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

Commodity brain-computer interfaces (BCI) are beginning to accompany everything from toys and games to sophisticated health care devices. These contemporary interfaces allow for varying levels of interaction with a computer. Not surprisingly, the more intimately BCIs are integrated into the nervous system, the better the control a user can exert on a system. At one end of the spectrum, implanted systems can enable an individual with full body paralysis to utilize a robot arm and hold hands with their loved ones [28, 62]. On the other end of the spectrum, the untapped potential of commodity devices supporting electroencephalography (EEG) and electromyography (EMG) technologies require innovative approaches and further research. This thesis proposes a modularized software architecture designed to build flexible systems based on input from commodity BCI devices. An exploratory study using a commodity EEG provides concrete assessment of the potential for the modularity of the system to foster innovation and exploration, allowing for a combination of a variety of algorithms for manipulating data and classifying results. Specifically, this study analyzes a pipelined architecture for researchers, starting with the collection of spatio temporal brain data (STBD) from a commodity EEG device and correlating it with intentional behaviour involving keyboard and mouse input. Though classification proves troublesome in the preliminary dataset considered, the architecture demonstrates a unique and flexible combination of a liquid state machine (LSM) and a deep belief network (DBN). Research in methodologies and techniques such as these are required for innovation in BCIs, as commodity devices, processing power, and algorithms continue to improve. Limitations in terms of types of classifiers, their range of expected inputs, discrete versus continuous data, spatial and temporal considerations and alignment with neural networks are also identified. / Graduate / 0317 / 0984 / jasoncummer@gmail.com

<p>Brain-computer interfaces (BCI) have the potential to restore communication or control abilities in individuals with severe neuromuscular limitations, such as those with amyotrophic lateral sclerosis (ALS). The role of a BCI is to extract and decode relevant information that conveys a user's intent directly from brain electro-physiological signals and translate this information into executable commands to control external devices. However, the BCI decision-making process is error-prone due to noisy electro-physiological data, representing the classic problem of efficiently transmitting and receiving information via a noisy communication channel. </p><p>This research focuses on P300-based BCIs which rely predominantly on event-related potentials (ERP) that are elicited as a function of a user's uncertainty regarding stimulus events, in either an acoustic or a visual oddball recognition task. The P300-based BCI system enables users to communicate messages from a set of choices by selecting a target character or icon that conveys a desired intent or action. P300-based BCIs have been widely researched as a communication alternative, especially in individuals with ALS who represent a target BCI user population. For the P300-based BCI, repeated data measurements are required to enhance the low signal-to-noise ratio of the elicited ERPs embedded in electroencephalography (EEG) data, in order to improve the accuracy of the target character estimation process. As a result, BCIs have relatively slower speeds when compared to other commercial assistive communication devices, and this limits BCI adoption by their target user population. The goal of this research is to develop algorithms that take into account the physical limitations of the target BCI population to improve the efficiency of ERP-based spellers for real-world communication. </p><p>In this work, it is hypothesised that building adaptive capabilities into the BCI framework can potentially give the BCI system the flexibility to improve performance by adjusting system parameters in response to changing user inputs. The research in this work addresses three potential areas for improvement within the P300 speller framework: information optimisation, target character estimation and error correction. The visual interface and its operation control the method by which the ERPs are elicited through the presentation of stimulus events. The parameters of the stimulus presentation paradigm can be modified to modulate and enhance the elicited ERPs. A new stimulus presentation paradigm is developed in order to maximise the information content that is presented to the user by tuning stimulus paradigm parameters to positively affect performance. Internally, the BCI system determines the amount of data to collect and the method by which these data are processed to estimate the user's target character. Algorithms that exploit language information are developed to enhance the target character estimation process and to correct erroneous BCI selections. In addition, a new model-based method to predict BCI performance is developed, an approach which is independent of stimulus presentation paradigm and accounts for dynamic data collection. The studies presented in this work provide evidence that the proposed methods for incorporating adaptive strategies in the three areas have the potential to significantly improve BCI communication rates, and the proposed method for predicting BCI performance provides a reliable means to pre-assess BCI performance without extensive online testing.</p> / Dissertation

