An Unsupervised Channel Selection Method for SSVEP-based Brain Computer Interfaces

Brain-computer interfaces (BCIs) provide an alternative communication channel for people with motor deficits that prevent normal communication. The underlying premise of a BCI is that a neuroimaging process such as electroencephalography (EEG) can be used to measure the user’s brain activity as signals. The obtained signals are analyzed to determine the user’s intended actions and a computer system can be used to replace voluntary muscle activity as a means of communication. The information transfer rate (ITR) of an algorithm used for determining the user’s intentions greatly affects the perceived practicality of the BCI system. Such algorithms are divided into two main categories, supervised and unsupervised. While the former achieves higher ITR, the latter is most useful when the user is unable to be involved in the calibration process of the BCI system. In our paper, we introduce an unsupervised algorithm for steady-state visual evoked potential (SSVEP)-based BCIs. Our algorithm works in three steps: (i) it selects multiple sets of electroencephalogram channels, then (ii) applies a feature extraction method to each one of these channel sets. As its final step, (iii) it combines the extracted features from these channel sets by performing a majority vote, yielding a classification. We evaluate the ITR attained using our proposed method on a dataset of 35 subjects using three different feature extraction methods. We then compare these results to existing methods in the literature that use a single channel set without a majority vote. The proposed method indicates an improvement for at least 7 subjects

Anchor-Assisted and Vote-Based Trustworthiness Assurance in Smart City Crowdsensing

Smart city sensing calls for crowdsensing via mobile devices that are equipped with various built-in sensors. As incentivizing users to participate in distributed sensing is still an open research issue, the trustworthiness of crowdsensed data is expected to be a grand challenge if this cloud-inspired recruitment of sensing services is to be adopted. Recent research proposes reputation-based user recruitment models for crowdsensing; however, there is no standard way of identifying adversaries in smart city crowdsensing. This paper adopts previously proposed vote-based approaches, and presents a thorough performance study of vote-based trustworthiness with trusted entities that are basically a subset of the participating smartphone users. Those entities are called trustworthy anchors of the crowdsensing system. Thus, an anchor user is fully trustworthy and is fully capable of voting for the trustworthiness of other users, who participate in sensing of the same set of phenomena. Besides the anchors, the reputations of regular users are determined based on vote-based (distributed) reputation. We present a detailed performance study of the anchor-based trustworthiness assurance in smart city crowdsensing through simulations, and compare it with the purely vote-based trustworthiness approach without anchors, and a reputation-unaware crowdsensing approach, where user reputations are discarded. Through simulation findings, we aim at providing specifications regarding the impact of anchor and adversary populations on crowdsensing and user utilities under various environmental settings. We show that significant improvement can be achieved in terms of usefulness and trustworthiness of the crowdsensed data if the size of the anchor population is set properl

Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-Sensing

Ubiquity of mobile devices with rich sensory capabilities has given rise to the mobile crowd-sensing (MCS) concept, in which a central authority (the platform) and its participants (mobile users) work collaboratively to acquire sensory data over a wide geographic area. Recent research in MCS highlights the following facts: 1) a utility metric can be defined for both the platform and the users, quantifying the value received by either side; 2) incentivizing the users to participate is a non-trivial challenge; 3) correctness and truthfulness of the acquired data must be verified, because the users might provide incorrect or inaccurate data, whether due to malicious intent or malfunctioning devices; and 4) an intricate relationship exists among platform utility, user utility, user reputation, and data trustworthiness, suggesting a co-quantification of these inter-related metrics. In this paper, we study two existing approaches that quantify crowd-sensed data trustworthiness, based on statistical and vote-based user reputation scores. We introduce a new metric - collaborative reputation scores - to expand this definition. Our simulation results show that collaborative reputation scores can provide an effective alternative to the previously proposed metrics and are able to extend crowd sensing to applications that are driven by a centralized as well as decentralized control

Game-Theoretic Recruitment of Sensing Service Providers for Trustworthy Cloud-Centric Internet-of-Things (IoT) Applications

