I hope to use machine learning techniques to extract greater information from electroencephalographic signals gathered in visual psychophysics experiments, and so better inform models of cognition.
In the past, I have published work on neuropsychiatric genetics - Stewart 2009 SchzTGM2. I completed a neuroscience BSc at Edinburgh before being awarded an MSc for research on the use of calcium imaging to monitor the activity of custom wet neural networks. I am now a member of the Neuroinformatics Doctoral Training Centre, where I have worked on mathematical and computational approaches to neuroscience research and have recently completed an MSc on the use of graphics cards to greatly enhance the capability of real-time EEG analysis.
Current work applies this new capability to help tease apart the relationship between visual processing, attention and metrics of consciousness as can be assessed by EEG.
Neuronal gamma-band synchronisation is an intriguing putative neural mechanism (Fries_07_Gamma_Tins). EEG experimentation has the advantage of allowing this to be investigated investigated in awake humans. A recent study (Yuval_09_iGBR_Neuron) has suggested that one kind of the gamma related EEG signal may be artefacts of eye muscle movement. I plan to use powerful real time EEG analysis alongside eye tracking in order to untangle this, and believe this is an informative and accessible novel methodology for studying cognition in humans.