Navigation and service


Magnetoencephalography (MEG) is a non-invasive measurement for the detection of tiny magnetic field components, which originate from the neural processes taking place in the living human brain. Due to the excellent time resolution (within millisecond range) of MEG, the method is ideal for the investigation of fast neuromagnetic brain responses.

The primary interest of the Magnetoencephalography (MEG) Methodology group is the development and implementation of novel soft- and hardware components in the field of MEG research. A major focus is to establish real time data acquisition and signal processing in order to acquire new insights into ongoing electrophysiological brain processes during running MEG measurements.

This, however, is a big challenge, since standard MEG data analysis, including noise and artefact rejection, source localisation and connectivity analysis, is computationally demanding and usually takes in the region of several days for each single experiment. In combination with neuro-feedback techniques and the application of brain computer interfaces (BCI), real time data analysis will offer great potential in neuroscience and therapy in neurology.

Another field of attention is dedicated to the development of state-of-the-art analysis tools, such as multivariate connectivity analysis in combination with blind source separation and novel source localisation routines.


Causal information flow during conflict processing.

Insights into conflict processing networks using MEG

This study uses an adaptation of the Simon task with magnetoencephalography (MEG) to provide detailed insight into the underlying neural mechanisms of the FPAN, with particular focus on its temporal characteristics and directional interconnections.

More: Insights into conflict processing networks using MEG …


Effect of zolpidem in the aftermath of traumatic brain injury

Zolpidem is a drug commonly used to treat insomnia, but it has also been shown to have a paradoxical therapeutic affect in various disorders of consciousness, such as traumatic brain injury, dystonia and Parkinson's disease.

More: Effect of zolpidem in the aftermath of traumatic brain injury …


Deep learning-based classification

We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings.

More: Deep learning-based classification …


Real-time data analysis

In order to decode the spatio-temporal organisation of the human brain in vivo, MEG is an essential tool to study transient changes of electrophysiological processes.

More: Real-time data analysis …


MEG recordings using High-Temperature SQUIDs

Today almost all MEG systems use low temperature SQUIDs to detect the very small magnetic fields generated by the human brain. Low temperature SQUIDs (LTc) require liquid helium for cooling

More: MEG recordings using High-Temperature SQUIDs …

Resting state connectivity Analysis

Resting state connectivity analysis

The study of the neural activity in the resting state has been shown to provide valuable insight into the functional organisation of the human brain.

More: Resting state connectivity analysis …

Neurodynamic progression

Neurodynamic progression

The neurodynamic propagation involves a complex network of communication pathways. The causal relationship between different brain regions is applied in order to investigate the neurodynamics of the network during information processing.

More: Neurodynamic progression …

Single trial data analysis

Single trial data analysis

MEG source analysis is usually applied to signal averages revealing the most prominent stereotypic activity. The major drawback is that averaging will not preserve the temporal dynamics of each individual response.

More: Single trial data analysis …