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Brain-computer interfaces (BCIs) create a new form of communication and control for humans by translating brain activity directly into actions performed by a computer. This new field of research, best known for its breakthroughs in enabling fully paralyzed or locked-in patients to communicate and control simple devices, has resulted in a variety of remarkable technological developments. However, the field is still in its infancy, and facilitating control of a computer application via thought in a broader context involves a number of a challenges that have not yet been met. Advancing BCIs beyond the experimental phase continues to be a struggle. End-users have rarely been reached, except for in the case of a few highly specialized applications which require continual involvement of BCI experts. While these applications are profoundly beneficial for the patients they serve, the potential for BCIs is much broader in scope and powerful in effect. Unfortunately, the current approaches to brain-computer interfacing research have not been able to address the primary limitations in the field: the poor reliability of most BCIs and the highly variable performance across individuals. In addition to this, the modes of control available to users tend to be restrictive and unintuitive (\emph{e.g.}, imagining complex motor activities to answer ``Yes" or ``No" questions). This thesis presents a novel approach that addresses both of these limitations simultaneously. Brain-computer interfacing is currently viewed primarily as a machine learning problem, wherein the computer must learn the patterns of brain activity associated with a user's mental commands. In order to simplify this problem, researchers often restrict mental commands to those which are well characterized and easily distinguishable based on \emph{a priori} knowledge about their corresponding neural correlates. However, this approach does not fully recognize two properties of a BCI which makes it unique to other human-computer interfaces. First, individuals can vary widely with respect to the patterns of activation associated with how their brains generate similar mental activity and with respect to which kinds of mental activity have been most trained due to life experience. Thus, it is not surprising that BCIs based on predefined neural correlates perform inconsistently for different users. Second, for a BCI to perform well, the human and the computer must become a cohesive unit such that the computer can adapt as the user's brain naturally changes over time and while the user learns to make their mental commands more consistent and distinguishable given feedback from the computer. This not only implies that BCI use is a skill that must be developed, honed, and maintained in relation to the computer's algorithms, but that the human is the fundamental component of the system in a way that makes human learning just as important as machine learning. In this thesis it is proposed that, in the long term, a generalized BCI that can discover the appropriate neural correlates of individualized mental commands is preferable to the traditional approach. Generalization across mental strategies allows each individual to make better use of their own experience and cognitive abilities in order to interact with BCIs in a more free and intuitive way. It is further argued that in addition to generalization, it is necessary to develop improved training protocols respecting the potential of the user to learn to effectively modulate their own brain activity for BCI use. It is shown through a series of studies exploring generalized BCI methods, the influence of prior non-BCI training on BCI performance, and novel methods for training individuals to control their own brain activity, that this new approach based on balancing the roles of the user and the computer according to their respective capabilities is a promising avenue for advancing brain-computer interfacing towards a broader array of applications usable by the general population. / Thesis / Doctor of Philosophy (PhD)

A detailed walkthrough of the engineering process for the development of an accessible search engine tool. Contributions include a comprehensive literature review, assumptions, requirements, high-level design, implementation, and usability evaluations. / The internet has become useful in just about anything we do. Unfortunately, as vision degrades so does our ability to perceive the web. The design of Graphical User Interfaces (GUIs) has become overwhelmingly common and is meant to be coupled with a screen and mouse. The interface introduced in this thesis was developed to avoid graphically driven design and create a novel Search Engine interface intended for blind and low vision users. This is achieved by minimizing the total concern of the user (the userspace) to a handful of options and a predetermined structure to the Search Engine Results Page (SERP). This thesis describes the entire development process starting from the literature review and including implementation, evaluation, and future work. / Thesis / Master of Applied Science (MASc)

This research addresses a problem that centers on the persistent disparities in computer use and access among racial minorities, particularly African-Americans and Latinos, and persons of low socio-economic status (SES) here in the USA. "Access" to computer technology maintains a dual meaning. Access may refer to having a computer and software available for use or it may refer to having a computer interface that effectively facilitates user learning. This study conceptualizes "access" as the latter — having an interface that facilitates user learning. One intervention for this problem of access, from a Human Factors perspective, is in recognizing and accounting for culture's influence on one's cognition. Both qualitative and quantitative approaches were integrated to effectively determine a process for engaging typically marginalized groups, interface metaphor preferences of African-Americans, and user performance with varying types of interface metaphors. The qualitative aspects of this study provided a basis for understanding how entry was obtained into the participants' community and for obtaining richer descriptions of user successes and challenges with the various interface designs. The researcher developed a culturally valid interface design methodology, i.e., Acculturalization Interface Design (A.I.D.) methodology, which was used to identify meaningful computer interface metaphors for low SES African-Americans. Through the A.I.D. methodology and an associated field study, a group of African-American novice computer users determined that the home, the bedroom and comfort were meaningful computer interface metaphors to integrate into a letter writing task. A separate group of African-Americans performed benchmark tasks on an interface design that utilized the home, bedroom and comfort metaphors or Microsoft Word 2003. The African-American group performed significantly better on the novel interface than on Microsoft Word 2003 for several benchmark tasks. Qualitative analyses showed that low acculturation African-Americans were particularly challenged with those same tasks. Regression analyses used to determine the relationship between psychosocial characteristics and user performance were inconclusive. Subject matter experts (SME), representing low SES Latinos, discussed potential learnability issues for both interface designs. Furthermore, results from the African-American group and the SMEs highlight the critical importance of using terminology (i.e., verbal metaphors) and pictorial metaphors that are culturally and socially valid. / Ph. D.

