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Multimodal, Multisubject data fusion


This dataset contains EEG, MEG, functional MRI and structural MRI data from 16 subjects who undertook multiple runs of a simple task performed on a large number of Famous, Unfamiliar and Scrambled faces. It will be used to demonstrate:

  1. batching and scripting of preprocessing of multiple subjects/runs of combined MEG and EEG data,

  2. creation of trial-averaged evoked responses,

  3. 3D scalp-time statistical mapping of evoked responses across trials within one subject,

  4. 2D time-frequency statistical mapping of time-frequency data across subjects,

  5. preprocessing and group analysis of fMRI data from the same subjects and paradigm,

  6. source-reconstruction of the “N/M170” face component (using structural MRI for forward modelling),

  7. individual and group-based fusion of EEG and MEG during source reconstruction,

  8. statistical mapping across subjects of cortical power in a time-frequency window, using the functional MRI results as spatial priors.

For the formal basis of these steps, see SPM publications, most specifically Henson et al. (2011).

The raw data can be found here (see README.txt there for more details):

Data acquisition

The MEG data consist of 102 magnetometers and 204 planar gradiometers from an Elekta VectorView system. The same system was used to simultaneously record EEG data from 70 electrodes (using a nose reference), which are stored in the same “FIF” format file. The above FTP site includes a raw FIF file for each run/subject, but also a second FIF file in which the MEG data have been “cleaned” using Signal-Space Separation as implemented in MaxFilter 2.1. We use the latter here. A Polhemus digitizer was used to digitise three fiducial points and a large number of other points across the scalp, which can be used to coregister the M/EEG data with the structural MRI image. Six runs (sessions) of approximately 10mins were acquired for each subject, while they judged the left-right symmetry of each stimulus (face or scrambled), leading to nearly 300 trials in total for each of the 3 conditions.

The MRI data were acquired on a 3T Siemens TIM Trio, and include a 1\(\times\)1\(\times\)1mm \(T_1\)-weighted structural MRI (sMRI) as well as a large number of 3\(\times\)3\(\times\)\(\sim\)4mm \(T^*_2\)-weighted functional MRI (fMRI) EPI volumes acquired during 9 runs of the same task (performed by same subjects with different set of stimuli on a separate visit). (The FTP site also contains ME-FLASH data from the same subjects, plus DWI data from a subset, which could be used for improved head modelling for example, but these are not used here.) For full description of the data and paradigm, see Wakeman and Henson (2015).

Versions of the SPM12 batch job files and scripts used in this tutorial can be found here:


It should be noted that the pipeline described below is just one possible sequence of processing steps, designed to illustrate some of the options available in SPM12. It is not necessarily the optimal preprocessing sequence, which really depends on the question being asked of the data.

Getting Started

Download the data from above FTP site. There are over 100GB of data in total, so you can start by just downloading one subject (e.g, Subject 15 that is used in the first demo below), and perhaps just their MEEG sub-directory initially (though you will need the T1 and BOLD sub-directories later)[^3]. Within the MEEG sub-directory, you will need all the MaxFiltered files (run_0[1-6]_sss.fif), the bad_channels.mat file and the Trials sub-directory. It will be much easier if you maintain this directory structure for your copy of the data.

Open SPM12 and ensure it is set to the EEG modality. To do this, type spm eeg into the MATLAB command window. For this to work, SPM12 root folder must be in your MATLAB path.

Open the batch editor window by pressing Batch from the SPM Menu window. This opens the window shown below.

Screenshot of the Batch Editor. The Module List, Current Module Window and Current Item Windows are identified. The cursor highlights how to save the pipeline as a batch and script.