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

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. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

High accuracy EEG biometrics identification using ICA and AR model

Modern biometric identification methods combine interdisciplinary approaches to enhance person identification and classification accuracy. One popular technique for this purpose is Brain-Computer Interface (BCI).The signal so obtained from BCI will be further processed by the Autoregressive (AR) Model for feature extraction. Many researches in the area find that for more accurate results, the signal must be cleaned before extracting any useful feature information. This study proposes Independent Component Analysis (ICA), k-NN classifier, and AR as the combined techniques for electroencephalogram (EEG) biometrics to achieve the highest personal identification and classification accuracy. However, there is a classification gap between using the combined ICA with the AR model and AR model alone.Therefore, this study takes one step further by modifying the feature extraction of AR and comparing the outcome with the proposed approaches in lieu of prior researches. The experiment based on four relevant locations shows that the combined ICA and AR can achieve higher accuracy than the modified AR. More combinations of channels and subjects are required in future research to explore the significance of channel effects and to enhance the identification accuracy

One-Step, Three-Factor Passthought Authentication With Custom-Fit, In-Ear EEG

In-ear EEG offers a promising path toward usable, discreet brain-computer interfaces (BCIs) for both healthy individuals and persons with disabilities. To test the promise of this modality, we produced a brain-based authentication system using custom-fit EEG earpieces. In a sample of N = 7 participants, we demonstrated that our system has high accuracy, higher than prior work using non-custom earpieces. We demonstrated that both inherence and knowledge factors contribute to authentication accuracy, and performed a simulated attack to show our system's robustness against impersonation. From an authentication standpoint, our system provides three factors of authentication in a single step. From a usability standpoint, our system does not require a cumbersome, head-worn device

Security, Comfort, Healthcare, and Energy Saving: A Review on Biometric Factors for Smart Home Environment

The Internet of Things (IoT) have become significantly important in authentication mechanisms in which traditional authentication have shift to the biometric factors whereby biometric is said to offer more security and convenience to the users.The purpose of this paper is to provide an extensive review on biometric factors for smart home environments that are intended for security, comfort, healthcare, and energy saving.This paper also discusses the security authentication mechanisms, which are knowledge factor (password, PIN), ownership factor (ID card, passport), and inherent factor (fingerprint, iris, facial), known as biometric factors.Biometric factors can be used as authentications for smart home environments, which are more robust and reliable in terms of accuracy, convenience, and speed

A Comprehensive Analysis on EEG Signal Classification Using Advanced Computational Analysis

Electroencephalogram (EEG) has been used in a wide array of applications to study mental disorders. Due to its non-invasive and low-cost features, EEG has become a viable instrument in Brain-Computer Interfaces (BCI). These BCI systems integrate user\u27s neural features with robotic machines to perform tasks. However, due to EEG signals being highly dynamic in nature, BCI systems are still unstable and prone to unanticipated noise interference. An important application of this technology is to help facilitate the lives of the tetraplegic through assimilating human brain impulses and converting them into mechanical motion. However, BCI systems are remarkably challenging to implement as recorded brain signals can be unreliable and vary in pattern throughout time. In the initial work, a novel classifier structure is proposed to classify different types of imaginary motions (left hand, right hand, and imagination of words starting with the same letter) across multiple sessions using an optimized set of electrodes for each user. The proposed technique uses raw brain signals obtained utilizing 32 electrodes and classifies the imaginary motions using Artificial Neural Networks (ANN). To enhance the classification rate and optimize the set of electrodes of each subject, a majority voting system combining a set of simple ANNs is used. This electrode optimization technique achieved classification accuracies of 69.83%, 94.04% and 84.56% respectively for the three subjects considered in this work. In the second work, the signal variations are studied in detail for a large EEG dataset. Using the Independent Component Analysis (ICA) with a dynamic threshold model, noise features were filtered. The data was classified to a high precision of more than 94% using artificial neural networks. A decreased variance in classification validated both, the effectiveness of the proposed dynamic threshold systems and the presence of higher concentrations of noise in data for specific subjects. Using this variance and classification accuracy, subjects were separated into two groups. The lower accuracy group was found to have an increased variance in classification. To confirm these results, a Kaiser windowing technique was used to compute the signal-to-noise ratio (SNR) for all subjects and a low SNR was obtained for all EEG signals pertaining to the group with the poor data classification. This work not only establishes a direct relationship between high signal variance, low SNR, and poor signal classification but also presents classification results that are significantly higher than the accuracies reported by prior studies for the same EEG user dataset

An enhanced fuzzy commitment scheme in biometric template protection

Biometric template protection consists of two approaches; Feature Transformation (FT) and Biometric Cryptography (BC). This research focuses on Key-Binding Technique based on Fuzzy Commitment Scheme (FCS) under BC approach. In FCS, the helper data should not disclose any information about the biometric data. However, literatures showed that it had dependency issue in its helper data which jeopardize security and privacy. Moreover, this also increases the probability of privacy leakage which lead to attacks such as brute-force and cross-matching attack. Thus, the aim of this research is to reduce the dependency of helper data that can caused privacy leakage. Three objectives have been set such as (1) to identify the factors that cause dependency on biometric features (2) to enhance FCS by proposing an approach that reduces this dependency, and (3) to evaluate the proposed approach based on parameters such as security, privacy, and biometric performance. This research involved four phases. Phase one, involved research review and analysis, followed by designing conceptual model and algorithm development in phase two and three respectively. Phase four, involved with the evaluation of the proposed approach. The security and privacy analysis shows that with the additional hash function, it is difficult for adversary to perform brute‐force attack on information stored in database. Furthermore, the proposed approach has enhanced the aspect of unlinkability and prevents cross-matching attack. The proposed approach has achieved high accuracy of 95.31% with Equal Error Rate (EER) of 1.54% which performs slightly better by 1.42% compared to the existing approach. This research has contributed towards the key-binding technique of biometric fingerprint template protection, based on FCS. In particular, this research was designed to create a secret binary feature that can be used in other state-of-the-art cryptographic systems by using an appropriate error-correcting approach that meets security standards