Developing new adaptive and machine learning algorithms to successfully decode brain signals across subjects

A brain decoding model aims at predicting what sensory stimulus is received (e.g. visual stimuli, different images), which mental state is experienced (i.e. asleep, awake, drowsy) or even what is the intention of the subject. The advancement in brain decoding models benefits the development of brain-machine interfaces, Neuroprosthetics and the understanding of neurological disorders such as epilepsy. To make a meaningful interface at the group level, developing generic brain decoding models that work well across different subjects is crucial. However, because of the structural and functional differences of the brain across subjects together with the inherent variability of the brain measurements due to, for example, changes in environmental variables, a certain degree of difference is expected between the brain signals of different subjects.

The aim of this project is developing new adaptive and machine learning algorithms to successfully decode brain signals across subjects. The prospective student will gain experience across different disciplines including engineering, and neuro-computation.