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School of Biological and Behavioural Sciences

Alertness Monitoring during Human-Computer Interaction

Research environment

The School of Biological and Behavioural Sciences at Queen Mary is one of the UK’s elite research centres, according to the 2014 Research Excellence Framework (REF). We offer a multi-disciplinary research environment and have approximately 150 PhD students working on projects in the biological and psychological sciences. Our students have access to a variety of research facilities supported by experienced staff, as well as a range of student support services.

Dr Valdas Noreika leads a research program on the neurocognition of consciousness and drowsiness, using a range of cognitive neurosciences techniques, including electroencephalography and transcranial brain stimulation. He would support the experimental side of the project. Prof. Ioannis Patras directs a research lab focusing on the computer vision, machine learning and affective computing. He would support the computing side of the project.

Training and development

Our PhD students become part of Queen Mary’s Doctoral College which provides training and development opportunities, advice on funding, and financial support for research. Our students also have access to a Researcher Development Programme designed to help recognise and develop key skills and attributes needed to effectively manage research, and to prepare and plan for the next stages of their career.

The PhD student will also be individually trained by lab members in developing coding, statistics, cognitive neuroscience and experimental psychology knowledge and skills.

Project description

A drowsy neurologist going through slices upon slices of MRI could miss a dangerous tissue, whereas exhausted space safety personnel could issue a false missile alert, as happened in Hawaii in the early hours of January 13, 2018. The critical importance of alertness fluctuations is well recognized in traffic psychology and safety research. However, the prevalence and significance of drowsiness are much less understood in other human behaviour contexts, including human-computer interaction, where it can impact workplace safety, efficiency, and satisfaction.

In this project, a PhD student will carry out computational cognitive neuroscience studies aiming to identify behavioural markers of drowsiness and mental fatigue that can predict the performance of computer users. In Study 1 conducted in the electroencephalography (EEG) lab, participants will carry out the following tasks for a prolonged period: (i) speeded typing of text, (ii) fine navigation of mouse cursor, (iii) visual monitoring and target detection, and (iv) information search on the internet. Features extracted from the video camera, keyboard and mouse data will be used to train machine learning models to detect neurophysiologically defined fluctuations of alertness.

Study 2 will assess whether the identified behavioural features can predict drowsiness and mental fatigue while participants work with their computers in the office or at home. A generic model will be further individually adapted using federated learning. The project will provide the proof-of-the-concept for the alertness monitoring system that would signal computer users when they become too sleepy and inattentive to perform critical tasks.

For more information about the project, please contact Dr Noreika by email to 


This studentship is open to students applying for China Scholarship Council funding. Queen Mary University of London has partnered with the China Scholarship Council (CSC) to offer a joint scholarship programme to enable Chinese students to study for a PhD programme at Queen Mary. Under the scheme, Queen Mary will provide scholarships to cover all tuition fees, whilst the CSC will provide living expenses for 4 years and one return flight ticket to successful applicants.

Eligibility and applying

Applicants must:

  • Chinese students with a strong academic background.
  • Students must hold a PR Chinese passport.
  • Applicants can either be resident in China at the time of application or studying overseas.
  • Students with prior experience of studying overseas (including in the UK) are eligible to apply. Chinese QMUL graduates/Masters’ students are therefore eligible for the scheme.

Please refer to the CSC website for full details on eligibility and conditions on the scholarship.

Applications are invited from outstanding candidates with or expecting to receive a first or upper-second class honours degree in an area relevant to the project, e.g. computer science, psychology, neuroscience. A masters degree is desirable, but not essential. Experience in Python or Matlab coding is highly desirable, as would be a firm mathematical background.

Applicants are required to provide evidence of their English language ability. Please see our English language requirements page for details.

The deadline for applications to Queen Mary is 30th January 2022. Applicants will need to complete an online application form by this date to be considered, including a CV, personal statement and qualifications. Shortlisted applicants will be invited for a formal interview by the project supervisor. Those who are successful in their application for our PhD programme will be issued with an offer letter which is conditional on securing a CSC scholarship (as well as any academic conditions still required to meet our entry requirements).

Once applicants have obtained their offer letter from Queen Mary they should then apply to CSC for the scholarship by the advertised deadline with the support of the project supervisor. For September 2022 entry, applicants must complete the CSC application on the CSC website between 10th March - 31st March 2022.

Only applicants who are successful in their application to CSC can be issued an unconditional offer and enrol on our PhD programme.

Apply Online


  • Noreika, V., Kamke, M. R., Canales-Johnson, A., Chennu, S., Bekinschtein, T. A., & Mattingley, J. B. (2020). Alertness fluctuations when performing a task modulate cortical evoked responses to transcranial magnetic stimulation. NeuroImage, 223, 117305.
  • Mou, W., Gunes, H., & Patras, I. (2019). Alone versus in-a-group: A multi-modal framework for automatic affect recognition. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 15(2), 1-23.
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