This dissertation addresses user responses to the introduction of Brain-Computer Interface technologies (BCI) for gaming and consumer applications in the early part of the 21st century. BCI technology has emerged from the contexts of interrelated medical, academic, and military research networks including an established computer and gaming industry. First, I show that the emergence and development of BCI technology are based on specific economic, socio-cultural, and material factors, and secondly, by utilizing user surveys and interviews, I argue that the success of BCI are not determined by these contextual factors but are dependent on user acceptance and interpretation. Therefore, this project contributes to user-technology studies by developing a model which illustrates the interrelations between producers, users, values, and technology and how they contribute to the acceptance, resistance, and modification in the technological development of emerging BCI technologies. This project focuses on human computer interaction researchers, independent developers, the companies producing BCI headsets, and neuro-gadget companies who are developing BCI's for users as an alternative interface for the enhancement of human performance and gaming and computer simulated experience. Moreover, BCI production and use as modes of enhancement align significantly with social practices of play which allows an expanded definition of technology to include cultural dimensions of play. / Ph. D.

We consider unconstrained Simulation Optimization (SO) problems, that is, optimization problems where the underlying objective function is unknown but can be estimated at any chosen point by repeatedly executing a Monte Carlo (stochastic) simulation. SO, introduced more than six decades ago through the seminal work of Robbins and Monro (and later by Kiefer and Wolfowitz), has recently generated much attention. Such interest is primarily because of SOs flexibility, allowing the implicit specification of functions within the optimization problem, thereby providing the ability to embed virtually any level of complexity. The result of such versatility has been evident in SOs ready adoption in fields as varied as finance, logistics, healthcare, and telecommunication systems. While SO has become popular over the years, Robbins and Monros original stochastic approximation algorithm and its numerous modern incarnations have seen only mixed success in solving SO problems. The primary reason for this is stochastic approximations explicit reliance on a sequence of algorithmic parameters to guarantee convergence. The theory for choosing such parameters is now well-established, but most such theory focuses on asymptotic performance. Automatically choosing parameters to ensure good finite-time performance has remained vexingly elusive, as evidenced by continuing efforts six decades after the introduction of stochastic approximation! The other popular paradigm to solve SO is what has been called sample-average approximation. Sample-average approximation, more a philosophy than an algorithm to solve SO, attempts to leverage advances in modern nonlinear programming by first constructing a deterministic approximation of the SO problem using a fixed sample size, and then applying an appropriate nonlinear programming method. Sample-average approximation is reasonable as a solution paradigm but again suffers from finite-time inefficiency because of the simplistic manner in which sample sizes are prescribed. It turns out that in many SO contexts, the effort expended to execute the Monte Carlo oracle is the single most computationally expensive operation. Sample-average approximation essentially ignores this issue since, irrespective of where in the search space an incumbent solution resides, prescriptions for sample sizes within sample-average approximation remain the same. Like stochastic approximation, notwithstanding beautiful asymptotic theory, sample-average approximation suffers from the lack of automatic implementations that guarantee good finite-time performance. In this dissertation, we ask: can advances in algorithmic nonlinear programming theory be combined with intelligent sampling to create solution paradigms for SO that perform well in finite-time while exhibiting asymptotically optimal convergence rates? We propose and study a general solution paradigm called Sampling Controlled Stochastic Recursion (SCSR). Two simple ideas are central to SCSR: (i) use any recursion, particularly one that you would use (e.g., Newton and quasi- Newton, fixed-point, trust-region, and derivative-free recursions) if the functions involved in the problem were known through a deterministic oracle; and (ii) estimate objects appearing within the recursions (e.g., function derivatives) using Monte Carlo sampling to the extent required. The idea in (i) exploits advances in algorithmic nonlinear programming. The idea in (ii), with the objective of ensuring good finite-time performance and optimal asymptotic rates, minimizes Monte Carlo sampling by attempting to balance the estimated proximity of an incumbent solution with the sampling error stemming from Monte Carlo. This dissertation studies the theoretical and practical underpinnings of SCSR, leading to implementable algorithms to solve SO. We first analyze SCSR in a general context, identifying various sufficient conditions that ensure convergence of SCSRs iterates to a solution. We then analyze the nature of such convergence. For instance, we demonstrate that in SCSRs which guarantee optimal convergence rates, the speed of the underlying (deterministic) recursion and the extent of Monte Carlo sampling are intimately linked, with faster recursions permitting a wider range of Monte Carlo effort. With the objective of translating such asymptotic results into usable algorithms, we formulate a family of SCSRs called Adaptive SCSR (A-SCSR) that adaptively determines how much to sample as a recursion evolves through the search space. A-SCSRs are dynamic algorithms that identify sample sizes to balance estimated squared bias and variance of an incumbent solution. This makes the sample size (at every iteration of A-SCSR) a stopping time, thereby substantially complicating the analysis of the behavior of A-SCSRs iterates. That A-SCSR works well in practice is not surprising" the use of an appropriate recursion and the careful sample size choice ensures this. Remarkably, however, we show that A-SCSRs are convergent to a solution and exhibit asymptotically optimal convergence rates under conditions that are no less general than what has been established for stochastic approximation algorithms. We end with the application of a certain A-SCSR to a parameter estimation problem arising in the context of brain-computer interfaces (BCI). Specifically, we formulate and reduce the problem of probabilistically deciphering the electroencephalograph (EEG) signals recorded from the brain of a paralyzed patient attempting to perform one of a specified set of tasks. Monte Carlo simulation in this context takes a more general view, as the act of drawing an observation from a large dataset accumulated from the recorded EEG signals. We apply A-SCSR to nine such datasets, showing that in most cases A-SCSR achieves correct prediction rates that are between 5 and 15 percent better than competing algorithms. More importantly, due to the incorporated adaptive sampling strategies, A-SCSR tends to exhibit dramatically better efficiency rates for comparable prediction accuracies. / Ph. D.

