# Online functional connectivity analysis of large all-to-all networks in MNE Scan

## TL;DR

This paper introduces a new method for real-time analysis of brain connectivity using EEG/MEG data, enabling dynamic insights during experiments.

## Contribution

The paper presents an efficient online functional connectivity analysis implementation in MNE Scan for large all-to-all networks.

## Key findings

- Online computation of functional connectivity is feasible for large all-to-all networks.
- The implementation supports real-time analysis and neurofeedback applications.
- Performance evaluations confirm the system's capability for overlapping interval computations.

## Abstract

The analysis of electroencephalography (EEG)/magnetoencephalography (MEG) functional connectivity has become an important tool in neuroscience. 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, that is, 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 intervals. 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