Novel computing tools and advanced algorithms developed by EU-funded researchers are leading to next-generation electroencephalography technologies to help doctors map brain activity and diagnose disorders faster and more accurately.
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Non-invasive electroencephalography (EEG), in which multiple small electrodes are attached to the scalp to detect electrical activity in the brain, is one of the primary techniques for studying brain functioning. It is of critical importance as a diagnostic tool for a variety of neurological disorders such as epilepsy, which is caused by abnormal neuronal activity that triggers recurrent episodes of sensory disturbance, convulsions and loss of consciousness.
In the case of focal epilepsy, where abnormal activity is concentrated in a localised part of the brain, precise and fast EEG imaging is essential to ensure an accurate diagnosis a challenge that was addressed by the NEUROIMAGEEG project.
Led by Francesco P. Andriulli, a professor of computational electromagnetics at the École Nationale Supérieure Mines-Télécom Atlantique in France, NEUROIMAGEEG focused on developing new computational tools and algorithms as well as practical demonstrators of advanced EEG techniques.
We have investigated computational environments and tools to model, predict and solve some very challenging problems in electromagnetics. We focused in particular on the fast even potentially real time electric modelling of signal propagation in the brain, obtaining answers rapidly for problems that usually require days of calculations to solve, Andriulli says.
From theory to application
Supported by a Marie Curie grant, Andriulli and his fellow researchers worked at the theoretical, algorithmic and practical level to devise novel numerical techniques, advanced algorithmic strategies and high-performance computing tools. Their research has not only shown promise in enabling much faster EEG imaging than the current state of the art, but also greater accuracy, reliability and improved imaging resolution in specific applications.
One key outcome of NEUROIMAGEEG is a new set of integral equations, distinct from existing techniques, that are capable of mapping neuronal activity from a volume, surface and linear perspective. In practice, this leads to very accurate solutions that can also be computed much more quickly, Andriulli explains.
For doctors treating patients with focal epilepsy, fast and highly accurate EEG readings are essential, especially prior to surgery, which often involves the removal of the part of the patients brain causing the abnormal signals.
Our research will therefore impact brain imaging applications in a very substantial manner, especially for disorders such as epilepsy but also for research in other areas, for example for neurofeedback systems, to assist the self-regulation of brain function and emerging brain-computer interfaces, Andriulli says.
Building on the success of NEUROIMAGEEG, Andrulli, who was granted a full professorship and set up his own laboratory during the project, has been awarded a consolidator grant of €2 million from the European Research Council to continue his work.
He has also received several notable awards including the URSI Issac Koga Gold Medal of the Triennium 2014-2016, the 2014 IEEE AP-S Donald G. Dudley Jr. Undergraduate Teaching Award and the 2015 EurAAP Leopold B. Felsen Award for Excellence in Electrodynamics.