Project description
Scalp recordings contain brain activity (EEG) and noise, particularly muscle artefact (electromyographic signals or EMG). Techniques for removing muscle artefact from brain signals exist, but it is hard to quantitate how well they perform. In recent years, we have collected data from humans with no EMG artefact (using temporary paralysis). This data set allows us to objectively compare artefact reduction algorithms, but there is still room for improvement with the algorithms. This project has a significant mathematical/analytical side, and requires matlab or python programming.
Supervisors research focus
My research is biomedical signal processing, particular focussed on the brain. Electroencephalography (EEG) allows us to measure brain waves with excellent temporal resolution, so we can see the rapid changes that happen as we think, experience and respond to the world around us. This gives us the chance to study a range of interests, such as disease diagnosis (differences in brains), and connectivity and consciousness (how the brain works as a whole). But there are many sources of noise to make this difficult. We have contributed substantially to artefact reduction algorithms, to enable exploration of high frequencies, or parts of the brain usually hidden by muscle artefact.
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