Low-cost wearable electroencephalogram (EEG) for fatigue and pain monitoring in brain-injured children

Electroencephalogram (EEG) devices measure brain electrical signals, which provide neural information indicating mental states, including levels of consciousness, etc. They are routinely used in hospitals for the diagnosis and monitoring of neurological conditions, such as epilepsy; however, clinical EEGs are typically bulky, expensive and with tens of wired electrodes, so they are not suitable for long-term monitoring or use outside the hospital environment.

Recent progress in healthcare technology has led to the development of a wide range of wearable sensors that can wirelessly acquire and process bioelectrical signals, including EEG. Mobile technology has the potential to assess health states regularly, in real time, remotely and cost-effectively. In particular, there is hope that this technology can be applied to long-term monitoring of neurological and mental health conditions. However, the accuracy and reliability of present wearable devices is debatable, and further research is needed before they can be reliably used in clinical applications.

Measuring brain activity is particularly challenging for the following reasons: 1) EEG signals have a very low amplitude (tens of microvolts), so the signal-to-noise ratio is likely to be small; 2) significant artefacts (with larger amplitudes) are created by muscle activity (EMG), e. g. from head movement, eye blinking, etc.; 3) a large number of electrodes are necessary to accurately measure brain activity from different parts of the brain and to differentiate neural signals from artefacts. All this can lead to highly inaccurate results when using wearable devices (with less electrodes and more subject to artefacts compared to clinical EEGs), unless advanced signal processing is implemented.

The main aim of this project is to address these technical challenges to enable the use of relatively simple wearable EEGs (with up to 8 electrodes and costing up to a few thousand pounds) to monitor fatigue and pain (as well as other possible symptoms) in brain-injured patients (particularly children). Indeed, fatigue and chronic pain are long-term symptoms of brain injury that would benefit from an objective assessment (in addition to patient’s self-reporting) to improve their treatment. To obtain an objective and reliable assessment of such symptoms based on wearable EEG (possibly combined with other wearable sensors), this project will develop advanced signal processing to remove (or at least detect) artefacts and to analyse the uncertainty of the measurement results to guarantee that only accurate data is retained.

The project has a strong multi-disciplinary nature and can benefit from a unique network of collaborations. The primary supervisor, Dr Roberto Ferrero from the Department of Electrical Engineering and Electronics, has expertise in sensors, signal processing and uncertainty analysis; one of the secondary supervisors, Dr Christopher Brown from the Institute of Psychology, Health and Society, has expertise in brain mechanisms and EEG; the other secondary supervisor, Dr Mark White from the School of Engineering, has expertise in fatigue monitoring. Clinicians from Alder Hey children’s hospital in Liverpool will also be part of the supervisory team to provide clinical support throughout the project. Finally, a UK-based manufacturer of portable EEG will be involved in the project to provide a manufacturer’s perspective.

The University of Liverpool represents an excellent environment to carry out research in this area, also because of its links with Sensor City, a newly-built innovation hub entirely focused on sensor technology, which is hosting a number of SMEs working in the healthcare sector.

The project is engineering-focused, so applications from candidates with an engineering background (electronic engineering, medical engineering, or similar) are particularly encouraged, but applications from candidates with different background are also welcome, if they can demonstrate knowledge and expertise relevant to this project.

The project should start as soon as possible.