4,144 research outputs found
Human brain distinctiveness based on EEG spectral coherence connectivity
The use of EEG biometrics, for the purpose of automatic people recognition,
has received increasing attention in the recent years. Most of current analysis
rely on the extraction of features characterizing the activity of single brain
regions, like power-spectrum estimates, thus neglecting possible temporal
dependencies between the generated EEG signals. However, important
physiological information can be extracted from the way different brain regions
are functionally coupled. In this study, we propose a novel approach that fuses
spectral coherencebased connectivity between different brain regions as a
possibly viable biometric feature. The proposed approach is tested on a large
dataset of subjects (N=108) during eyes-closed (EC) and eyes-open (EO) resting
state conditions. The obtained recognition performances show that using brain
connectivity leads to higher distinctiveness with respect to power-spectrum
measurements, in both the experimental conditions. Notably, a 100% recognition
accuracy is obtained in EC and EO when integrating functional connectivity
between regions in the frontal lobe, while a lower 97.41% is obtained in EC
(96.26% in EO) when fusing power spectrum information from centro-parietal
regions. Taken together, these results suggest that functional connectivity
patterns represent effective features for improving EEG-based biometric
systems.Comment: Key words: EEG, Resting state, Biometrics, Spectral coherence, Match
score fusio
Wearable System Based on Ultra-Thin Parylene C Tattoo Electrodes for EEG Recording
In an increasingly interconnected world, where electronic devices permeate every aspect of our lives, wearable systems aimed at monitoring physiological signals are rapidly taking over the sport and fitness domain, as well as biomedical fields such as rehabilitation and prosthetics. With the intent of providing a novel approach to the field, in this paper we discuss the development of a wearable system for the acquisition of EEG signals based on a portable, low-power custom PCB specifically designed to be used in combination with non-conventional ultra-conformable and imperceptible Parylene-C tattoo electrodes. The proposed system has been tested in a standard rest-state experiment, and its performance in terms of discrimination of two different states has been compared to that of a commercial wearable device for EEG signal acquisition (i.e., the Muse headset), showing comparable results. This first preliminary validation demonstrates the possibility of conveniently employing ultra-conformable tattoo-electrodes integrated portable systems for the unobtrusive acquisition of brain activity
Cancellable Template Design for Privacy-Preserving EEG Biometric Authentication Systems
As a promising candidate to complement traditional biometric modalities,
brain biometrics using electroencephalography (EEG) data has received a
widespread attention in recent years. However, compared with existing
biometrics such as fingerprints and face recognition, research on EEG
biometrics is still in its infant stage. Most of the studies focus on either
designing signal elicitation protocols from the perspective of neuroscience or
developing feature extraction and classification algorithms from the viewpoint
of machine learning. These studies have laid the ground for the feasibility of
using EEG as a biometric authentication modality, but they have also raised
security and privacy concerns as EEG data contains sensitive information.
Existing research has used hash functions and cryptographic schemes to protect
EEG data, but they do not provide functions for revoking compromised templates
as in cancellable template design. This paper proposes the first cancellable
EEG template design for privacy-preserving EEG-based authentication systems,
which can protect raw EEG signals containing sensitive privacy information
(e.g., identity, health and cognitive status). A novel cancellable EEG template
is developed based on EEG graph features and a non-invertible transform. The
proposed transformation provides cancellable templates, while taking advantage
of EEG elicitation protocol fusion to enhance biometric performance. The
proposed authentication system offers equivalent authentication performance
(8.58\% EER on a public database) as in the non-transformed domain, while
protecting raw EEG data. Furthermore, we analyze the system's capacity for
resisting multiple attacks, and discuss some overlooked but critical issues and
possible pitfalls involving hill-climbing attacks, second attacks, and
classification-based authentication systems
In-ear EEG biometrics for feasible and readily collectable real-world person authentication
The use of EEG as a biometrics modality has been investigated for about a
decade, however its feasibility in real-world applications is not yet
conclusively established, mainly due to the issues with collectability and
reproducibility. To this end, we propose a readily deployable EEG biometrics
system based on a `one-fits-all' viscoelastic generic in-ear EEG sensor
(collectability), which does not require skilled assistance or cumbersome
preparation. Unlike most existing studies, we consider data recorded over
multiple recording days and for multiple subjects (reproducibility) while, for
rigour, the training and test segments are not taken from the same recording
days. A robust approach is considered based on the resting state with eyes
closed paradigm, the use of both parametric (autoregressive model) and
non-parametric (spectral) features, and supported by simple and fast cosine
distance, linear discriminant analysis and support vector machine classifiers.
