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Journal Article

Integrated analysis of anatomical and electrophysiological human intracranial data


Schoffelen,  Jan-Mathijs
Donders Institute for Brain, Cognition and Behaviour, External Organizations;
Neurobiology of Language Department, MPI for Psycholinguistics, Max Planck Society;

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Stolk, A., Griffin, S., Van der Meij, R., Dewar, C., Saez, I., Lin, J. J., et al. (2018). Integrated analysis of anatomical and electrophysiological human intracranial data. Nature Protocols, 13, 1699-1723. doi:10.1038/s41596-018-0009-6.

Cite as: https://hdl.handle.net/21.11116/0000-0004-9DBA-A
Human intracranial electroencephalography (iEEG) recordings provide data with much greater spatiotemporal precision
than is possible from data obtained using scalp EEG, magnetoencephalography (MEG), or functional MRI. Until recently,
the fusion of anatomical data (MRI and computed tomography (CT) images) with electrophysiological data and their
subsequent analysis have required the use of technologically and conceptually challenging combinations of software.
Here, we describe a comprehensive protocol that enables complex raw human iEEG data to be converted into more readily
comprehensible illustrative representations. The protocol uses an open-source toolbox for electrophysiological data
analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible
analysis methods that, over the past decade, have been developed and used by a large research community. In this
protocol, we describe how to analyze complex iEEG datasets by providing an intuitive and rapid approach that can handle
both neuroanatomical information and large electrophysiological datasets. We provide a worked example using
an example dataset. We also explain how to automate the protocol and adjust the settings to enable analysis of
iEEG datasets with other characteristics. The protocol can be implemented by a graduate student or postdoctoral
fellow with minimal MATLAB experience and takes approximately an hour to execute, excluding the automated cortical
surface extraction.