This thesis investigated the development of an EEG-based brain controlled switch and the design of a software for nerve conduction study. For EEG-based brain controlled switch, we proposed a novel paradigm for an online brain-controlled switch based on Event-Related Synchronizations (ERDs) following external sync signals. Furthermore, the ERD feature was enhanced by 3 event-related moving averages and the performance was tested online. Subjects were instructed to perform an intended motor task following an external sync signal in order to turn on a virtual switch. Meanwhile, the beta-band (16-20Hz) relative ERD power (ERD in reverse value order) of a single EEG Laplacian channel from primary motor area was calculated and filtered by 3 event-related moving average in real-time. The computer continuously monitored the filtered relative ERD power level until it exceeded a pre-set threshold selected based on the observations of ERD power range to turn on the virtual switch. Four right handed healthy volunteers participated in this study. The false positive rates encountered among the four subjects during the operation of the virtual switch were 0.8±0.4%, whereby the response time delay was 36.9±13.0s and the subjects required approximately 12.3±4.4 s of active urging time to perform repeated attempts in order to turn on the switch in the online experiments. The aim of nerve conduction simulator software design is to create software that can be used by nerve conduction simulator to serve as a medical simulator or education tool to train novice physicians for nerve conduction study test. The real response waveform of 10 different upper limb nerves in conduction studies were obtained from the equipment used in real patient studies. A waveform generation model was built to generalize the response waveform near the standard stimulus site within study interest region based on the extracted waveforms and normal reference parameters of each study and stimulus site coordinates. Finally, based on the model, a software interface was created to simulate 10 different nerve conduction studies of the upper limb with 9 pathological conditions.

Cette thèse s'inscrit dans le cadre du projet Européen intégré VERE (Virtual Embodiement and Robotics re-Embodiement). Il s'agit de proposer une architecture logicielle intégrant un ensemble de stratégies de contrôle et de retours informationnels basés sur la "fonction tâche" pour incorporer (embodiment) un opérateur humain dans un humanoïde robotique ou un avatar notamment par la pensée. Les problèmes sous-jacents peuvent se révéler par le démonstrateur suivant (auquel on souhaite aboutir à l'issue de cette thèse). Imaginons un opérateur doté d'une interface cerveau-ordinateur ; le but est d'arriver à extraire de ces signaux la pensée de l'opérateur humain, de la traduire en commandes robotique et de faire un retour sensoriel afin que l'opérateur s'approprie le "corps" robotique ou virtuel de son "avatar". Une illustration cinématographique de cet objectif est le film récent "Avatar" ou encore "Surrogates". Dans cette thèse, on s'intéressera tout d'abord à certains problèmes que l'on a rencontré en travaillant sur l'utilisation des interfaces cerveau-ordinateur pour le contrôle de robots ou d'avatars, par exemple, la nécessité de multiplier les comportements ou les particularités liées aux retours sensoriels du robot. Dans un second temps, nous aborderons le cœur de notre contribution en introduisant le concept d'interface cerveau-ordinateur orienté objet pour le contrôle de robots humanoïdes. Nous présenterons ensuite les résultats d'une étude concernant le rôle du son dans le processus d'embodiment. Enfin, nous montrerons les premières expériences concernant le contrôle d'un robot humanoïde en interface cerveau-ordinateur utilisant l'électrocorticographie, une technologie d'acquisition des signaux cérébraux implantée dans la boîte crânienne. / This thesis is part of the European project VERE (Virtual Embodiment and Robotics re-Embodiment). The goal is to propose a software framework integrating a set of control strategies and information feedback based on the "task function" in order to embody a human operator within a humanoid robot or a virtual avatar using his thoughts. The underlying problems can be shown by considering the following demonstrator. Let us imagine an operator equipped with a brain-computer interface; the goal is to extract the though of the human operator from these signals, then translate it into robotic commands and finally to give an appropriate sensory feedback to the operator so that he can appropriate the "body", robotic or virtual, of his avatar. A cinematographic illustration of this objective can be seen in recent movies such as "Avatar" or "Surrogates". In this thesis, we start by discussing specific problems that we encountered while using a brain-computer interface for the control of robots or avatars, e.g. the arising need for multiple behaviours or the specific problems induced by the sensory feedback provided by the robot. We will then introduce our main contribution which is the concept of object-oriented brain-computer interface for the control of humanoid robot. We will then present the results of a study regarding the role of sound in the embodiment process. Finally, we show some preliminary experiments where we used electrocorticography (ECoG)~--~a technology used to acquire signals from the brain that is implanted within the cranium~--~to control a humanoid robot.

