\chapter{SPM for MEG/EEG overview \label{Chap:eeg:overview}}
\section{Welcome to SPM for M/EEG}
SPM functionality for M/EEG data analysis consists of three major parts.
\begin{itemize}
\item Statistical analysis of voxel-based images. For statistical analysis, we use exactly the same routines as SPM for fMRI users would. These are robust and validated functions based on the General Linear Model\footnote{GLM: \url{http://www.fil.ion.ucl.ac.uk/spm/doc/biblio/Keyword/GLM.html}} (GLM) and Random Field Theory\footnote{RFT: \url{http://www.fil.ion.ucl.ac.uk/spm/doc/biblio/Keyword/RFT.html}} (RFT). The statistical methods are equally applicable to multi- (or single-) subject M/EEG studies.
\item Source Reconstruction \footnote{Source Reconstruction: \url{http://www.fil.ion.ucl.ac.uk/spm/doc/biblio/Keyword/EEG.html}} . Our group has invested heavily in establishing Bayesian approaches to the source reconstruction of M/EEG data. Good source reconstruction techniques are vital for the M/EEG field, otherwise it would be very difficult to relate sensor data to neuroanatomy or findings from other modalities like fMRI. Bayesian source reconstruction provides a principled way of incorporating prior beliefs about how the data were generated, and enables principled methods for model comparison. With the use of priors and Bayesian model comparison, M/EEG source reconstruction is a very powerful neuroimaging tool, which has a unique macroscopic view on neuronal dynamics.
\item Dynamic Causal Modelling\footnote{Dynamic Causal Modelling: \url{http://www.fil.ion.ucl.ac.uk/spm/doc/biblio/Keyword/DCM.html}} (DCM), which is a spatio-temporal network model to estimate effective connectivity in a network of sources. For M/EEG, DCM is a powerful technique, because the data are highly resolved in time and this makes the identifiability of neurobiologically inspired network models feasible. This means that DCM can make inferences about temporal precedence of sources and can quantify changes in feedforward, backward and lateral connectivity among sources on a neuronal time-scale of milliseonds.
\end{itemize}
In order to make it possible for the users to prepare their data for SPM analyses we also implemented a range of tools for the full analysis pipeline starting with raw data from the MEG or EEG machine.
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Our overall goal is to provide an academic M/EEG analysis software package that can be used by everyone to apply the most recent methods available for the analysis of M/EEG data. Although SPM development is focusing on a set of specific methods pioneered by our group, we aim at making it straightforward for the users to combine data processing in SPM and other software packages. We have a formal collaboration with the excellent FieldTrip package (head developer: Robert Oostenveld, F.C. Donders centre in Nijmegen/Netherlands)\footnote{FieldTrip: \url{http://fieldtrip.fcdonders.nl/}} on many analysis issues. For example, SPM and FieldTrip share routines for converting data to \matlab, forward modelling for M/EEG source reconstruction and the SPM distribution contains a version of FieldTrip so that one can combine FieldTrip and SPM functions in custom scripts. SPM and FieldTrip complement each other well, as SPM is geared toward specific analysis tools, whereas FieldTrip is a more general repository of different methods that can be put together in flexible ways to perform a variety of analyses. This flexibility of FieldTrip, however, comes at the expense of accessibility to a non-expert user. FieldTrip does not have a graphical user interface (GUI) and its functions are used by writing custom \matlab\ scripts. By combining SPM and FieldTrip the flexibility of FieldTrip can be complemented by SPM's GUI tools and batching system. Within this framework, power users can easily and rapidly develop specialized analysis tools with GUIs that can then also be used by non-proficient \matlab\ users. Some examples of such tools are available in the MEEG toolbox distributed with SPM. We will also be happy to include in this toolbox new tools contributed by other users as long as they are of general interest and applicability.
\section{Changes from SPM8 to SPM12}
SPM8 introduced major changes to the initial implementation of M/EEG analyses in SPM5. The main change was a different data format that used an object to ensure internal consistency and integrity of the data structures and provide a consistent interface to the functions using M/EEG data. The use of the object substantially improved the stability and robustness of SPM code. The changes in data format and object details from SPM8 to SPM12 are relatively minor. The aims of those changes were to rationalise the internal data structures and object methods to remove some 'historical' design mistakes and inconsistencies. For instance, the methods meegchannels, eogchannels, ecgchannels from SPM8 have been replaced with method indchantype that accepts as an argument the desired channel type and returns channel indices. indchantype is one of several methods with similar functionality, the others being indsample, indchannel, indtrial (that replaces pickconditions) and indfrequency.
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Another major change in data preprocessing functionality was removal of interactive GUI elements and switch to the use of SPM batch system. This should make it easy to build processing pipelines for performing complete complicated data analyses without programming. The use of batch has many advantages but can also complicate some of the operations because a batch must be configured in advance and cannot rely on information available in the input file. For instance, the batch tool cannot know the channel names for a particular dataset and thus cannot generate a dialog box for the user to choose the channels. To facilitate the processing steps requiring this kind of information additional functionalities have been added to the 'Prepare' tool under 'Batch inputs' menu. One can now make the necessary choices for a particular dataset using an unteractive GUI and then save the results in a mat file and use this file as an input to batch.
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The following chapters go through all the EEG/MEG related functionality of SPM. Most users will probably find the tutorial (chapter \ref{Chap:data:mmn}) useful for a quick start.
%A more extensive tutorial demonstrating many new features of SPM on both EEG and MEG data can be found in \ref{Chap:data:multimodal}.
A further detailed description of the conversion, preprocessing functions, and the display is given in chapter \ref{Chap:eeg:preprocessing}. In chapter \ref{Chap:eeg:sensoranalysis}, we explain how one would use SPM's statistical machinery to analyse M/EEG data. The 3D-source reconstruction routines, including dipole modelling, are described in chapter \ref{Chap:eeg:imaging}. Finally, in chapter \ref{Chap:eeg:DCM}, we describe the graphical user interface for dynamical causal modelling, for evoked responses, induced responses, and local field potentials.