1][2][3] , resting-state [4][5][6][7][8][9][10] , and event-related potentials (ERPs) 11,12 .…”