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Towards a novel biomarker for pain: advanced analysis of oscillatory brain activity in healthy and chronic pain individuals

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  • Full or part time
    Dr E Valentini
    Dr C Antonopoulos
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

About This PhD Project

Project Description

• Basic Funding details – Full time Home/EU fees and a stipend of £15,009 p.a.
• Application deadline – Friday 16th August 2019
• Start date – October 2019
• Duration – 3 years full-time
• Location – Department of Psychology and Department of Mathematical Sciences, University of Essex, Colchester, Essex, UK.

Interviews will be taking place on 27 August 2019


The present proposal focuses on electroencephalography (EEG) oscillations as a brain biomarker of pain, both in healthy individuals and chronic-pain patients. This project is a step forward as it proposes to study whether EEG oscillations can: (a) predict the unpleasantness of prolonged pain in healthy individuals and (b) predict ongoing pain in chronic patients. The project’s impact stems from the combined use of psychophysiological and mathematical tools to study EEG data (i.e. advanced machine learning, brain network theory and connectivity analysis – see figure below for an example of data analysis approach) of healthy individuals and chronic-pain patients. The collaboration with Prof. Bhaskar Dasgupta (Consultant lead of Rheumatology and Clinical Director of Research & Audit, Southend University Hospital NHS Foundation Trust in Essex) will provide us with data from a large pool of patients.

Detailed funding information – The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (see for further fee details), a doctoral stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£15,009 in 2019-20), plus £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.


The lead supervisor is an expert in pain science and the study of brain activity by means of EEG while Dr Antonopoulos is an expert in computational neuroscience and network inference. The co-supervisor will provide support and guide the student through the most advanced data processing aspects of the work. In combination, they will train the student in literature review, experiment design, data interpretation and EEG data collection, advanced machine learning and network inference.

Primary supervisor:

Dr Elia Valentini - Lecturer in Psychology and brain sciences, Department of Psychology

Dr Elia Valentini has been at the University of Essex since September 2015, where he is teacher and researcher at the Department of Psychology and Centre for Brain Science (CBS). His current research investigates how people perceive negative valence information, how they interpret both physical and psychological events as threatening. Current projects involve measuring electroencephalography and other psychophysiological measures, subjective reports (i.e. from sensory ratings to personality questionnaires), cognitive and behavioural performance in a multisensory setting. He has published 35 papers in international journals. His h-index is 16 and has 765 citations to his work.


Dr Chris Antonopoulos MIMA - Lecturer in Applied Mathematics, Department of Mathematical Sciences

Dr Chris Antonopoulos has been at the University of Essex since September 2015, where he is involved in teaching students from the Department of Mathematical Sciences and Essex Business School, as well as carrying out research in applied mathematics, dynamical systems, chaos theory, complex systems and networks, and computational neuroscience. He has published 35 papers in international journals and 8 papers in conference proceedings and chapters in books. His h-index is 16 and has 915 citations to his work.


The successful candidate should possess:
- A Masters degree with relevant experience in the analysis of multivariate psychological and physiological data
- Good skills in programming (particularly Matlab and Python) and advanced statistical analyses
- Strong motivation for the project and its basic and applied health-related aspects
- Excellent organisational and communication skills

Desirable criteria:
- Proficiency in English language
- A background in mathematics at either undergraduate or postgraduate level
- Knowledge of available EEG data analysis tools
- Experience on EEG source localisation and connectivity analyses
- While experience with EEG is desirable, full training can be provided

Candidates will be asked to provide:
• Transcripts
• Detailed CV
• Personal statement
• One reference letter

For more information and details on how to apply please follow this link

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