Both the verification and identification forensics scenarios are considered and
the achieved results are on par with the studies based on impractical on-scalp
recordings. Comprehensive analysis over a number of subjects, setups, and
analysis features demonstrates the feasibility of the proposed ear-EEG
biometrics, and its potential in resolving the critical collectability,
robustness, and reproducibility issues associated with current EEG biometrics
BED: A new dataset for EEG-based biometrics
Various recent research works have focused on the use of electroencephalography (EEG) signals in the field of biometrics. However, advances in this area have somehow been limited by the absence of a common testbed that would make it possible to easily compare the performance of different proposals. In this work, we present a dataset that has been specifically designed to allow researchers to attempt new biometric approaches that use EEG signals captured by using relatively inexpensive consumer-grade devices. The proposed dataset has been made publicly accessible and can be downloaded from https://doi.org/10.5281/zenodo.4309471. It contains EEG recordings and responses from 21 individuals, captured under 12 different stimuli across three sessions. The selected stimuli included traditional approaches, as well as stimuli that aim to elicit concrete affective states, in order to facilitate future studies related to the influence of emotions on the EEG signals in the context of biometrics. The captured data were checked for consistency and a performance study was also carried out in order to establish a baseline for the tasks of subject verification and identification
Influencing brain waves by evoked potentials as biometric approach: taking stock of the last six years of research
The scientific advances of recent years have made available to anyone affordable hardware devices capable of doing something unthinkable until a few years ago, the reading of brain waves. It means that through small wearable devices it is possible to perform an electroencephalography (EEG), albeit with less potential than those offered by high-cost professional devices. Such devices make it possible for researchers a huge number of experiments that were once impossible in many areas due to the high costs of the necessary hardware. Many studies in the literature explore the use of EEG data as a biometric approach for people identification, but, unfortunately, it presents problems mainly related to the difficulty of extracting unique and stable patterns from users, despite the adoption of sophisticated techniques. An approach to face this problem is based on the evoked potentials (EPs), external stimuli applied during the EEG reading, a noninvasive technique used for many years in clinical routine, in combination with other diagnostic tests, to evaluate the electrical activity related to some areas of the brain and spinal cord to diagnose neurological disorders. In consideration of the growing number of works in the literature that combine the EEG and EP approaches for biometric purposes, this work aims to evaluate the practical feasibility of such approaches as reliable biometric instruments for user identification by surveying the state of the art of the last 6 years, also providing an overview of the elements and concepts related to this research area
PolyCosGraph:A Privacy-Preserving Cancelable EEG Biometric System
Recent findings confirm that biometric templates derived from electroencephalography (EEG) signals contain sensitive information about registered users, such as age, gender, cognitive ability, mental status and health information. Existing privacy-preserving methods such as hash function and fuzzy commitment are not cancelable, where raw biometric features are vulnerable to hill-climbing attacks. To address this issue, we propose the PolyCosGraph, a system based on Polynomial transformation embedding Cosine functions with Graph features of EEG signals, which is a privacy-preserving and cancelable template design that protects EEG features and system security against multiple attacks. In addition, a template corrupting process is designed to further enhance the security of the system, and a corresponding matching algorithm is developed. Even when the transformed template is compromised, attackers cannot retrieve raw EEG features and the compromised template can be revoked. The proposed system achieves the authentication performance of 1.49% EER with a resting state protocol, 0.68% EER with a motor imagery task, and 0.46% EER under a watching movie condition, which is equivalent to that in the non-encrypted domain. Security analysis demonstrates that our system is resistant to attacks via record multiplicity, preimage attacks, hill-climbing attacks, second attacks and brute force attacks.</p
