Investigating novel approaches to the real time classification of non-invasive brain recordings for use in assistive technologies

Brain Computer Interfaces (BCIs) are a form of Assistive Technology (AT) that can to be used by people with severe motor or sensory disability. However, with current non-invasive BCIs there is a need for the user to devote a significant level of attentive effort on the desired task which effectively abolishes the opportunity for them to multitask and engage in any other concurrent interactive behaviour. The need for high levels of attention to be directed the control of BCIs limits the opportunity for such devices to support user independence.
BCIs that are intuitive to use and require little user training are highly desirable for use in home environmental control, social interaction and engagement, navigating smart wheelchairs and instructing robot personal assistants.
In this project we will further advance our research into non-invasive, real time BCI controllers that exploit scalp recorded electroencephalographic (EEG) signals that can recognise signatures for intended motor actions and that are suited for integration into smart AT controllers with the potential for fast and robust event classification. Real time classification of user initiated brain signatures for event instructions to a BCI will require research on the signals themselves, the signal processing algorithms that are best suited to classification in real time, the computer architectures to achieve this and development of semi-autonomous AT that will enable the user to be less attentive to the BCI/AT itself. Accordingly, the objective of this project is to develop and implement shared (collaborative) control strategies with the aim to reducing cognitive load and attention of users of BCI linked ATs.
The project will develop research skills in real time signal capture, processing and classification of human neurophysiological recordings. The implementation of artificial intelligence into an existing model system of an advanced assistive device simulator (electric wheelchair) in order to allow virtual testing of controllers in which the BCI element and the device have shared control based on 1) the robust and rapid detection of intention events (BCI) and 2) the translation of these commands into goal orientated high, level requests (navigation) to the semi-autonomous AT device. The existing simulator will provide the foundation for development of the shared control strategies for this work.
The ultimate goal will be to implement these strategies onto an actual AT and test its performance in real world situations.