Statistics in sensor space

A standardised way or running sensor-level statistics on your MEG data is implemented in SPM5:

http://imaging.mrc-cbu.cam.ac.uk/meg/SensorSpm

But nothing prevents you from finding your own way... = Converting Fiff-files to SPM images = This script has not been tested extensively, but it is based on the scripts at http://imaging.mrc-cbu.cam.ac.uk/meg/SensorSPM.

% Modified from http://imaging.mrc-cbu.cam.ac.uk/meg/SensorSPM
% takes a list of Fiff-files (cell array "fifflist{}") and converts MEG data to SPM image files,
% which can then be subjected to 2nd-level statistics, for example
% "rootdir" can contain the directory path common to all files
% the script will produce subdirectories with img-files in the directories of the input fiff-files
% so far only tested on one data set
% OH, March 2009


if exist('fifflist')~=1,   % you can specify a list of files here (e.g. on-line averages for all subjects in your study)
    fifflist = {'/fullpath/file4subj1.fif', ...
             '/fullpath/file4subj2.fif', ...
             '/fullpath/file4subj3.fif'};
end;

nr_fiffs = length(fifflist);


if exist('rootdir')~=1, % if not root directory specified, leave empty
    root_dir = '';
end;

% GET GOING...
clear S;
for ff = 1:nr_fiffs,    % for all Fiff-files...

% CONVERT FROM FIFF TO SPM FORMAT...        
    S.Fchannels = fullfile(spm('dir'),'EEGtemplates','FIF306_setup.mat');
    S.veogchan  = [62];         % Channel 62 is (bipolar) VEOG, MAY NEED EDITING
    S.heogchan  = [61];         % Channel 61 is (bipolar) HEOG, MAY NEED EDITING
    S.veog_unipolar = 0;        % MAY NEED EDITING        
    S.heog_unipolar = 0;        % MAY NEED EDITING
    S.conds = 1;                % conditions in fiff-file, MAY NEED EDITING

    S.grms = 1;    % so that the splitting outputs mags, grads and grad RMS (grms)
    
    [fiffpath,fiffname,fiffext,fiffversn] = fileparts(fifflist{ff});
    
    S.Fdata = fullfile(root_dir, fifflist{ff});   % Input Fiff-file before splitting
    fileSPMout = fullfile(root_dir, fiffpath, [fiffname '_SPM.mat']); % Output SPM file (if not specified, will be 'myexp.mat')

    S.Pout  = fileSPMout;   % output for conversion from Fiff to SPM format (before splitting)

    D0 = spm_eeg_rdata_FIF(S);  % convert fiff- to SPM-format
        
% SPLIT DATA INTO MAGS/GRADS/EEG...       
    S.D = fileSPMout;       % structure for splitting
    
    D1 = spm_eeg_splitFIF(S);   % split SPM files
    

% WRITE TO IMAGE VOLUMES:
    % Select options (see help for spm_eeg_convertmat2ana3D):
    clear S
    S.interpolate_bad = 0;
    S.n = 32;
    S.pixsize = 3;
    % Select trial types:
    S.trialtypes = [1];
    img_outnames = '';

    % Mags:
    S.Fname = fullfile(root_dir, fiffpath, [fiffname '_SPM-mags.mat']);
    tmpname = spm_eeg_convertmat2ana3D(S);
    img_outnames(1,:) = fullfile(root_dir, fiffpath, [fiffname '_SPM-mags'], 'trialtype1', 'average.img');    
    
    % Grad magnitude:
    S.Fname = fullfile(root_dir, fiffpath, [fiffname '_SPM-grds.mat']);
    tmpname = spm_eeg_convertmat2ana3D(S);
    img_outnames(2,:) = fullfile(root_dir, fiffpath, [fiffname '_SPM-grds'], 'trialtype1', 'average.img');
    
    % GradRMS magnitude:
    S.Fname = fullfile(root_dir, fiffpath, [fiffname '_SPM-grms.mat']);
    tmpname = spm_eeg_convertmat2ana3D(S);
    img_outnames(3,:) = fullfile(root_dir, fiffpath, [fiffname '_SPM-grms'], 'trialtype1', 'average.img');

% SMOOTH IMAGES    
    P = img_outnames;
    
    SmoothKernel = [5 5 10]; % % Smoothness in x (mm), y (mm) and z (ms), MAY NEED EDITING 
    for n=1:size(P,1);
               [pth,nam,ext] = fileparts(P(n,:));
               Pout{n}       = fullfile(pth,['s' nam ext]);
               spm_smooth(spm_vol(P(n,:)),Pout{n},SmoothKernel);
               Pin = strvcat(P(n,:),Pout{n});
               spm_imcalc_ui(Pin,Pout{n},'((i1+eps).*i2)./(i1+eps)',{[],[],'float32',0}); 
    end
    %The final imcalc step above is just to reinsert NaN's for voxels outside space-time volume into which data were smoothed
    
end;    %..ff