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Model-based depth of anaesthesia monitoring

Primary supervisor

Levin Kuhlmann

This project involves model-based depth of anaesthesia monitoring using autoregressive moving average modelling and neural mass and neural field modelling of the electroencephalographic (EEG) signal. This will be achieved through frequency domain and time domain state and parameter estimation techniques to infer model states and parameters in real time to simultaneously track the anaesthetic brain states while inferring underlying physiological changes.

Required knowledge

Machine learning, dynamical systems theory, control theory, signal processing, time series analysis, neuroscience are all relevant and a student should have strong knowledge in at least one of these areas and be keen to learn the rest.

Learn more about minimum entry requirements.