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There is a variety of software packages, toolboxes, or libraries for the analysis and processing of neurophysiological data such as EEG and MEG. Many of these solutions provide algorithms for both, sensor-space analysis and sourcespace analysis. Especially with the solutions that run on Windows machines, it is noticeable that the step of the volume model generation is usually not included, since the state-ofthe- art software for this (FreeSurfer) is a Unix-based software and thus not available forWindows machines. Therefore, our goal was to develop a fully-integrated software solution for Windows machines, accessing all processing steps already implemented in an existing toolbox and using FreeSurfer in the same system. Due to its widespread use, we chose MNE Python as the basis for our fully integrated software solution. We used the Windows Subsystem for Linux to create a virtual Linux kernel for the FreeSurfer installation. To demonstrate the workflow, the libeep, and AutoReject libraries have been added. A 64-channel EEG recording during right-hand movement (ME) and imagination (MI) was used to test the implemented workflow. The developed framework consists of several modules within Python, mainly using existing scripts and functions. The library libeep was integrated to read the EEG data with the ‘.cnt’, eeprope format. AutoReject was used to automatically interpolate detected bad channels or to reject complete epochs. FreeSurfer was successfully integrated and customized Python scripts enabled the communication between MNE Python on a Windows machine and FreeSurfer on a virtual Linux kernel. With the above-mentioned EEG dataset, we performed source reconstruction and were able to show ERD/S patterns for both, ME and MI. Our new, fullyintegrated software framework can be used on Windows machines to perform a complete process of source reconstruction.

Fully integrated Windows framework for source localization with MNE Python and FreeSurfer