Computational modelling of EEG data from visual scene analysis

Contemporary EEG techniques focus on a range of statistical methods, such as fitting general linear models, correlations, time-frequency analysis, dynamic causal models, etc. However, there has been less work on connecting EEG signals directly to computational models of cognitive abilities. Such models are similar to methods in artificial intelligence (AI) which aim at mimicking human cognitive abilities (e.g. playing and winning the ancient board game, GO). The aim of this PhD project is to develop a novel method that tests such computational models by benchmarking them against EEG signals. In other words, this novel method will allow us to establish more directly links between human cognition and EEG signals than contemporary EEG methods. Consequently, we will be able to advance our understanding of neural mechanisms underlying cognitive abilities. As a test case for the new method the project will use the human visual system.
Further information can be found here:
https://warwick.ac.uk/fac/cross_fac/mibtp/pgstudy/phd_opportunities/neuroscience2018/cognitive

Applicants should have a background in computational modelling, neuroscience, computer science, psychology, physics or related areas. Prior experience in statistical analysis and/or machine learning would be an advantage.
The project will be based at the School of Psychology and the Computational Neuroscience and Cognitive Robotics Centre of the University of Birmingham, UK. The centre provides an excellent multidisciplinary, interactive and collaborative research environment combining expertise in cognitive neuroimaging, psychophysics and computational neuroscience. The psychology department was rated 5th in the UK research assessment exercise.

The application deadline is the 7th January 2018. The starting date is Sept/Oct 2018.
For further information click on
https://www2.warwick.ac.uk/fac/cross_fac/mibtp/pgstudy/phd_opportunities/application/

or email: Dr Dietmar Heinke, [Email Address Removed]