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

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe