@article{heinze_towards_2015,
	title = {Towards an estimation of {ECoG} decoding results based on fully non-invasive {MEG} acquisition},
	volume = {126},
	issn = {1388-2457},
	url = {http://www.sciencedirect.com/science/article/pii/S1388245715005106},
	doi = {http://dx.doi.org/10.1016/j.clinph.2015.04.263},
	abstract = {Over the past decade electrocorticography (ECoG) recordings have been evaluated as a promising signal platform in basic and clinical neuroscience (Schalk and Leuthardt, xxxx). Their characteristics, e.g. high spatio-temporal resolution, noise resistance and signal fidelity, make them especially suited for single trial analysis of functional paradigms for corticography. Because recording time is limited and electrode positioning is based on clinical indication, paradigms must be chosen carefully in respect of grid electrode positions and the information content of the cortical area covered by the grid. We propose a method using magnetoencephalography (MEG) in single-trial analysis to estimate the information provided by the grids ahead of implantation. In single-trial analysis a main focus in evaluating a study is on classification rates (number of trials a classifier decodes correctly). Higher decoding accuracy means better use of the brain signal. We use these classification rates in order to estimate the information content of the grid in respect of the paradigm. In our concept, \{MEG\} data is acquired for a set of paradigms. Source analysis is performed, features extracted and classifiers for each paradigm trained. The channel selection of the classifier is limited to the channel set of the brain areas that will be directly covered by the \{ECoG\} grid of the patient. With this information only, classification accuracies for all paradigms are computed. Highest decoding accuracy for a paradigm means it is best suited for this grid location. Therefore, our method suggests choosing the experiment with the best decoding accuracy to be run on this patient. The comparability of \{ECoG\} and \{MEG\} data and its respective decoding performances has been shown (Heinze et al., xxxx). Focus of this study is to evaluate if restrictions to the channel selection lead to results that are expected in perspective to these restrictions (e.g. drop of decoding accuracy for motor stimuli when motor information is excluded). The confusion matrices and channel maps in Fig. 1 prove this to be true. This shows that \{MEG\} data provides a spatio-temporal resolution that is good enough to estimate the information content for any \{ECoG\} grid. In principle, our method can be inverted to plan grid implantation for brain-computer-interfaces (BCI): decoding algorithms for the desired \{BCI\} application (e.g. prosthesis control) could be run using \{MEG\} data. Feature selection routines extract the most important sensors for decoding. Signals of these sensors are mapped to the anatomy using source analysis. The resulting location represents the optimal implantation position. Additionally, alternative placements (e.g. enabling minimally invasive implantation) could be simulated and trade-offs can be made between surgery risk and signal optimization. Funding: Saxony-Anhalt (grant I 60) Forschungscampus \{STIMULATE\}},
	number = {8},
	journal = {Clinical Neurophysiology},
	author = {Heinze, Nicolai and Pfeiffer, Tim and Schoenfeld, Ariel and Rose, Georg},
	year = {2015},
	pages = {e156 -- e157}
}