Vast improvements in robotics and wireless communication have made teleoperated robots significantly more prevalent in industry, defense, and research. To help bridge the gap for these robots in the workplace, there has been a tremendous increase in research toward the development of biomimetic robotic hands that can simulate human operators. However, current methods of control are limited in scope and do not adequately represent human muscle memory and skills. The vision of this thesis is to provide a pathway for overcoming these limitations and open an opportunity for development and implementation of a cost effective methodology towards controlling a robotic hand. The first chapter describes the experiments conducted using Flexpoint bend sensors in conjunction with a simple voltage divider to generate a cost-effective data glove that is significantly less expensive than the commercially available alternatives. The data glove was able to provide sensitivity of less than 0.1 degrees. The second chapter describes the molding process for embedding pressure sensors in silicone skin and data acquisition from them to control the robotic hand. The third chapter describes a method for parsing and observing the information from the data glove and translating the relevant control variables to the robotic hand. The fourth chapter focuses on the feasibility of the brain computer interfaces (BCI) and successfully demonstrates the implementation of a simple brain computer interface in controlling a robotic hand. / Master of Science

Brain-computer interface (BCI) or Brain Machine Interface (BMI) provides direct communication channel between user’s brain and an external device without any requirement of user’s physical movement. Primarily BCI has been employed in medical sciences to facilitate the patients with severe motor, visual and aural impairments. More recently many BCI are also being used as a part of entertainment. BCI differs from Neuroprosthetics, a study within Neuroscience, in terms of its usage; former connects the brain with a computer or external device while the later connects the nervous system to an implanted device. A BCI receives the modulated input from user either invasively or non-invasively. The modulated input, concealed in the huge amount of noise, contains distinct brain patterns based on the type of activity user is performing at that point in time. Primary task of a typical BCI is to find out those distinct brain patterns and translates them to meaningful communication command set. Cursor controllers, Spellers, Wheel Chair and robot Controllers are classic examples of BCI applications. This study aims to investigate an Electroencephalography (EEG) based non-invasive BCI in general and its interaction with a web interface in particular. Different aspects related to BCI are covered in this work including feedback techniques, BCI frameworks, commercial BCI hardware, and different BCI applications. BCI paradigm Steady State Visually Evoked Potentials (SSVEP) is being focused during this study. A hybrid solution is developed during this study, employing a general purpose BCI framework OpenViBE, which comprised of a low-level stimulus management and control module and a web based Google Street View client application. This study shows that a BCI can not only provide a way of communication for the impaired subjects but it can also be a multipurpose tool for a healthy person. During this study, it is being established that the major hurdles that hamper the performance of a BCI system are training protocols, BCI hardware and signal processing techniques. It is also observed that a controlled environment and expert assistance is required to operate a BCI system.