Real-time neurofeedback system for children with attention deficit hyperactivity disorder (ADHD)

About the Project

Project Summary

Current approaches for treating attention deficit hyperactivity disorder (ADHD) in children mostly rely on cognitive therapy and medication. This project brings together neuroscience and psychology with machine learning and engineering to recast the problem in terms of neurofeedback control. This novel approach holds promise for delivering safe and effective therapy.

Project Details

Background

ADHD in children is a leading neurodevelopmental disorder, often recognised as one of the most common causes for referral to child psychology and psychiatric clinics. Affecting about one in 20 children [1], it manifests through persistent, impairing levels of inattention, hyperactivity, and impulsivity. ADHD treatment for children typically involves a behavioural therapy and, if necessary, medication.

These therapies are not always effective or convenient: behavioural interventions require frequent attendance at multiple sessions and are often subject to long waiting lists, while pharmacological treatments can lead to undesirable side effects. Thus, there is an urgent need to develop personalised therapeutic approaches that can be delivered in home or community settings.

Neurofeedback intervention has been suggested as an alternative treatment for children with ADHD [2, 3]. However, the proposed protocols are often based on spatially imprecise measurements of neural activity, especially using electroencephalography (EEG), and the use of overly simplistic data analysis methods. Furthermore, these interventions show substantial variability in their outcomes, largely due to the limited understanding of the neural mechanisms underlying ADHD.

This research aims to develop a neurofeedback system that utilises comprehensive mapping of neural activity using the more spatially precise optically pumped magnetoencephalography (OPM), combined with advanced machine learning methods and a novel gamified paradigm. The system has the potential to alleviate ADHD symptoms in children by directly modulating the neural mechanisms underlying this condition.

Research Challenges

Currently, the neural mechanisms underlying ADHD in children poorly understood. The existing MEG/EEG datasets are often limited to basic experimental conditions which impede the exploration and understanding of the complex neural mechanisms.

Current neurofeedback approaches utilise limited spatial and spectral characteristics of neural activity and largely ignore changes in neural activity caused by the stimulation itself (i.e., not adaptive). Moreover, the methodologies fail to incorporate recent advances in brain imaging technologies, computational modelling, and machine learning.

Research plan

• WP1: Acquire OPM data in children with and without ADHD while playing a video game involving attentional switching between visual and auditory stimuli.
• WP2: Develop machine learning models to detect specific neural patterns associated with difficulties of attentional switching (e.g., delayed or missed response) between visual and auditory modalities.
• WP3: Develop a neurofeedback system that tracks neural activity in real-time and, using machine learning, sends a feedback signal to the user when the error-associated neural pattern is detected. Attention behaviour in the game will be measured to assess improvement.

Novelty

The proposed neurofeedback system leverages state‑of‑the‑art OPM, supported by a multimillion‑pound investment from Aston University and the Institute for Health and Neurodevelopment. This technology will be integrated with advanced signal processing and machine learning techniques, enabling a robust, evidence based approach grounded in a deep understanding of the neural mechanisms underlying ADHD. The project’s close partnership with Birmingham Community Healthcare NHS Foundation Trust ensures a strong pathway to real world impact and meaningful societal benefits.

Person Specification

Candidates should have been awarded, or expect to achieve, EITHER:

a] a First or Upper Second Class award in their undergraduate degree, in a relevant subject.

OR

b] a First or Upper Second Class award in their undergraduate degree, and a Merit or Distinction in a Masters degree, both in a relevant subject.

Qualifications from overseas institutions will be considered, but performance must be equivalent to that described above, and the University reserves the right to ascertain this equivalence according to its own criteria.

Desirable / Essential Skills or Experience

Essential Skills and Experience

• Experience writing reports, essays, dissertations, or research proposals
• Strong programming skills in MATLAB/Python
• Strong skills in data collection and analysis
• Proficient in designing experiments
• Capacity to work independently
• Ability to interact professionally and in age-appropriate manner with child participants and their parent/caregiver

Desirable Skills

• Understanding neurophysiological data (MEG/EEG/ECG)
• Statistical analysis
• Machine learning
• Completed research projects
• Experience working with children (any context) and/or neurodiversity (including adults)

Submitting an application

We can only consider applications that are complete and have all supporting documents. Applications that do not provide all the relevant documents will be automatically rejected.Your application must include:

1. English language copies of the transcripts and certificates for all your higher education degrees, including any Bachelor degrees.
2. A Research Statement detailing your understanding of the research area, how you would approach the project, and a brief review of relevant literature. Be sure to use the title of the research project you are applying for. There is no set format or word count.
3. A personal statement which outlines any further information which you think is relevant to your application, such as your personal suitability for research, career aspirations, possible future research interests, and further description of relevant employment experience.
4. A Curriculum Vitae (Resume) which details your education and work history.
5. Two academic refereeswho can discuss your suitability for independent research. References must be on headed paper, signed and dated no more than 2 years old. At least one reference should be from your most recent University. You can submit your references at a later date if necessary.
6. Evidence that you meet the English Language requirements. If you do not currently meet the language requirements, you can submit this at a later stage.
7. A copy of your passport. Where relevant, include evidence of settled or pre-settled status.

Location

This position will be based on the Aston Campus in Birmingham. The successful candidate will need to be located within a reasonable distance of the campus, and will be expected to visit in person regularly.

Interviews

Interviews will be conducted online via Microsoft Teams. If you are shortlisted, you will be contacted directly with details of the interview.

Apply for this position here

Funding Notes

This project covers all tuition fees.

Please note that the successful candidate will be responsible for any living expenses and costs relating to moving to Birmingham and/or visiting the Aston campus.

Further information can be found here: Financial Requirements | Aston University

References

References
[1] Faraone, S. et al., Nature Reviews 2024; 10:11
[2] Westwood, S. et al., JAMA Psychiatry 2025; 82(2):118-129.
[3] Schönenberg, M. et al., Lancet Psychiatry 2017; 4(9): 673-684.

Project supervisors

Dr Alexander Zhigalov
Dr Johanna Zumer
Dr A Fratini