Computational Intelligence for Brain-Computer Interfaces

Closing Date

31st December 2021*

Applicants should contact the primary supervisor, and submit their Expression of Interest (EOI) and Application as soon as possible.

*unless filled earlier

The Research Project

Given the important challenges associated with the processing of brain signals obtained from neuroimaging modalities, fuzzy sets, neural networks and evolutional systems have been proposed as a useful and effective framework for the modelling and understanding of brain activity patterns as well as to enable a direct communication pathway between the brain and external devices (brain computer/machine interfaces). However, most of the research so far has focused on lab-based applications in constrained scenarios, which cannot be extrapolated to realistic field contexts. Considering the decoding of brain activity, the computational Intelligence models, including fuzzy sets, neural networks, and evolutional computation, provide an excellent tool to overcome the challenge of learning from brain activity patterns that are very likely to be affected by non-stationary behaviours and high uncertainty. The application of computational Intelligence methods to learning and modeling​ has recently demonstrated its remarkable usefulness for coping with the effects of extremely noisy environments, as well as the variability and dynamicity of brain signals. Additionally, neurobiological studies have suggested that the behaviour of neural cells exhibits functional patterns that resemble the properties of intelligent computation to encode logical perception. This paves the way for developing new computational intelligence techniques based on intelligence abstractions that foster the capabilities for modeling and understanding brain function from a quantitative point of view.


Please refer to the Entry Requirements for a {Doctor of Philosophy/Master of Research} degree.

The following eligibility criteria also apply:

  • The project is open to domestic and international candidates
  • The PhD must be undertaken on a full-time basis
  • Applicants must already have been awarded a first class Honours degree or hold equivalent qualifications or relevant and substantial research experience in an appropriate sector

Selection Criteria

Knowledge and skills that will be ranked highly include:

  • Applicants must be able to demonstrate strong research and analytical skills
  • Data Mining and Predictive Analytics Skills
  • Foundational programming skills
  • Statistics

More Information

Please contact Zehong Cao for more information.