How can we stimulate the brain non-invasively and use this interaction for therapeutic purposes?
One approach to answer this question is to improve our understanding of the biophysical and physiological processes underlying the non-invasive application of electric fields to the central nervous system (CNS), and to improve the delivery of such fields. This involves developing and refining computational models to predict the electric field distribution in the CNS, implementing models of the interaction between the applied electric field and the target cells or neural networks at various spatial scales, carrying out studies involving human subjects to validate our models and to test non-invasive CNS stimulation applications, and collaborating with SMEs that produce and sell this kind of medical equipment. Examples of applications currently under investigation are the use of transcutaneous spinal cord direct current stimulation (ts-DCS) to modulate spinal excitability or reduce pain, and the use of tumor treating fields (TTFields) to arrest cell division in brain tumors.
Another approach is to combine non-invasive brain stimulation (NIBS) and brain-computer interfaces (BCI). This research focuses on user-BCI/BCI-user adaptation to facilitate and improve BCI usage by patients in neuro-rehabilitation and also by the general population. For this purpose, an innovative multimodal approach will be undertaken: knowledge derived from brain connectivity as well as from NIBS techniques such as transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS) will be used together with scalp EEG-based BCI devices. Brain connectivity data obtained from EEG and MRI will be used to evaluate brain networks’ anatomical locations and relations. Additionally, connectivity analysis will also be used to monitor the effects of NIBS and relate them to BCI performance. This work will be complemented by the development of hybrid BCI devices that combine stimulation and EEG-data acquisition together with a natural user interface for self-paced BCI training and optimal training signal classification.