Project description

Brain Computer Interfaces (BCI) using electroencephalography (EEG) have been explored as an assistive technology solution, particularly for people with no or very limited motor capability. Typically, BCIs are designed for a person and their current capabilities. With neurodegenerative diseases (eg motor neurone disease), there is a loss of motor capability over time. Can we make use of motor capability while it is still present to design better BCIs for when there is much less or no motor capability? Using EEG from scalp electrodes, we can record brain activity while the participant is making choices at different stimuli. Each participant will perform the task three times with different amounts of actual movement: (1) some, (2) little and (3) no movement. Different BCIs can be designed based on using one or more of the movement datasets. In particular, does knowledge of (1) and/or (2) let us design a better BCI than just using (3)? The research question is therefore to see if we can proactively design BCIs using current motor capability that will perform better as motor capability degenerates.

Assumed knowledge

Some familiarity or comfort with coding in matlab or python

Supervisors research focus

Biomedical signal processing

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