Real-time processing of neural data provides new opportunities for neuroscience research. This approach yields more intuitive insights into instantaneous brain functions and creates the foundation for a wide range of neurofeedback scenarios. Real-time processing also allows early assessment of data quality and validity of the experiment setup. This enables early identification of possible problems and thus helps to optimize the measurement procedure. Magnetoencephalography (MEG) and Electroencephalography (EEG) are non-invasive electrophysiological methods with a high temporal resolution. Tools and software toolboxes have already been proposed that monitor and process M/EEG data in real-time. This work introduces new tools to acquire and process electrophysiological data streams in real-time. A special emphasis is put on the estimation and visualization of functional connectivity networks. Functional connectivity estimation has been hardly ever considered in the real-time setting so far. The methods described in this work allow the real-time estimation and visualization of large functional connectivity networks based on sensor- and source-level M/EEG data. The new tools were integrated into the open-source MNE-CPP software, which provides an API and standalone GUI applications. The MNE-CPP project caters both software developers and users with little or no coding experience.weiterlesen