Overcoming Inter-Subject Variability in BCI Using EEG-Based Identification

The high dependency of the Brain Computer Interface (BCI) system performance on the BCI user is a well-known issue of many BCI devices. This contribution presents a new way to overcome this problem using a synergy between a BCI device and an EEG-based biometric algorithm. Using the biometric algorithm, the BCI device automatically identifies its current user and adapts parameters of the classification process and of the BCI protocol to maximize the BCI performance. In addition to this we present an algorithm for EEG-based identification designed to be resistant to variations in EEG recordings between sessions, which is also demonstrated by an experiment with an EEG database containing two sessions recorded one year apart. Further, our algorithm is designed to be compatible with our movement-related BCI device and the evaluation of the algorithm performance took place under conditions of a standard BCI experiment. Estimation of the mu rhythm fundamental frequency using the Frequency Zooming AR modeling is used for EEG feature extraction followed by a classifier based on the regularized Mahalanobis distance. An average subject identification score of 96 % is achieved

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

Neural network security and optimization for single-person authentication using electroencephalogram data

Includes bibliographical references.2022 Fall.Security is an important focus for devices that use biometric data, and as such security around authentication needs to be considered. This is true for brain-computer interfaces (BCIs), which often use electroencephalogram (EEG) data as inputs and neural network classification to determine their function. EEG data can also serve as a form of biometric authentication, which would contribute to the security of these devices. Neural networks have also used a method known as ablation to improve their efficiency. In light of this info, the goal of this research is to determine whether neural network ablation can also be used as a method to improve security by reducing a network's learning capabilities to include authenticating only a given target, and preventing adversaries from training new data to be authenticated. Data on the change in entropy of weight values of the networks after training was also collected for the purpose of determining patterns in weight distribution. Results from a set of ablated networks to a set of baseline (non-ablated) networks for five targets chosen randomly from a data set of 12 people were compared. The results found that ablated maintained accuracy through the ablation process, but that they did not perform as well as the baseline networks. Change in performance between single-target authentication and target-plus-invader authentication was also examined, but no significant results were found. Furthermore, the change in entropy differed between both baseline networks and ablated networks, as well as between single-target authentication and target-plus-invader authentication for all networks. Ablation was determined to have potential for security applications that need to be expanded on, and weight distribution was found to have some correlation with the complexity of an input to a network

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

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