EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons: applications to connectivity studies

Electrophysiological Source Imaging (ESI) methods are hampered by the lack of "gold standards" for model comparison. Concurrent electroencephalography (EEG) and electrocorticography (ECoG) recordings (namely EECoG) are considered gold standard to validating EEG generative models with primate models have the unique advantages of both flexibility and translational value in human research. However the severe EEG artifacts during such invasive experiments, the complexity of providing sufficiently detailed biophysical models, as well as lacking sound statistical connectivity comparison methods have hampered the availability and analysis of such datasets. In this paper, 1) we provide EECoG-Comp: an open source platform (https://github.com/Vincent-wq/EECoG-Comp) which encompasses the preprocessing, forward modeling, simulation and comparison module; 2) we take the simultaneous EECoG dataset from www.neurotycho.org as an example to illustrate the use of this platform and compare the source connectivity estimation performance of 4 popular ESI methods named MNE, LCMV, eLORETA and SSBL. The conclusion shows the limits of performance of these ESI connectivity estimators using both simulations and real data analysis. In fact, the use of this platform also suggests the need for both improved simultaneous EEG and ECoG experiments and ESI connectivity estimators.