Systems Neuroscience Computing in Python (SyNCoPy): a python package for large-scale analysis of electrophysiological data.

We introduce an open-source Python package for the analysis of large-scale electrophysiological data, named SyNCoPy, which stands for Systems Neuroscience Computing in Python. The package includes signal processing analyses across time (e.g.

Involvement of top-down networks in the perception of facial emotions: A magnetoencephalographic investigation.

Conscious perception of the emotional valence of faces has been proposed to involve top-down and bottom-up information processing. Yet, the underlying neuronal mechanisms of these two processes and the implementation of their cooperation is still unclear. According to the global workspace model, higher level cognitive processing of visual emotional stimuli relies on both bottom-up and top-down processing.

A Tactile Virtual Reality for the Study of Active Somatosensation.

Natural exploration of textures involves active sensing, i.e., voluntary movements of tactile sensors (e.

Optically Pumped Magnetometers for Magneto-Myography to Study the Innervation of the Hand.

The central nervous system exerts control over the activation of muscles via a dense network of nerve fibers targeting each individual muscle. There are numerous clinical situations where a detailed assessment of the nerve-innervation pattern is required for diagnosis and treatment. Especially, deep muscles are hard to examine and are as yet only accessible by uncomfortable and painful needle EMG techniques.

Learned control of inter-hemispheric connectivity: Effects on bimanual motor performance.

Bimanual movements involve the interactions between both primary motor cortices. These interactions are assumed to involve phase-locked oscillatory brain activity referred to as inter-hemispheric functional coupling. So far, inter-hemispheric functional coupling has been investigated as a function of motor performance.