Prof. Dr.-Ing. habil. Jens Haueisen
Director of the BMTI Institute and head of Biomedical Engineering Group
Prof. Dr.-Ing. habil. Jens Haueisen
phone: +49 3677 69 2861
Magnetoencephalography (MEG) and Electroencephalography (EEG) enable researchers to investigate fast spatial and temporal changes of electrophysiological activity in the human brain. In parallel, with advances in off-line MEG/EEG analysis there is a growing interest in online data processing. MEG/EEG online processing paves the way for a faster and intuitive insight on instantaneous brain functions and at the same time creates the foundation for a wide range of neurofeedback scenarios. More specifically, online neurofeedback enables researchers to test hypotheses about specific brain properties (e.g. the activity state of specific brain areas) by online monitoring this property and adapting the experimental interventions according to the state of this parameter. This opens up a new way to investigate basic mechanisms of brain functions and might be also a valuable tool in neurorehabilitation where functional deficits are heterogenic. Moreover, real-time feedback provided to subjects during a neurofeedback scenario gives them a chance to learn to modify their neuronal activity patterns. This will be of great use when treating epilepsy, attention deficit hyperactivity disorder (ADHD), stroke and spinal cord injured patients.
However, processing MEG/EEG data online introduces the following, notrivial challenges: the low Signal-to-Noise-Ratio (SNR), the large amount of incoming data and the high computational cost of complex analysis procedures. In order to improve the SNR, preprocessing the data online by working on single trial, including spatial and temporal filtering techniques, is indispensably necessary.
During a prior DFG funded project we established and investigated methods for estimating cortical activity online. In this joint project with our Austrian partner group, we aim to establish methods for cortical online connectivity estimation based on MEG/EEG data in conjunction with EEG and transcranial magnetic stimulation (TMS) scenarios. First of all, we want to establish novel online connectivity estimation methods for analyzing brain network structures. With the novel online connectivity estimation, we aim to provide input for robust feature extraction in neurofeedback scenarios. Moreover, we aim to visualize the results in an online 3D display for a more imminent analysis. Secondly, we want to include brain state dependent cortical stimulation into our online processing pipeline. Little work on directly integrating cortical stimulation into a neurofeedback or Brain Computer Interface (BCI) scenario has been proposed so far. By including the new online methods into TMS we want to prepare for upcoming EEG/TMS studies and the integration of TMS into neurofeedback scenarios.