This thesis analyzes the market and products of brain-computer interface. Its main goal is evaluate the current market of these products and their use in information technologies and systems. The document is divided into three main parts. The first one focuses on familiarizing the reader with brain-computer interface technology. It mentions a brief history of development, technological principles, types of devices, their contemporary use, and the positives and negatives they bring. The second one focuses on market analysis. It summarizes the active companies on the market, and their products, and describes their customer targeting. It contains brief insight in market's future development. The third part focuses on practical testing of two selected brain-computer interface devices. The testing will evaluate applicability in information technologies and systems. For testing purpose will be developed its own methodology and selected appropriate evaluation criteria.

<p>Brain-machine interfaces (BMI) are systems which connect brains directly to machines or computers for communication. BMI-controlled prosthetic devices use algorithms to decode neuronal recordings into movement commands. These algorithms operate using models of how recorded neuronal signals relate to desired movements, called models of tuning. Models of tuning have typically been linear in prior work, due to the simplicity and speed of the algorithms used with them. Neuronal tuning has been shown to slowly change over time, but most prior work do not adapt tuning models to these changes. Furthermore, extracellular electrical recordings of neurons' action potentials slowly change over time, impairing the preprocessing step of spike-sorting, during which the neurons responsible for recorded action potentials are identified.</p> <p></p> <p>This dissertation presents a non-linear adaptive Bayesian filter and an adaptive spike-sorting method for BMI decoding. The adaptive filter consists of the n-th order unscented Kalman filter and Bayesian regression self-training updates. The unscented Kalman filter estimates desired prosthetic movements using a non-linear model of tuning as its observation model. The model is quadratic with terms for position, velocity, distance from center of workspace, and velocity magnitude. The tuning model relates neuronal activity to movements at multiple time offsets simultaneously, and the movement model of the filter is an order n autoregressive model.</p> <p>To adapt the tuning model parameters to changes in the brain, Bayesian regression self-training updates are performed periodically. Tuning model parameters are stored as probability distributions instead of point estimates. Bayesian regression uses the previous model parameters as priors and calculates the posteriors of the regression between filter outputs, which are assumed to be the desired movements, and neuronal recordings. Before each update, filter outputs are smoothed using a Kalman smoother, and tuning model parameters are passed through a transition model describing how parameters change over time. Two variants of Bayesian regression are presented: one uses a joint distribution for the model parameters which allows analytical inference, and the other uses a more flexible factorized distribution that requires approximate inference using variational Bayes.</p> <p>To adapt spike-sorting parameters to changes in spike waveforms, variational Bayesian Gaussian mixture clustering updates are used to update the waveform clustering used to calculate these parameters. This Bayesian extension of expectation-maximization clustering uses the previous clustering parameters as priors and computes the new parameters as posteriors. The use of priors allows tracking of clustering parameters over time and facilitates fast convergence.</p> <p>To evaluate the proposed methods, experiments were performed with 3 Rhesus monkeys implanted with micro-wire electrode arrays in arm-related areas of the cortex. Off-line reconstructions and on-line, closed-loop experiments with brain-control show that the n-th order unscented Kalman filter is more accurate than previous linear methods. Closed-loop experiments over 29 days show that Bayesian regression self-training helps maintain control accuracy. Experiments on synthetic data show that Bayesian regression self-training can be applied to other tracking problems with changing observation models. Bayesian clustering updates on synthetic and neuronal data demonstrate tracking of cluster and waveform changes. These results indicate the proposed methods improve the accuracy and robustness of BMIs for prosthetic devices, bringing BMI-controlled prosthetics closer to clinical use.</p> / Dissertation

