Investigating information content from different brain areas for single trial MEG decoding

Magnetoencephalography (MEG) is a quite over-looked imaging modality within the field of brain-computer-interface (BCI) research, but due to its promising signal quality and non-invasive character it offers a variety of unexplored possibilities for paradigm design in electrocorticography (ECoG). In this study we investigate MEG data from a visual paradigm with motor responses for the influence of brain signals from different brain regions on the achievable decoding accuracies. Across data sets from all four subjects, our results consistently match reasonable expectations. This holds true not only for achievable decoding accuracies, but also for the spatial distrubition of brain regions that contribute most valuable information to the classifier. Therefore, our findings are a step further towards estimations of ECoG outcomes in various grid positions based on a fully non-invasive modality.