The use of electroencephalogram (EEG), an electrophysiological monitoring method for recording the brain activity, for authentication has attracted the interest of researchers for over a decade. In addition to exhibiting qualities of biometric-based authentication, they are revocable, impossible to mimic, and resistant to coercion attacks. However, EEG signals carry a wealth of information about an individual and can reveal private information about the user. This brings significant privacy issues to EEG-based authentication systems as they have access to raw EEG signals. This thesis proposes a privacy-preserving EEG-based authentication system that preserves the privacy of the user by not revealing the raw EEG signals while allowing the system to authenticate the user accurately. In that, perceptual hashing is utilized and instead of raw EEG signals, their perceptually hashed values are used in the authentication process. In addition to describing the authentication process, algorithms to compute the perceptual hash are developed based on two feature extraction techniques. Experimental results show that an authentication system using perceptual hashing can achieve performance comparable to a system that has access to raw EEG signals if enough EEG channels are used in the process. This thesis also presents a security analysis to show that perceptual hashing can prevent information leakage.

In the development of digitally connected solutions that require the use of personal data, the issue of data privacy is an important factor that must be taken into account. Simply informing users about how data is used and getting their consent with a simple click is not enough to create awareness of the issue of data privacy and let them make a conscious decision about the use of their personal data. Furthermore, there is a big gap in knowledge about what personal data is and what is considered sensitive data. Especially the knowledge about what biometric identifiers that they are used in a variety of everyday life applications and in which sense the handling can be problematic is unknown.This thesis project explores how the use of critical play in form of an activist game can create awareness of the issue of data privacy, inform about the value of biometric data and foster self-reflection of handling one’s own personal data. Through the simulation of dependencies between personal data, the motivation to share them and the aggregation of personal data in combination with real and prospective use cases, players are empowered to reflect on their behaviour and to critically deal with the topic of data privacy.

Centers for Disease Control and Prevention (CDC) estimate that more than 11.2 million people require critical and emergency care in the United States per year. Optimizing and improving patient morbidity and mortality outcomes are the primary objectives of monitoring in critical and emergency care. Patients in need of critical or emergency care in general are at a risk of single or multiple organ failures occurring due to a traumatic injury, a surgical event, or an underlying pathology that results in severe patient hemodynamic instability. Hence, continuous monitoring of fundamental cardiovascular hemodynamic parameters, such as heart rate, respiratory rate, blood pressure, blood oxygenation and core temperature, is essential to accomplish diagnostics in critical and emergency care. Today’s standard of care measures these critical parameters using multiple monitoring technologies. Though it is possible to measure all the fundamental cardiovascular hemodynamic parameters using the blood flow dynamics, its use is currently only limited to measuring continuous blood pressure. No other comparable studies in the literature were successful in quantifying other critical parameters from the blood flow dynamics for a few reasons. First, the blood flow dynamics exhibit a complicated and sensitive dynamic pressure field. Existing blood flow based data acquisition systems are unable to detect these sensitive variations in the pressure field. Further, the pressure field is also influenced by the presence of background acoustic interference, resulting in a noisy pressure profile. Thus in order to extract critical parameters from this dynamic pressure field with fidelity, there is need for an integrated framework that is composed of a highly sensitive data acquisition system and advanced signal processing. In addition, existing state-of-the-art technologies require expensive instrumentation and complex infrastructure. The information sensed using these multiple monitoring technologies is integrated and visualized using a clinical information system. This process of integration and visualization creates the need for functional interoperability within the multiple monitoring technologies. Limited functional interoperability not only results in diagnostic errors but also their complexity makes it impossible to use such technologies to accomplish monitoring in low resource settings. These multiple monitoring technologies are neither portable nor scalable, in addition to inducing extreme patient discomfort. For these reasons, existing monitoring technologies do not efficiently meet the monitoring and diagnostic requirements of critical and emergency care. In order to address the challenges presented by existing blood flow based data acquisition systems and other monitoring systems, a point of care monitoring device was developed to provide multiple critical parameters by means of uniquely measuring a physiological process. To demonstrate the usability of this novel catheter multiscope, a feasibility study was performed using an animal model. The corresponding results are presented in this dissertation. The developed measurement system first acquires the dynamics of blood flow through a minimally invasive catheter. Then, a signal processing framework is developed to characterize the blood flow dynamics and to provide critical parameters such as heart rate, respiratory rate, and blood pressure. The framework used to extract the physiological data corresponding to the acoustic field of the blood flow consisted of a noise cancellation technique and a wavelet based source separation. The preliminary results of the acoustic field of the blood flow revealed the presence of acoustic heart and respiratory pulses. A unique and novel framework was also developed to extract continuous blood pressure from the pressure field of the blood flow. Finally, the computed heart and respiratory rates, systolic and diastolic pressures were benchmarked with actual values measured using conventional devices to validate the measurements of the catheter multiscope. In summary, the results of the feasibility study showed that the novel catheter multiscope can provide critical parameters such as heart rate, respiratory rate and blood pressure with clinical accuracy. In addition, this dissertation also highlights the diagnostic potential of the developed catheter multiscope by presenting preliminary results of proof of concept studies performed for application case studies such as sinus rhythm pattern recognition and fetal monitoring through phonocardiography.