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Data Analytics for Steady-State Visual Evoked Potential-based Brain-Computer Interface


   Faculty of Engineering and Physical Sciences

   Applications accepted all year round  Competition Funded PhD Project (Students Worldwide)

About the Project

Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signal-to-noise ratio. The aim of this project is to develop data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate.


Funding Notes

Please visit our website for more information on funding opportunities that may be available for this project.

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