Wearable Magnetoencephalography (MEG) to detect brain activity.

Lead Research Organisation: University of Glasgow
Department Name: School of Engineering


My research project aims to develop a wearable (helmet or cap) Magnetoencephalography (MEG) system. A MEG system is a medical device used to detect the brain's activity with magnetic sensors. Neurophysiological process creates both electrical and magnetic field. Where the more common, electroencephalography (EEG) system, uses electrical probes to detect electrical neuron pulses, it fails to provide a precise spatial resolution of the brains activities, due to attenuation of electrical filed through scull and scalp. MEG has already been proved to give much more explicit information using magnetic sensors. The project aims to use miniaturised magnetic sensors by making it wearable and more accessible to use in clinical environments. The existing MEG systems are bulky with special requirements, such as metal insulated rooms to prevent background radiation. The magnetic sensors of a MEG must also be extremely cold around -270 degrees Celsius and result in a huge and expensive system, which cannot be used in a small general practitioner's clinic (GP).
The tunnel magnetoresistance (TMR) sensor in the cap will detect the generated magnetic field from the brain neurons and convert it into a signal. This signal will be noisy (high interference of unwanted signal) and cannot be used for any application. The noise will come from adjacent neurons, background radiation and wirings. Therefore, the next step in this project is to remove noise from the machine/system to give a clearer and smoother signal. This process is called signal processing, where any signal can be filtered in real time to provide the desired signal.
However, not all noise can be removed and by using a machine-learning algorithm that will learn to separate neural activity from noise, will be able to distinguish a pure brain signal. This next step of the project requires the algorithm to be continually tested as the accuracy and process speed of the algorithm is depended on the number of tests conducted. Once a pure signal is obtained, a neurologist will be able to see the actual brain activity by computers, just like the current MEG machines.
The proposed research will also have other potential applications. Such as scientists have already tested a feeding robot, whereby identifying the region of the brain responsible for motor functions, a probe was embedded in a monkey's brain. The monkey was able to control a robotic arm to feed it bananas via the brain-computer interface system. Therefore, if a wearable MEG cap can be developed, humans will be able to control robots or even prosthetics with their minds with much better precision than it is currently available using MEG-based devices. Creating a precise, noise free and portable systems for recording neuronal activity is the most important prerequisite towards wide spread acceptance of brain computer interface systems in clinical or home environments.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509668/1 01/10/2016 30/09/2021
2126385 Studentship EP/N509668/1 01/10/2018 30/09/2022 Asfand Tanwear