Biomedical Computer Science and Mechatronics

DFG-FWF (DACH) project: ONCE-TMS - Online Neuronal Connectivity Estimation and Neurofeedback with Transcranial Magnetic Stimulation


Duration: 2018 - 2021

Principal Investigator (UMIT): Univ.-Prof. Dr. Daniel Baumgarten
Cooperation Partner: Univ.-Prof. Dr. Jens Haueisen (Institute of Biomedical Engineering and Informatics, TU Ilmenau, Germany)  
Funding:  German Research Council (DFG) and Austrian Science Fund (FWF)
Project Description
Human capabilities are characterized by fast and parallel processing of information across local and long-range brain networks. Understanding the dynamic neural processing underlying our capabil­ities requires an electrophysiological technique capable of identifying the networks and their functions with a millisecond resolution. Today, electroencephalography (EEG) and magneto­encephalography (MEG) are the only noninvasive techniques that can estimate the neuronal activity with such resolution. On the contrary, techniques such as (functional) Magnetic Resonance Imaging (MRI/fMRI) measure comparatively slow, but with a much higher spatial resolution. Regarding the proposed project with the title “Online Neuronal Connectivity Estimation and Neurofeedback with Transcranial Magnetic Stimulation” we will establish new real-time methods to analyze and process MEG/EEG data for subsequent cortical stimulation scenarios. The overarching objective is to develop real-time computational tools for advancing our understanding of electrophysiological functions of neuronal networks and cortical stimulation in the human brain in health and disease. During the project methods for estimating functional connectivity in real-time MEG/EEG scenarios will be established. Furthermore, ways to integrate Transcranial Magnetic Stimulation (TMS) into a real-time scenario will be investigated. Such real-time scenarios can be found in neurofeedback and Brain Computer Interface (BCI) research. Both fields address data processing pipelines, which present specific stimuli to the subject, measure the corresponding brain activity, process the measured data in real-time and readjust the upcoming stimulus. This way the subject is part of a close-loop data processing pipeline with a direct feedback. This is for example useful when learning to regain motor function during stroke rehabilitation treatment. The proposed work will advance knowledge regarding the network activity in human with a millisecond resolution and thus contribute to one of the promising research field of our time, the decoding of the human brain.