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Development of a novel approach to neurophysiological pain assessment in unresponsive patients


About This PhD Project

Project Description

• Basic Funding details – Full time Home/EU fees and a stipend of £15,009 p.a.
• Application deadline – Tuesday 7th May 2019
• Start date – October 2019
• Duration – 3 years full-time
• Location – School of Computer Science and Electrical Engineering and Department of Psychology, University of Essex, Colchester, Essex, UK.

Overview:


Improvements in intensive care have led to an increasing number of patients surviving traumatic brain injuries. Due to their injuries these patients may be unable to self-report pain. Vital signs are not a reliable primary indicator of pain and methods such as functional magnetic resonance imaging cannot be applied at the bedside. Thus, the goal of this project is the development of a novel method based on the online analysis of electroencephalography (EEG) data to determine if a patient is experiencing pain. The successful applicant will (1) investigate the influence of the tonic experience of pain on neurophysiological markers extracted from the EEG (2) implement the detection of the neurophysiological markers of pain in a signal processing pipeline, based on brain-computer interface (BCI) technology that can be deployed at the bedside, for the online detection of pain.

Detailed funding information – The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (see https://www1.essex.ac.uk/fees-and-funding/research/default.aspx 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.

Supervisors


Overview: The lead supervisor is an expert in brain-computer interfacing and the online analysis of neurophysiological signals in clinical settings. The co-supervisor is an expert in pain science and the induction of experimental pain. Together they will provide the PhD student with an ideal training environment in experimental design and data collection, online and offline analysis of neurophysiological data as well as guide the student through the process of disseminating the results of the project via scientific publication in high-ranking peer-reviewed journals.

Lead Supervisor
Dr Sebastian Halder, Lecturer in Brain-Computer Interfacing, School of Computer Science and Electrical Engineering. Dr. Sebastian Halder has been at the University of Essex since January 2019. Previously he worked in Germany, Japan and Norway. His research is focussed on improving the efficiency and understanding of brain-computer interfaces (BCIs): devices that establish a direct connection between the brain and an external device. To accomplish this the complex signals recorded from the brain must be analysed and classified online using signal processing algorithms and machine learning. His current projects include using BCIs for communication, the neural mechanisms of BCI usage and the detection of mental states such as awareness or the sensation of pain. Dr. Halder’s work has been published in 42 international journal articles that have been cited 4493 times (h-index 31).

Co-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.

Criteria:


The successful candidate should possess a (i) Master level degree with a strong background in programming (Matlab, Python and ideally C++) and neurophysiological data analysis, (ii) experience in the collection of neurophysiological data such as EEG, (iii) strong motivation to be involved in a project with a clinical background and learning advanced neuroscience data analysis skills, (iv) proficiency in spoken and written English.

Candidates will be asked to provide:
• Transcripts
• Covering letter and detailed CV
• Personal statement
• One reference letter

For more information and details on how to apply please follow this link https://www.essex.ac.uk/postgraduate-research-degrees/opportunities/neurophysiological-pain-assessment-in-unresponsive-patients

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