Ever since I began to work on electrophysiology and cognitive research questions, it has been my foremost conviction that reproducible research needs scalable automated data processing methods. Only when MEG and EEG data can be processed at scale and automatically, large data sets can be assembled at ease to support fore-front research using data-intense machine learning methods needed for optimizing prediction of clinical end-points in translational clinical neuroscience. Moreover, automated methods reduce researcher degrees of freedom, and, hence, may mitigate the ubiquitous replication crises encountered in several empirical sciences.
The goal of this axis of reasearch is to develop tools that simplify analysis of EEG and MEG data. Please consider the related publications listed below.