Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Banerjee, Ayan
Description
Due to the advent of easy-to-use, portable, and cost-effective brain signal sensing devices, pervasive Brain-Machine Interface (BMI) applications using Electroencephalogram (EEG) are growing rapidly. The main objectives of these applications are: 1) pervasive collection of brain data from multiple users, 2) processing the collected data to recognize the corresponding mental states, and 3) providing real-time feedback to the end users, activating an actuator, or information harvesting by enterprises for further services. Developing BMI applications faces several challenges, such as cumbersome setup procedure, low signal-to-noise ratio, insufficient signal samples for analysis, and long processing times. Internet-of-Things (IoT) technologies provide the opportunity to solve these challenges through large scale data collection, fast data transmission, and computational offloading.
This research proposes an IoT-based framework, called BraiNet, that provides a standard design methodology for fulfilling the pervasive BMI applications requirements including: accuracy, timeliness, energy-efficiency, security, and dependability. BraiNet applies Machine Learning (ML) based solutions (e.g. classifiers and predictive models) to: 1) improve the accuracy of mental state detection on-the-go, 2) provide real-time feedback to the users, and 3) save power on mobile platforms. However, BraiNet inherits security breaches of IoT, due to applying off-the-shelf soft/hardware, high accessibility, and massive network size. ML algorithms, as the core technology for mental state recognition, are among the main targets for cyber attackers. Novel ML security solutions are proposed and added to BraiNet, which provide analytical methodologies for tuning the ML hyper-parameters to be secure against attacks.
To implement these solutions, two main optimization problems are solved: 1) maximizing accuracy, while minimizing delays and power consumption, and 2) maximizing the ML security, while keeping its accuracy high. Deep learning algorithms, delay and power models are developed to solve the former problem, while gradient-free optimization techniques, such as Bayesian optimization are applied for the latter. To test the framework, several BMI applications are implemented, such as EEG-based drivers fatigue detector (SafeDrive), EEG-based identification and authentication system (E-BIAS), and interactive movies that adapt to viewers mental states (nMovie). The results from the experiments on the implemented applications show the successful design of pervasive BMI applications based on the BraiNet framework.
This research proposes an IoT-based framework, called BraiNet, that provides a standard design methodology for fulfilling the pervasive BMI applications requirements including: accuracy, timeliness, energy-efficiency, security, and dependability. BraiNet applies Machine Learning (ML) based solutions (e.g. classifiers and predictive models) to: 1) improve the accuracy of mental state detection on-the-go, 2) provide real-time feedback to the users, and 3) save power on mobile platforms. However, BraiNet inherits security breaches of IoT, due to applying off-the-shelf soft/hardware, high accessibility, and massive network size. ML algorithms, as the core technology for mental state recognition, are among the main targets for cyber attackers. Novel ML security solutions are proposed and added to BraiNet, which provide analytical methodologies for tuning the ML hyper-parameters to be secure against attacks.
To implement these solutions, two main optimization problems are solved: 1) maximizing accuracy, while minimizing delays and power consumption, and 2) maximizing the ML security, while keeping its accuracy high. Deep learning algorithms, delay and power models are developed to solve the former problem, while gradient-free optimization techniques, such as Bayesian optimization are applied for the latter. To test the framework, several BMI applications are implemented, such as EEG-based drivers fatigue detector (SafeDrive), EEG-based identification and authentication system (E-BIAS), and interactive movies that adapt to viewers mental states (nMovie). The results from the experiments on the implemented applications show the successful design of pervasive BMI applications based on the BraiNet framework.
ContributorsSadeghi Oskooyee, Seyed Koosha (Author) / Gupta, Sandeep K S (Thesis advisor) / Santello, Marco (Committee member) / Li, Baoxin (Committee member) / Venkatasubramanian, Krishna K (Committee member) / Banerjee, Ayan (Committee member) / Arizona State University (Publisher)
Created2020
Description
In recent years, brain signals have gained attention as a potential trait for biometric-based security systems and laboratory systems have been designed. A real-world brain-based security system requires to be usable, accurate, and robust. While there have been developments in these aspects, there are still challenges to be met. With regard to usability, users need to provide lengthy amount of data compared to other traits such as fingerprint and face to get authenticated. Furthermore, in the majority of works, medical sensors are used which are more accurate compared to commercial ones but have a tedious setup process and are not mobile. Performance wise, the current state-of-art can provide acceptable accuracy on a small pool of users data collected in few sessions close to each other but still falls behind on a large pool of subjects over a longer time period. Finally, a brain security system should be robust against presentation attacks to prevent adversaries from gaining access to the system.
This dissertation proposes E-BIAS (EEG-based Identification and Authentication System), a brain-mobile security system that makes contributions in three directions. First, it provides high performance on signals with shorter lengths collected by commercial sensors and processed with lightweight models to meet the computation/energy capacity of mobile devices. Second, to evaluate the system's robustness a novel presentation attack was designed which challenged the literature's presumption of intrinsic liveness property for brain signals. Third, to bridge the gap, I formulated and studied the brain liveness problem and proposed two solution approaches (model-aware & model agnostic) to ensure liveness and enhance robustness against presentation attacks. Under each of the two solution approaches, several methods were suggested and evaluated against both synthetic and manipulative classes of attacks (a total of 43 different attack vectors). Methods in both model-aware and model-agnostic approaches were successful in achieving an error rate of zero (0%). More importantly, such error rates were reached in face of unseen attacks which provides evidence of the generalization potentials of the proposed solution approaches and methods. I suggested an adversarial workflow to facilitate attack and defense cycles to allow for enhanced generalization capacity for domains in which the decision-making process is non-deterministic such as cyber-physical systems (e.g. biometric/medical monitoring, autonomous machines, etc.). I utilized this workflow for the brain liveness problem and was able to iteratively improve the performance of both the designed attacks and the proposed liveness detection methods.
ContributorsSohankar Esfahani, Mohammad Javad (Author) / Gupta, Sandeep K.S. (Thesis advisor) / Santello, Marco (Committee member) / Dasgupta, Partha (Committee member) / Banerjee, Ayan (Committee member) / Arizona State University (Publisher)
Created2021