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

The Use of EEG Signals For Biometric Person Recognition

This work is devoted to investigating EEG-based biometric recognition systems. One potential advantage of using EEG signals for person recognition is the difficulty in generating artificial signals with biometric characteristics, thus making the spoofing of EEG-based biometric systems a challenging task. However, more works needs to be done to overcome certain drawbacks that currently prevent the adoption of EEG biometrics in real-life scenarios: 1) usually large number of employed sensors, 2) still relatively low recognition rates (compared with some other biometric modalities), 3) the template ageing effect. The existing shortcomings of EEG biometrics and their possible solutions are addressed from three main perspectives in the thesis: pre-processing, feature extraction and pattern classification. In pre-processing, task (stimuli) sensitivity and noise removal are investigated and discussed in separated chapters. For feature extraction, four novel features are proposed; for pattern classification, a new quality filtering method, and a novel instance-based learning algorithm are described in respective chapters. A self-collected database (Mobile Sensor Database) is employed to investigate some important biometric specified effects (e.g. the template ageing effect; using low-cost sensor for recognition). In the research for pre-processing, a training data accumulation scheme is developed, which improves the recognition performance by combining the data of different mental tasks for training; a new wavelet-based de-noising method is developed, its effectiveness in person identification is found to be considerable. Two novel features based on Empirical Mode Decomposition and Hilbert Transform are developed, which provided the best biometric performance amongst all the newly proposed features and other state-of-the-art features reported in the thesis; the other two newly developed wavelet-based features, while having slightly lower recognition accuracies, were computationally more efficient. The quality filtering algorithm is designed to employ the most informative EEG signal segments: experimental results indicate using a small subset of the available data for feature training could receive reasonable improvement in identification rate. The proposed instance-based template reconstruction learning algorithm has shown significant effectiveness when tested using both the publicly available and self-collected databases

On the Usability of Electroencephalographic Signals for Biometric Recognition: A Survey

Research on using electroencephalographic signals for biometric recognition has made considerable progress and is attracting growing attention in recent years. However, the usability aspects of the proposed biometric systems in the literatures have not received significant attention. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of electroencephalography (EEG)-based biometric recognition. We first compare the characteristics of different stimuli that have been used for evoking biometric information bearing EEG signals. This is followed by a survey of the reported features and classifiers employed for EEG biometric recognition. To highlight the usability challenges of using EEG for biometric recognition in real-life scenarios, we propose a novel usability assessment framework which combines a number of user-related factors to evaluate the reported systems. The evaluation scores indicate a pattern of increasing usability, particularly in recent years, of EEG-based biometric systems as efforts have been made to improve the performance of such systems in realistic application scenarios. We also propose how this framework may be extended to take into account Aging effects as more performance data becomes available

Task sensitivity in EEG biometric recognition

This work explores the sensitivity of electroencephalographic-based biometric recognition to the type of tasks required by subjects to perform while their brain activity is being recorded. A novel wavelet-based feature is used to extract identity information from a database of 109 subjects who performed four different motor movement/imagery tasks while their data was recorded. Training and test of the system was performed using a number of experimental protocols to establish if training with one type of task and tested with another would significantly affect the recognition performance. Also, experiments were conducted to evaluate the performance when a mixture of data from different tasks was used for training. The results suggest that performance is not significantly affected when there is a mismatch between training and test tasks. Furthermore, as the amount of data used for training is increased using a combination of data from several tasks, the performance can be improved. These results indicate that a more flexible approach may be incorporated in data collection for EEG-based biometric systems which could facilitate their deployment and improved performance

Learning EEG Biometrics for Person Identification and Authentication

EEG provides appealing biometrics by presenting some unique attributes, not possessed by common biometric modalities like fingerprints, retina and face scan, in terms of robustness against forgery, secrecy and privacy compliance, aliveness detection and potential of continuous authentication. Meanwhile, the use of EEG to provide cognitive indicators for human workload, fatigue and emotions has created an environment where EEG is well-integrated into systems, making it readily available for biometrics purposes. Yet, still, many challenges need to be properly addressed before any actual deployment of EEG-based biometric systems in real-life scenarios: 1) subjects' inconvenience during the signal acquisition process, 2) the relatively low recognition rates, and 3) the lack of robustness against diverse human states. To address the aforementioned issues, this thesis is devoted to learn biometric traits from EEG signals for stable person identification and authentication. State of the art studies of EEG biometrics are mainly divided into two categories, the event-related potential (ERP) category, which relies on a tight control of the cognitive states of the subjects, and the ongoing EEG category, which uses continuous EEG signals (mainly in resting state) naturally produced by the brain without any particular sensory stimulation. Studies in the ERP category focus more on the design of proper signal elicitation protocols or paradigms which usually require repetitive sensory stimulation. Ongoing EEG, on the contrary, is more flexible in terms of signal acquisition, but needs more advanced computational methods for feature extraction and classification. This study focuses on EEG biometrics using ongoing signals in diverse states. Such a flexible system could lead to an effective deployment in the real world. Specifically, this work focuses on ongoing EEG signals under diverse human states without strict task-specific controls in terms of brain response elicitation during signal acquisition. This is in contrast to previous studies that rely on specific sensory stimulation and synthetic cognitive tasks to tightly control the cognitive state of the subject being reflected in the resulting EEG activity, or to use resting state EEG signals. The relaxation of the reliance of the user's cognitive state makes the signal acquisition process streamlined, which in turn facilitates the actual deployment of the EEG biometrics system. Furthermore, not relying on sensory stimulation and cognitive tasks also allows for flexible and unobtrusive biometric systems that work in the background without interrupting the users, which is especially important in continuous scenarios. However, relaxing the system's reliance on the human state also means losing control of the EEG activity produced. As a result, EEG signals captured from the scalp may be contaminated by the active involvement of the tasks and cognitive states such as workload and emotion. Therefore, it becomes a challenge to learn identity-bearing information from the complicated signals to support high stability EEG biometrics. Possible solutions are proposed and investigated from two main perspectives, feature extraction and pattern classification. Specifically, graph features and learning models are proposed based on the brain connectivity, graph theory, and deep learning algorithms. A comprehensive investigation is conducted to assess the performance of proposed methods and existing methods in biometric identification and authentication, including in continuous scenarios. The methods and experiments are reported and detailed in the corresponding chapters, with the results obtained from data analysis

Evidence of a Task-Independent Neural Signature in the Spectral Shape of the Electroencephalogram

Life-history traits for 61 species of lizards from the family Phrynosomatidae

Down-Conditioning of Soleus Reflex Activity using Mechanical Stimuli and EMG Biofeedback

Spasticity is a common syndrome caused by various brain and neural injuries, which can severely impair walking ability and functional independence. To improve functional independence, conditioning protocols are available aimed at reducing spasticity by facilitating spinal neuroplasticity. This down-conditioning can be performed using different types of stimuli, electrical or mechanical, and reflex activity measures, EMG or impedance, used as biofeedback variable. Still, current results on effectiveness of these conditioning protocols are incomplete, making comparisons difficult. We aimed to show the within-session task- dependent and across-session long-term adaptation of a conditioning protocol based on mechanical stimuli and EMG biofeedback. However, in contrast to literature, preliminary results show that subjects were unable to successfully obtain task-dependent modulation of their soleus short-latency stretch reflex magnitude