Widespread use of connected smart devices that are equipped with various built-in sensors has introduced the mobile crowdsensing concept to the IoT-driven information and communication applications. Mobile crowdsensing requires implicit collaboration between the crowdsourcer/recruiter platforms and users. Additionally, users need to be incentivized by the crowdsensing platform because each party aims to maximize their utility. Due to the participatory nature of data collection, trustworthiness and truthfulness pose a grand challenge in crowdsensing systems in the presence of malicious users, who either aim to manipulate sensed data or collaborate unfaithfully with the motivation of maximizing their income. In this paper, we propose a game-theoretic approach for trustworthiness-driven user recruitment in mobile crowdsensing systems that consists of three phases: i) user recruitment, ii) collaborative decision making on trust scores, and iii) badge rewarding. Our proposed framework incentivizes the users through a sub-game perfect equilibrium (SPE) and gamification techniques. Through simulations, we show that the platform utility can be improved by up to the order of 50\% while the average user utility can be increased by at least 15\% when compared to fully-distributed and user-centric trustworthy crowdsensing

Building decision support systems from large ECG data sets

University of Rochester. Department of Electrical and Computer Engineering, 2016.The advent of portable medical sensors such as electrocardiograms has enabled widespread remote monitoring. The data collected from such devices presents new research opportunities and challenges to improve diagnosis and treatment for every individual patient. Condensing the sensor data into a form that is useful for doctors and researchers is the focus of this work. In particular, I investigate techniques to streamline the handling and processing of large data sets, the utility of long-term monitoring data (such as Holter ECG recordings) in decision support, and the application of machine learning in risk stratification. To support this process, I explain a system for automatically delineating key markers in ECG recordings, and displaying them in a novel way for identification of anomalies or patterns. I then show how this method can be used in drug studies, as well as to unmask potential problems in patients with certain genetic disorders. Finally, machine learning and statistical analysis techniques are used to identify patients who have a higher risk of symptoms or who require urgent care. In the process, many challenges with the use of ECG data in machine learning algorithms are addressed

Design and analysis of privacy-preserving medical cloud computing systems

Thesis (Ph. D.)--University of Rochester. Department of Electrical and Computer Engineering, 2016.Current financial and regulatory pressure has provided strong incentives to institute better disease prevention, improved patient monitoring, and push U.S. healthcare into the digital era. Outsourcing medical applications to a cloud operator helps healthcare organizations (HCO) to provide better patient care without increasing the associated costs. Despite these advantages, the adoption of medical cloud computing by HCO’s has been slow due to the strict regulations on the privacy of Personal Health Information (PHI) dictated by The Health Insurance Portability and Accountability Act (HIPAA). In this dissertation, we propose a novel privacy-preserving medical cloud computing system with an emphasis on “secure computation.” The proposed system enables monitoring patients remotely outside the HCO using ECG signals. To eliminate privacy concerns associated with the public cloud providers, we utilize Fully Homomorphic Encryption (FHE) to enable computations on encrypted PHI data. Despite well-known performance penalties associated with FHE, we propose two methods for an efficient implementation. Specifically, we model our applications using two computational models: circuit and branching program, and propose optimizations to improve run-time performance. We compare our FHE-based solution with conventional and Attribute Based Encryption schemes for secure a) storage, b) computation, and c) sharing of the medical data. We show that despite the overhead compared to existing encryption schemes, our system can be implemented with a reasonable budget with major public cloud service providers. With the recent advances on FHE coupled with the decreasing costs of cloud services, we argue that our study is a novel step towards privacy-preserving cloud-based health monitoring that can improve the diagnosis of cardiac diseases, which are responsible for the highest percentage of deaths in the United States

AXaaS: Case for Acceleration as a Service

Abstract-The ubiquity and the range of utility of "smart" devices is ever increasing. Device limitations have lead developers to leverage cloud-offloading to gain performance for their applications. As users become aware of the expanding utility of their devices through these powerful applications, they tend to demand more from them. However, developers' intent on providing state-of-the-art applications will undoubtedly hit performance barriers for emerging products due to the inherently high latency of the prevailing mobile-cloud architecture. This paper proposes a new type of service architecture called AXaaS (Acceleration as a Service) that will empower developers to satisfy user demand for greater application performance and fully realize new computationally-intensive applications that would be otherwise impossible or impractical. While Telecom Service Providers (TSP) already provide data and bandwidth services, we introduce a new paradigm in which the TSP may charge subscribers for computational acceleration of complex applications by outsourcing computational tasks to larger cloud operators. We provide an exposition of the performance potential of such a service by examining its theoretical impact upon an opensource-based Face Recognition application. We also examine a sample instantiation of cloud resources via Amazon Web Services, and estimate the return on investment for a TSP implementing AXaaS. We find the TSP-side ROI to be quite favorable, which means that AXaaS is a viable new aaS alternative