Agarose-Based Inhomogeneous Brain Phantoms for Validating High-Resolution EEG Neuroimaging Solvers

Brain-Computer Interfaces (BCIs) are systems that translate their user's mental state, typically recorded via electroencephalography (EEG), into commands for a computer without any muscular activity being involved. They have been widely adopted in the medical community, for instance to allow patients suffering from locked-in syndrome to interact with their environment or for prosthesis control. In a recent incarnation, BCI algorithms leverage non-invasive neuroimaging techniques to better discriminate between users' mental states. One of the main challenges of these approaches is to ensure that the reconstruction of the intracranial brain activity from the external EEG measurements at the art of the neuroimaging procedure is accurate, without resorting to invasive measurements. Verifying the accuracy of the reconstructed activity is a challenging task due to the complexity of the brain activity, even at resting states, and makes it challenging to verify the stability and adequacy of the learning algorithm trained on the reconstructed data. In this work both problems are tackled by using controlled environments to simulate and design BCIs processing chains in which the location and the behavior of the source signals in the brain volume is known. In a first setting, we used state-of-the-art neuroimaging numerical models combined with realistic biosignals to simulate and assess BCI pipelines. This environment has then been replicated in an experimental setting were an EEG measured the potential generated by controlled sources located inside a gelatin brain phantom. Combining these setups to recordings obtained from real BCI session, will provide a robust and stable basis for the development of new BCI algorithms in a rigorous and controllable environment and allow for a fair comparison of their performances to that of the literature by alleviating the challenges posed by human subject's variability and proficiency in BCI exercises.