Les Interfaces Cerveau-Machine (ICM) sont des systèmes qui permettent à des patients souffrant d'un handicap moteur sévère d'utiliser leur activité cérébrale pour contrôler des effecteurs, par exemple des prothèses des membres supérieurs dans le cas d'ICM motrices. Les intentions de mouvement de l'utilisateur sont estimées en appliquant un décodeur sur des caractéristiques extraites de son activité cérébrale. Des challenges spécifiques au déploiement clinique d'ICMs motrices ont été considérés, à savoir le contrôle mono-membre ou séquentiel multi-membre asynchrone et précis. Un décodeur, le Markov Switching Linear Model (MSLM), a été développé pour limiter les activations erronées de l'ICM, empêcher des mouvements parallèles des effecteurs et décoder avec précision des mouvements complexes. Le MSLM associe des modèles linéaires à différents états possibles, e.g. le contrôle d'un membre spécifique ou une phase de mouvement particulière. Le MSLM réalise une détection d'état dynamique, et les probabilités des états sont utilisées pour pondérer les modèles linéaires.La performance du décodeur MSLM a été évaluée pour la reconstruction asynchrone de trajectoires de poignet et de doigts à partir de signaux electrocorticographiques. Il a permis de limiter les activations erronées du système et d'améliorer la précision du décodage du signal cérébral. / Brain-Computer Interfaces (BCI) are systems that allow severely motor-impaired patients to use their brain activity to control external devices, for example upper-limb prostheses in the case of motor BCIs. The user's intentions are estimated by applying a decoder on neural features extracted from the user's brain activity. Signal processing challenges specific to the clinical deployment of motor BCI systems are addressed in the present doctoral thesis, namely asynchronous mono-limb or sequential multi-limb decoding and accurate decoding during active control states. A switching decoder, namely a Markov Switching Linear Model (MSLM), has been developed to limit spurious system activations, to prevent parallel limb movements and to accurately decode complex movements.The MSLM associates linear models with different possible control states, e.g. activation of a specific limb, specific movement phases. Dynamic state detection is performed by the MSLM, and the probability of each state is used to weight the linear models. The performance of the MSLM decoder was assessed for asynchronous wrist and multi-finger trajectory reconstruction from electrocorticographic signals. It was found to outperform previously reported decoders for the limitation of spurious activations during no-control periods and permitted to improve decoding accuracy during active periods.

abstract: This research is focused on two separate but related topics. The first uses an electroencephalographic (EEG) brain-computer interface (BCI) to explore the phenomenon of motor learning transfer. The second takes a closer look at the EEG-BCI itself and tests an alternate way of mapping EEG signals into machine commands. We test whether motor learning transfer is more related to use of shared neural structures between imagery and motor execution or to more generalized cognitive factors. Using an EEG-BCI, we train one group of participants to control the movements of a cursor using embodied motor imagery. A second group is trained to control the cursor using abstract motor imagery. A third control group practices moving the cursor using an arm and finger on a touch screen. We hypothesized that if motor learning transfer is related to the use of shared neural structures then the embodied motor imagery group would show more learning transfer than the abstract imaging group. If, on the other hand, motor learning transfer results from more general cognitive processes, then the abstract motor imagery group should also demonstrate motor learning transfer to the manual performance of the same task. Our findings support that motor learning transfer is due to the use of shared neural structures between imaging and motor execution of a task. The abstract group showed no motor learning transfer despite being better at EEG-BCI control than the embodied group. The fact that more participants were able to learn EEG-BCI control using abstract imagery suggests that abstract imagery may be more suitable for EEG-BCIs for some disabilities, while embodied imagery may be more suitable for others. In Part 2, EEG data collected in the above experiment was used to train an artificial neural network (ANN) to map EEG signals to machine commands. We found that our open-source ANN using spectrograms generated from SFFTs is fundamentally different and in some ways superior to Emotiv's proprietary method. Our use of novel combinations of existing technologies along with abstract and embodied imagery facilitates adaptive customization of EEG-BCI control to meet needs of individual users. / Dissertation/Thesis / Ph.D. Psychology 2013