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.

BACKGROUND: Nutrition, diet, and fitness are among the most searched health topics by internet users. Besides that, health-related internet users are diverse in their motivations and individual characteristics. However, little is known about the individual characteristics associated with the usage of nutrition, weight loss, and fitness websites. OBJECTIVE: The aim of this study was to examine the individual factors associated with the usage of nutrition, weight loss, and fitness websites. METHODS: An invitation to an online survey was published on 65 websites and discussion forums. In total, we employed data from 623 participants (aged 13 to 39 years, mean 24.11 [SD 5.26]). The measures included frequency of usage of nutrition, weight loss and fitness websites, excessive exercise, eating disorder symptomatology, internalization of the beauty ideal, weight status, and perceived online social support. Participants' data were used as predictors in a base linear regression model. RESULTS: The final model had an acceptable fit (χ210 =14.1; P=.17; root mean square error of approximation=0.03; comparative fit index=0.99; Tucker-Lewis index=0.99). Positive associations were found between usage of (1) nutrition websites and being female, higher levels of excessive exercise, and perceived online social support; (2) weight loss websites and excessive exercise, internalization, being female, eating disorder symptomatology, and being overweight or obese; and (3) fitness websites and levels of excessive exercise, internalization, and frequency of internet use. CONCLUSIONS: The results highlighted the importance of individual differences in the usage of health-related websites. / Revisón por pares

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.

Functional neuroimaging techniques such as near-infrared spectroscopy (NIRS) have been studied in brain-computer interface (BCI) development. Previous research has suggested that the addition of a second brain-monitoring modality may improve the accuracy of a NIRS-BCI. The objective of this study was to determine whether the classification accuracies achievable by a multimodal BCI, which combines NIRS and transcranial Doppler ultrasonography (TCD) signals, can exceed those attainable using a unimodal NIRS-BCI or TCD-BCI. Nine able-bodied subjects participated in the study. Simultaneous measurements were made with NIRS and TCD instruments while participants were prompted to perform a verbal fluency task or to remain at rest, within the context of a block-stimulus paradigm. In five of nine (55.6%) participants, classification accuracies with the NIRS-TCD system were significantly higher (p<0.05) than with NIRS or TCD systems alone. Our results suggest that multimodal neuroimaging may be a promising approach towards improving the accuracy of future BCIs.

Functional neuroimaging techniques such as near-infrared spectroscopy (NIRS) have been studied in brain-computer interface (BCI) development. Previous research has suggested that the addition of a second brain-monitoring modality may improve the accuracy of a NIRS-BCI. The objective of this study was to determine whether the classification accuracies achievable by a multimodal BCI, which combines NIRS and transcranial Doppler ultrasonography (TCD) signals, can exceed those attainable using a unimodal NIRS-BCI or TCD-BCI. Nine able-bodied subjects participated in the study. Simultaneous measurements were made with NIRS and TCD instruments while participants were prompted to perform a verbal fluency task or to remain at rest, within the context of a block-stimulus paradigm. In five of nine (55.6%) participants, classification accuracies with the NIRS-TCD system were significantly higher (p<0.05) than with NIRS or TCD systems alone. Our results suggest that multimodal neuroimaging may be a promising approach towards improving the accuracy of future BCIs.

This dissertation examines the theory and praxis of taking an expanded concept of the human-computer interface (HCI) and working with the resulting concept to design a writing center website that facilitates online tutoring while fostering a conversational approach for online tutoring sessions. In order to foster a conversational approach, I explore the ways in which interactive digital technologies support the collaborative and communicative nature of online tutoring. I posit that my research will yield a deeper understanding of the visual rhetoric of human-designed computer interfaces in general and writing center online tutoring websites in particular, and will, at the same time, provide support and rationale for the use of interactive digital technologies that utilize the space within the interface to foster a conversational approach to online tutoring, an outcome that the writing center community strongly encourages but acknowledges is difficult to achieve in online tutoring situations (Bell, Harris, Harris and Pemberton, Gillespie and Lerner, Hobson, Monroe, Rickley, Thomas et. al). The resulting prototype design that I submit as part of this dissertation was developed by considering the surface and conceptual dimensions of the HCI along with pedagogies that support interactivity, exploration, communication, collaboration, and community.

The process of patient care relies on clinical data spread across specialized hospital departments. Powerful software is being designed to assimilate this disconnected patient data before treatment can be decided. However, these data are often presented to clinicians on interfaces that do not fit clinical workflows, leading to poor operational efficiency and increased patient safety risks. This project relies on ethnographic design methods to create evidence of clinician preferences pertaining to the presentation and collection of information on user interfaces in patient rooms. Using data gathered in clinical observation, a prototype interface was designed to enable doctors to conduct clinical tasks through a usable patient room interface. The prototype evaluation with doctors identified clinical tasks that are relevant in the patient room and provided insight into the perceived usability of such an interface. The evaluation sessions also elucidated on issues of patient-centeredness in technology design, effortless authentication and interface customizability.