A Prototype of EEG System for IoT
[Abstract] In this work, we develop open source hardware and software for eye state classification and integrate it with a protocol for the Internet of Things (IoT). We design and build the hardware using a reduced number of components and with a very low-cost. Moreover, we propose a method for the detection of open eyes (oE) and closed eyes (cE) states based on computing a power ratio between different frequency bands of the acquired signal. We compare several real- and complex-valued transformations combined with two decision strategies: a threshold-based method and a linear discriminant analysis. Simulation results show both classifier accuracies and their corresponding system delays.Xunta de Galicia; ED431G2019/01)Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-RXunta de Galicia; ED481A-2018/156Xunta de Galicia; ED431G 2019/01This work has been funded by the Xunta de Galicia (ED431G2019/01), the Agencia Estatal de Investigacion of Spain (TEC2016-75067-C4-1-R) and ERDF funds of the EU (AEI/FEDER, UE), and the predoctoral Grant No. ED481A-2018/156 (Francisco Laport). CITIC as a Research Centre of the Galician University System is financed by the Conselleria de Educacion, Universidades e Formacion Profesional (Xunta de Galicia) through the ERDF (80%), Operational Programme ERDF Galicia 2014–2020 and the remaining 20% by the Secretaria Xeral de Universidades (Ref. ED431G 2019/01
Evidence of Task-Independent Person-Specific Signatures in EEG using Subspace Techniques
Electroencephalography (EEG) signals are promising as alternatives to other
biometrics owing to their protection against spoofing. Previous studies have
focused on capturing individual variability by analyzing
task/condition-specific EEG. This work attempts to model biometric signatures
independent of task/condition by normalizing the associated variance. Toward
this goal, the paper extends ideas from subspace-based text-independent speaker
recognition and proposes novel modifications for modeling multi-channel EEG
data. The proposed techniques assume that biometric information is present in
the entire EEG signal and accumulate statistics across time in a high
dimensional space. These high dimensional statistics are then projected to a
lower dimensional space where the biometric information is preserved. The lower
dimensional embeddings obtained using the proposed approach are shown to be
task-independent. The best subspace system identifies individuals with
accuracies of 86.4% and 35.9% on datasets with 30 and 920 subjects,
respectively, using just nine EEG channels. The paper also provides insights
into the subspace model's scalability to unseen tasks and individuals during
training and the number of channels needed for subspace modeling.Comment: \copyright 2021 IEEE. Personal use of this material is permitted.
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Feature selection for EEG Based biometrics
Department of Human Factors EngineeringEEG-based biometrics identify individuals by using personal and distinctive information in human brain. This thesis aims to evaluate the electroencephalography (EEG) features and channels for biometrics and to propose methodology that identifies individuals. In my research, I recorded fourteen EEG channel signals from thirty subjects. While record EEG signal, subjects were asked to relax and keep eyes closed for 2 minutes. In addition, to evaluate intra-individual variability, we recorded EEG ten times for each subject, and every recording conducted on different days to reduce within-day effects. After acquisition of data, for each channel, I calculated eight features: alpha/beta power ratio, alpha/theta power ratio, beta/theta power ratio, median frequency, PSD entropy, permutation entropy, sample entropy, and maximum Lyapunov exponents. Then, I scored 112 features with three feature selection algorithms: Fisher score, reliefF, and information gain. Finally, I classified EEG data using a linear discriminant analysis (LDA) with a leave-one-out cross validation method. As a result, the best feature set was composed of 23 features that highly ranked on Fisher score and yielded a 18.56% half total error rate. In addition, according to scores calculated by feature selection, EEG channels located on occipital and right temporal areas most contributed to identify individuals. Thus, with suggested methodologies and channels, implementation of efficient EEG-based biometrics is possible.ope
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