  The analysis of EEG/MEG functional connectivity has become an important tool
in neural research. Especially the high time resolution of EEG/MEG enables
important insight into the functioning of the human brain. To date, functional
connectivity is commonly estimated offline, i.e., after the conclusion of the
experiment. However, online computation of functional connectivity has the
potential to enable unique experimental paradigms. For example, changes of
functional connectivity due to learning processes could be tracked in real time
and the experiment be adjusted based on these observations. Furthermore, the
connectivity estimates can be used for neurofeedback applications or the
instantaneous inspection of measurement results. In this study, we present the
implementation and evaluation of online sensor and source space functional
connectivity estimation in the open-source software MNE Scan. Online capable
implementations of several functional connectivity metrics were established in
the Connectivity library within MNE-CPP and made available as a plugin in MNE
Scan. Online capability was achieved by enforcing multithreading and high
efficiency for all computations, so that repeated computations were avoided
wherever possible, which allows for a major speed-up in the case of overlapping
intervalls. We present comprehensive performance evaluations of these
implementations proving the online capability for the computation of large
all-to-all functional connectivity networks. As a proof of principle, we
demonstrate the feasibility of online functional connectivity estimation in the
evaluation of somatosensory evoked brain activity.
