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Brain Computer Interfaces (BCls) are an emerging area of research combining the Neuroscience, Computer Science, Engineering, Mathematics, Human Computer Interaction and Psychology research fields. A BCI enables an individual to exert control of a computer without activation of the efferent nervous system or the muscles. This allows individuals suffering with partial or complete paralysis and associated conditions which prevent muscle movement to control a computer and hence communicate and exert control over their environment. This thesis first investigates tools for automatically removing artifacts from the Electroencephalogram (EEG), a signal commonly used in the control a BCI. Tools for measuring inter-regional connectivity patterns within the brain via phase synchronisation are then evaluated and extended to provide novel measures of inter-regional connectivity across the entire cortex. Feature selection approaches are then introduced and evaluated before being applied to select good feature sets for the discrimination of connectivity patterns. These approaches are compared to Markov modeling approaches which model and classify temporal dependencies in the data. The resulting tool-set is applied to a novel BCI control paradigm based upon the detection of single finger taps. It is demonstrated that the connectivity features produce significantly better classification accuracies than can be achieved using conventional features traditionally applied in BCI.

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This thesis focuses on the development of data-driven multivariate and multiscale methods for brain computer interface (BCI) systems. The electroencephalogram (EEG), the most convenient means to measure neurophysiological activity due to its noninvasive nature, is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its multichannel recording nature require a new set of data-driven multivariate techniques to estimate more accurately features for enhanced BCI operation. Also, a long term goal is to enable an alternative EEG recording strategy for achieving long-term and portable monitoring. Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary EEG signal into a set of components which are highly localised in time and frequency. It is shown that the complex and multivariate extensions of EMD, which can exploit common oscillatory modes within multivariate (multichannel) data, can be used to accurately estimate and compare the amplitude and phase information among multiple sources, a key for the feature extraction of BCI system. A complex extension of local mean decomposition is also introduced and its operation is illustrated on two channel neuronal spike streams. Common spatial pattern (CSP), a standard feature extraction technique for BCI application, is also extended to complex domain using the augmented complex statistics. Depending on the circularity/noncircularity of a complex signal, one of the complex CSP algorithms can be chosen to produce the best classification performance between two different EEG classes. Using these complex and multivariate algorithms, two cognitive brain studies are investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user attention to a sound source among a mixture of sound stimuli, which is aimed at improving the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments elicited by taste and taste recall are examined to determine the pleasure and displeasure of a food for the implementation of affective computing. The separation between two emotional responses is examined using real and complex-valued common spatial pattern methods. Finally, we introduce a novel approach to brain monitoring based on EEG recordings from within the ear canal, embedded on a custom made hearing aid earplug. The new platform promises the possibility of both short- and long-term continuous use for standard brain monitoring and interfacing applications.

This thesis describes research which explores the importance and feasibility of involving older people as equal partners in the creative design of digital devices for an ageing population. In exploring this topic, I have carried out two preliminary studies, a pilot study and a major empirical study. Firstly, I invited three groups of people, including very old people, active older people and postgraduate students, to evaluate a mock-­‐up model of an interactive device intended for older people that was designed using a standard design process. The results of this study suggested that products without an adequate contribution from older people would not always meet their needs. Secondly, I carried out observations of very old people, active older people, and young designers to identify factors that influence the way in which both older people and young designers can be involved in the creative design process. These factors included experiences with technology, processes and approaches currently applied with older people and designers, factors that stimulate or inhibit creativity, and practical constraints such as health issues. The results of these observations fed into the design of a pilot study, where I tested the content of a creative design process and a procedure for analysing data for the main empirical study. The main study involved three creative workshops where the same creative methods were employed with different sets of people: young designers, mixed groups (with older people and designers) and older people only. The results show that older people are able to participate in a creative design process; however, certain practical constraints have to be taken into account. Also, older people perform better when they work together with designers. Finally, the mixed groups with older people, who have relevant life experiences, and designers, who are familiar with the newest technology, may be more suitable for designing appropriate products for the older population.