Contacts: Prof. Thanasis Fokas, Dr Parham Hashemzadeh (Department of
Applied Mathematics and Theoretical Physics) Andrew Walsh and Olivia Nico-
letti (Cambridge Enterprise).
Electroencephalography (EEG) is a well-established method for monitoring brain activity through the electrical potentials at the scalp. For decades, researchers have proposed several mathematical formulations for reconstructing 3D maps of brain activity from time-series electrical potentials measured at the scalp. However, all these formulations rely on strong assumptions. A breakthrough in a mathematically correct approach of this inverse problem was made by Fokas [1]. An implementation of the relevant algorithm was carried out by Hashemzadeh. The novel approach is subject to a pending patent application led by Cambridge Enterprise.
There are a number of approaches to imaging brain activity in three dimensions, such as functional MRI, PET and Magnetoencephalography; however, they require large expensive instruments and highly trained sta ff. EEG has a number of advantages in comparison to these methods: it is a cheap and portable technology, headsets can be either obtained or easily fabricated, the acquisition of data involves standard computing equipment, and EEG does not require particular preparation of the patient or the use of labels or contrast agents. EEG has enormous potential as an imaging technique in medicine. For example, the current UK standard of care for brain injury, either via trauma or stroke, is that patients should have a CT scan within an hour of such trauma. This is a signi cant challenge both in time and resources for the NHS. The portable nature of EEG implies that the CT can be complemented, or in the future replace with, functional information. Other well known applications include, Alzheimer’s disease, epilepsy, Down’s syndrome, and respiratory disorders during sleep in chronic obstructive pulmonary diseases.
There is also a very strong consumer market potential for this technology including studies of how drivers cope with automated car technologies, gaming industry, and virtual reality. The challenge for the iTeams is to investigate the potential market for this technology particularly, in the early diagnosis of brain injury and the consumer market and to help the inventors and Cambridge Enterprise target their product development activities appropriately.
References:
1. A.S. Fokas, Electro-Magneto-Encephalography for the three-Shell Model:
Distributed Current in Arbitrary, Spherical and Ellipsoidal Geometries, J. R.
Soc. Interface 6, 479-488 (2009).
