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PhD Effective EEG analysis for advanced AI-driven BMI/BCI systems

A 36-month full-time, fully funded PhD project aiming to analyse and understanding EEG signals and how they are linked to our human brain activities. Modelling such mechanisms can further help strengthen BCI and BMI systems.

Number of places



Home fee, Travel costs, International fee, Stipend, Equipment costs


18 January 2018


31 August 2018


3 years


To be considered for the position, the candidate must:

  • Possess, or be about to obtain, an Upper second (2.1) UK BEng Hons or MEng degree in a relevant engineering or computer science related subject
  • Adhere to Research Council (RCUK) eligibility criteria if a UK or EU national
  • Have skills and understanding of Matlab data analysis
  • Knowledge and experience in statistical data analysis and mathematical modelling
  • Expertise in experimental research in signal/image analysis
  • Strong English language capability in oral/writing communication

Eligibility for RCUK studentships

  • Research Council (RC) fees and stipend can only be awarded to UK and EU students and not to EEA or International students.
  • EU students are only eligible for RC stipend if they have been resident in the UK for 3 years, including for study purposes, immediately prior to starting their PhD.
  • If an EU student cannot fulfil this condition then they are eligible for a fees only studentship.
  • International students cannot be funded from RC funds unless they are ‘settled’ in the UK. ‘Settled’ means being ordinarily resident in the UK without any immigration restrictions on the length of stay in the UK. To be ‘settled’ a student must either have the Right to Abode or Indefinite leave to remain in the UK or have the right of permanent residence in the UK under EC law. If the student’s passport describes them as a British citizen they have the Right of Abode.
  • Students with full Refugee status are eligible for fees and stipend.

Project Details

In this multi-disciplinary project, advanced signal processing and machine learning algorithms will be developed for effective processing and understanding of EEG signals. The developed system aims to provide fast and accurate detection and classification of various human behaviour events and fulfil the needs of a wide range of real-time applications. Consequently, we aim to investigate effective solutions and software tools in this area to benefit future AI-driven BMI/BCI systems. 

As an efficient modality to acquire brain signals corresponds to various states from the scalp surface area, EEG signals may have multiple channels (from 10s to 100s) are generally categorized into various signal frequencies ranges from 0.1 Hz to more than 100 Hz. These time-varying and non-stationary signals can be very weak and hidden in/embedded with other signals, where propagation noise may also lead to interference and degradation in between. As a result, there are a number of challenging issues to be addressed in this project.

This project primarily focuses on EEG signals and its characterization with respect to various states of human body. The main objectives are summarised as follows:

  • To develop effective models for signal enhancement from background noise using singular spectrum analysis (SSA) and blind signal detection techniques etc;
  • Based on sparse representation and independent component analysis (ICA) to develop models/tools for separating the interference EEG signals;
  • Using advanced feature extraction and machine learning tools such as deep learning network for characterising the EEG signals and associated with semantic states/events;
  • To develop visualisation models for visualisation of the data and analysed results in a dynamic and interactive way;
  • To propose approaches for quality assessment of EEG signals and assessment the developed models on publicly available datasets with specific applications;
  • Two optional tasks for further study according to the progress of the project: one is fusion with other modalities of data for more comprehensive behaviour modelling of brain, and the other is compressive sensing for dimensionality reduction in optimised data acquisition. 







The project will start on 1 October 2018.

Funding Details

Funding is provided for full tuition fees (UK & EU applicants only), plus a generous tax-free stipend of between £13,000 - £14,000 per year. Essential costs for travel and small equipment will be covered. 

Well qualified International (Non-EU) candidates are welcome to apply for the position, but funding is available for tuition fees only. Additional funding sources will need to be identified for the stipend and other costs.


Contact us

How to apply

Interested candidates are encouraged to first email Dr Ren for informal discussions. Thereafter, they should submit their CV, official transcripts and a covering letter outlining their suitability for the position. The candidate is also expected to specify a rough plan how to commit to the project and achieve the planed objectives.

Following review of the application submissions, selected candidates will be invited for interview.

The deadline for applications is 31 August 2018.

The project will start on 1 October 2018.