There is a growing interest in portable, non-invasive brain monitoring technologies in the field of personalised healthcare. This is well in-line with the needs of the European population for accurate and fast neurological diagnostics and targeted treatment. As an example, there are 6 million people in Europe who suffer from epilepsy. In most hospital units, the standard practice for brain activity monitoring is to use electroencephalography (EEG) devices due to their high temporal resolution and broad availability.

The accurate localisation of the active parts of the brain with the help of EEG scalp recordings, i.e. the EEG source imaging, is a crucial part of correct diagnostics. For the EEG source imaging, the neuroimaging community relies on EEG software that requires inputs from specialised auxiliary imaging tools or manual calibrations. This is because the solution of the EEG source imaging problem is very sensitive to the electrical modelling of the head of the patient under examination. Unfortunately, the currently available EEG devices and their embedded software do not take this into account, instead it is common to use the same fixed literature parameters for every patient.

In this project, we propose to use electrical impedance tomography (EIT), that is a tomographic technique for imaging electrical conductivities, to determine these head parameters individually. The proposed project will integrate EIT with EEG which together are able to automatically estimate the personalised conductivity parameters and tailor the EEG computations accordingly. This project will have a great significance in promoting the development of the integrated EIT-EEG imaging devices since, in principle, the EIT measurements could be carried out by using the same electrodes and electronics as already used for EEG measurements. The bottleneck in using EIT in this framework is the lack of algorithms and software which we mainly concentrate on in this project by exploiting finite element simulations tools and advanced Bayesian inversion techniques.

Our outputs will be open-access journal papers published in high-quality neuroimaging journals and freely available imaging codes. The project will be carried out in the Laboratory of Mathematics of Tampere University of Technology (Finland) and Department of Physics of University of Bath (United Kingdom).