Security systems are used by organizations to protect places or information for which privileges are needed or require access authorization, as well as to deny unauthorized access to facilities, equipment, or resources and protect against espionage, theft, or even terrorist attacks. Various safety measures have long been proposed, ranging from the use of generic systems (security guards, closed-circuit television, smart cards, proximity readers, and RFID) to that of biometric identifiers (fingerprints, palmprints, retinal scans, etc.) [268,269].
Biometric recognition refers to the automatic recognition of individuals based on their physiological and/or behavioral features [268]. A biometric system is a pattern recognition system that operates by acquiring biometric data from subjects, extracting a set of features, and comparing this set of features against a template
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process of current traditional/biometric security systems [269], there is a growing interest in exploring new biometric measures. In this context, the use of brain signals to create biometric markers using various neuro-paradigms has emerged as a robust alternative to the above-mentioned vulnerabilities.
Brain signals can be used as a basis for the design of biometric markers, as any human physiological and/or behavioral characteristic can be used as a biometric feature, as long as it satisfies the following requirements:universality, permanence, collectability, performance, acceptability, and circumvention[268]. Brain signals are highly reliable and secure because biometric markers obtained from EEG-recordings of human brain activity are almost impossible to duplicate, as the brain is highly individual [271].
An authentication system may include a stage in which the data is used in a multi-class model with all the subjects in the dataset to identify a specific subject.
It may also include a verification step to compare the data from the claimed subject with that of the true subject, alone in the dataset, to detect whether the subject is an intruder or not. The order of these stages may differ depending on the approach.
The number of EEG-based biometric systems has been steadily growing using various approaches to solve problems related to the authentication and verification stages.
A research-grade EEG device guarantees a controlled environment and high-quality multi-channel EEG recording, but this is offset by the high computational cost, non-portability of the equipment, and use of inconvenient conductive gels. The development of dry EEG sensors has created new possibilities for the development of new types of portable EEG systems. An important step towards
5.2. State-of-the-art 85 this goal is a reduction in the number of required EEG channels while increasing, or at least maintaining, the same performance as high-density EEG.
5.2 State-of-the-art
Depending on whether the paradigm is task-dependent or task-independent, certain EEG channels provide only redundant or sub-optimal information. Several techniques have been studied with the aim of developing low-density EEG-based systems with high performance, i.e., pre-processing and feature extraction, channel selection, and paradigms to stimulate brain signals. For EEG-based biometric systems, several approaches have been presented using various paradigms to stimulate and record the EEG signals, i.e., imagined speech [222,223,272], resting-state [85,173,273–277], and ERPs [138,206].
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 [206,273,276–278], with the localization of the relevant channels differing, depending on the paradigm.
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 PCA, DWT, EMD, and even approaches using raw data as input for different configurations of neural networks (NN) [138,206,222,223,279–283].
Several approaches have been proposed for the creation of biometric systems following various experiment configurations with various paradigms and methods for feature extraction and classification using the EEGMMIDB dataset (see Section 3.6.2), using various configurations of neural networks [280, 284–286], other supervised and unsupervised techniques [274,278,287–296], and methods for EEG channel selection [201,275,297].
One approach used a subset of eight pre-selected channels [297] and EEG data from a task for training and then that from another task for testing. The selection of the channels was justified based on their stability across various mental tasks, and the results presented were evaluated using the half total error rate (HTER), which was 14.69%. Another approach used various tasks from the EEGMMIDB and channel selection, using the binary flower pollination algorithm (BFPA), and reported accuracy values of up to 0.87 using supervised learning and
An approach with one-second EEG signals from the FP1 and FP2 channels and a 256-Hz sample rate during the resting state has been proposed for a biometric system, extracting features directly from the raw data and using Fisher’s discriminant analysis [276], obtaining a TAR of up to 0.966 and a false acceptance rate (FAR) of 0.034. Another approach used two-second EEG signals from the FP1 and FP2 channels, with a 2048-Hz sample rate, and the authors used a set of classifiers to perform multi-class classification [273]. They obtained an accuracy of 0.93 and a false positive identification rate of 0.165. Another approach presented the results of a study using the Cz EEG channel, which was manually selected , on 20 subjects during the resting-state [277], obtaining a TAR of 1.0 and TRR of over 0.8. None of these studies attempted to systematically select the minimal number of optimal channels to perform the task.
Deep-learning algorithms have shown success in image processing and other fields but have not shown convincing and consistent improvement over the most advanced current methods for EEG data [148,282]. However, several new approaches have been recently presented that show high accuracy. For example, an approach using convolutional neural network (CNN) gated recurrent units (CNN-GRU) was presented in [281], and the authors evaluated the proposed method in a public dataset called DEAP, which consists of EEG signals from 32 subjects recorded from 32 channels using different emotions as a paradigm [298].
Their experiments were performed using 10-second segments of EEG signals and they reported a mean CRR of up to 0.999 with 32 channels using CNN-GRU and 0.991 with five channels that were selected using one-way repeated measures ANOVA with Bonferroni pairwise comparison (post-hoc). The findings of this
5.3. First approach using a two-stage classification process 87