An Optimal Transport Based Transferable System for Detection of Erroneous Somato-Sensory Feedback from Neural Signals

This study is aimed at the detection of single-trial feedback, perceived as erroneous by the user, using a transferable classification system while conducting a motor imagery brain–computer interfacing (BCI) task. The feedback received by the users are relayed from a functional electrical stimulation (FES) device and hence are somato-sensory in nature. The BCI system designed for this study activates an electrical stimulator placed on the left hand, right hand, left foot, and right foot of the user. Trials containing erroneous feedback can be detected from the neural signals in form of the error related potential (ErrP). The inclusion of neuro-feedback during the experiments indicated the possibility that ErrP signals can be evoked when the participant perceives an error from the feedback. Hence, to detect such feedback using ErrP, a transferable (offline) decoder based on optimal transport theory is introduced herein. The offline system detects single-trial erroneous trials from the feedback period of an online neuro-feedback BCI system. The results of the FES-based feedback BCI system were compared to a similar visual-based (VIS) feedback system. Using our framework, the error detector systems for both the FES and VIS feedback paradigms achieved an F1-score of 92.66% and 83.10%, respectively, and are significantly superior to a comparative system where an optimal transport was not used. It is expected that this form of transferable and automated error detection system compounded with a motor imagery system will augment the performance of a BCI and provide a better BCI-based neuro-rehabilitation protocol that has an error control mechanism embedded into it

Improving Group Decision Making with Collaborative Brain-Computer Interfaces

Groups are generally superior to individuals in making decisions. However, time constraints and authoritarian leaders could nullify the potential advantages provided by groups. This thesis proposes a hybrid collaborative Brain-Computer Interface (cBCI) for improving performance in group decision-making. Neural signals recorded via electroencephalography are integrated with other physiological and behavioural measures to predict the likelihood of the user being correct in a decision, i.e., decision confidence. Behavioural responses from multiple users are then weighed according to these confidence estimates to obtain group decisions. The proposed cBCI has been tested with a variety of decision-making tasks, including visual matching, visual search with traditional and realistic stimuli, face recognition from multiple viewpoints, and speech perception. Groups assisted by the cBCI were significantly superior in making decisions than both individuals and traditional equally-sized groups making decisions using the majority method. This thesis also investigates the impact that a constrained form of communication has on individual and group performance in a visual-search experiment. When decision makers are able to exchange information during the experiment, their performance dramatically decreases. However, the cBCI yields superior group decisions even in this context. The confidence estimated by the cBCI is also a more reliable predictor of correctness than the confidence reported by participants after making a decision. When group members were allowed to communicate during visual search, their reported confidence was totally unrelated to the decision correctness, while in a speech perception task reported confidences were very good predictors of correctness. On the contrary, the cBCI?s confidence estimates correlated with correctness in all experiments. When critical decisions involving substantial risks have to be made (e.g., in defence), the proposed cBCI could be a useful tool to reduce the number of erroneous group decisions, thereby saving money and lives

Occupant-Facade interaction: a review and classification scheme

The interest in occupant interaction with building controls and automation systems is growing due to the wider availability of embedded sensing devices and automated or intelligent building components that can integrate building control strategies with occupant-centred data and lead to greater occupant satisfaction and reduction in energy consumption. An area of particular interest is the interaction strategies between occupants and the so called automated facades, such as dynamic shading devices and switchable glazing. Occupant-Facade interactions are often disruptive and source of dissatisfaction because of conflicts between competing requirements, e.g. energy-efficiency and indoor environmental quality. To solve these conflicts, expertise from several disciplines is required, including Behavioural Science and Building Physics, but the absence of common research frameworks impedes knowledge transfer between different fields of expertise. This paper reviews existing multi-disciplinary research on occupant interaction with facades, buildings and automation systems and provides a new classification scheme of Occupant-Facade interaction. The scheme is based on an extensive review of interactive scenarios between occupants and facades that are summarised in this paper. The classification scheme was found to be successful in: 1) capturing the multidisciplinary nature of interactive scenarios by clarifying relationships between components; 2) identifying similarities and characteristics among interactive scenarios; 3) understanding research gaps. The classification scheme proposed in this paper has the potential to be a useful tool for the multi-disciplinary research community in this field. The review also showed that more research is needed to characterise the holistic and multi-disciplinary effect of occupant interaction with intelligent building components.EPSRC Doctoral Training Accoun

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers