The role of simulation in developing and designing applications for 2-class motor imagery brain-computer interfaces

A Brain-Computer Interface (BCI) can be used by people with severe physical disabilities such as Locked-in Syndrome (LiS) as a channel of input to a computer. The time-consuming nature of setting up and using a BCI, together with individual variation in performance and limited access to end users makes it difficult to employ techniques such as rapid prototyping and user centred design (UCD) in the design and development of applications. This thesis proposes a design process which incorporates the use of simulation tools and techniques to improve the speed and quality of designing BCI applications for the target user group. Two different forms of simulation can be distinguished: offline simulation aims to make predictions about a user’s performance in a given application interface given measures of their baseline control characteristics, while online simulation abstracts properties of inter- action with a BCI system which can be shown to, or used by, a stakeholder in real time. Simulators that abstract properties of BCI control at different levels are useful for different purposes. Demonstrating the use of offline simulation, Chapter 3 investigates the use of finite state machines (FSMs) to predict the time to complete tasks given a particular menu hierarchy, and compares offline predictions of task performance with real data in a spelling task. Chapter 5 aims to explore the possibility of abstracting a user’s control characteristics from a typical calibration task to predict performance in a novel control paradigm. Online simulation encompasses a range of techniques from low-fidelity prototypes built using paper and cardboard, to computer simulation models that aim to emulate the feel of control of using a BCI without actually needing to put on the BCI cap. Chapter 4 details the develop- ment and evaluation of a high fidelity BCI simulator that models the control characteristics of a BCI based on the motor-imagery (MI) paradigm. The simulation tools and techniques can be used at different stages of the application design process to reduce the level of involvement of end users while at the same time striving to employ UCD principles. It is argued that prioritising the level of involvement of end users at different stages in the design process is an important strategy for design: end user input is paramount particularly at the initial user requirements stage where the goals that are important for the end user of the application can be ascertained. The interface and specific interaction techniques can then be iteratively developed through both real and simulated BCI with people who have no or less severe physical disabilities than the target end user group, and evaluations can be carried out with end users at the final stages of the process. Chapter 6 provides a case study of using the simulation tools and techniques in the development of a music player application. Although the tools discussed in the thesis specifically concern a 2-class Motor Imagery BCI which uses the electroencephalogram (EEG) to extract brain signals, the simulation principles can be expected to apply to a range of BCI systems

Efficient human-machine control with asymmetric marginal reliability input devices

Input devices such as motor-imagery brain-computer interfaces (BCIs) are often unreliable. In theory, channel coding can be used in the human-machine loop to robustly encapsulate intention through noisy input devices but standard feedforward error correction codes cannot be practically applied. We present a practical and general probabilistic user interface for binary input devices with very high noise levels. Our approach allows any level of robustness to be achieved, regardless of noise level, where reliable feedback such as a visual display is available. In particular, we show efficient zooming interfaces based on feedback channel codes for two-class binary problems with noise levels characteristic of modalities such as motor-imagery based BCI, with accuracy <75%. We outline general principles based on separating channel, line and source coding in human-machine loop design. We develop a novel selection mechanism which can achieve arbitrarily reliable selection with a noisy two-state button. We show automatic online adaptation to changing channel statistics, and operation without precise calibration of error rates. A range of visualisations are used to construct user interfaces which implicitly code for these channels in a way that it is transparent to users. We validate our approach with a set of Monte Carlo simulations, and empirical results from a human-in-the-loop experiment showing the approach operates effectively at 50-70% of the theoretical optimum across a range of channel conditions

Bladder cancer cells secrete while normal bladder cells express but do not secrete AGR2.

Anterior gradient 2 (AGR2) is a cancer-associated secreted protein found predominantly in adenocarcinomas. Given its ubiquity in solid tumors, cancer-secreted AGR2 could be a useful biomarker in urine or blood for early detection. However, normal organs express and might also secrete AGR2, which would impact its utility as a cancer biomarker. Uniform AGR2 expression is found in the normal bladder urothelium. Little AGR2 is secreted by the urothelial cells as no measurable amounts could be detected in urine. The urinary proteomes of healthy people contain no listing for AGR2. Likewise, the blood proteomes of healthy people also contain no significant peptide counts for AGR2 suggesting little urothelial secretion into capillaries of the lamina propria. Expression of AGR2 is lost in urothelial carcinoma, with only 25% of primary tumors observed to retain AGR2 expression in a cohort of lymph node-positive cases. AGR2 is secreted by the urothelial carcinoma cells as urinary AGR2 was measured in the voided urine of 25% of the cases analyzed in a cohort of cancer vs. non-cancer patients. The fraction of AGR2-positive urine samples was consistent with the fraction of urothelial carcinoma that stained positive for AGR2. Since cancer cells secrete AGR2 while normal cells do not, its measurement in body fluids could be used to indicate tumor presence. Furthermore, AGR2 has also been found on the cell surface of cancer cells. Taken together, secretion and cell surface localization of AGR2 are characteristic of cancer, while expression of AGR2 by itself is not

Efficient human-machine control with asymmetric marginal reliability input devices

Input devices such as motor-imagery brain-computer interfaces (BCIs) are often unreliable. In theory, channel coding can be used in the human-machine loop to robustly encapsulate intention through noisy input devices but standard feedforward error correction codes cannot be practically applied. We present a practical and general probabilistic user interface for binary input devices with very high noise levels. Our approach allows any level of robustness to be achieved, regardless of noise level, where reliable feedback such as a visual display is available. In particular, we show efficient zooming interfaces based on feedback channel codes for two-class binary problems with noise levels characteristic of modalities such as motor-imagery based BCI, with accuracy <75%. We outline general principles based on separating channel, line and source coding in human-machine loop design. We develop a novel selection mechanism which can achieve arbitrarily reliable selection with a noisy two-state button. We show automatic online adaptation to changing channel statistics, and operation without precise calibration of error rates. A range of visualisations are used to construct user interfaces which implicitly code for these channels in a way that it is transparent to users. We validate our approach with a set of Monte Carlo simulations, and empirical results from a human-in-the-loop experiment showing the approach operates effectively at 50-70% of the theoretical optimum across a range of channel conditions

Mindful parenting behaviors and emotional self-regulation in children with ADHD and controls

Mindfulness is defined as paying attention in a particular way: on purpose, in the present moment, and nonjudgmentally and these behaviors can be applied to parenting. Thus far, it is not understood whether mindful parenting (MP) differs in parents of children with and without attention-deficit/hyperactivity disorder (ADHD), and how MP relates to other parenting practices and children’s self-regulation.MethodsThis study examined the relationships between MP, parenting behaviors and children’s self-regulation in 120 families with child ADHD (85% male; mean age = 11.93) and 105 control families (62% male; mean age = 11.98). Parents completed measures of MP (Interpersonal Mindfulness in Parenting Scale), parenting behaviors (parenting warmth, consistency, and anger assessed with the Longitudinal Study of Australian Children measures), psychological distress (Kessler 6), and children’s self-regulation (Social Skills Improvement System—self-control subscale).ResultsWhen compared with controls, parents of children with ADHD reported significantly lower MP. Higher MP was associated with lower levels of parent psychological distress, higher levels of parenting warmth and consistency, lower levels of parenting anger, and higher child emotion self-regulation in both groups. In mediation analyses, MP was indirectly associated with child emotion self-regulation through lower parenting anger, with the model accounting for 55% of the variance in child self-regulation. ConclusionsMP is a useful construct for understanding parent behaviors, and children’s emotion self-regulation