Before averaging or regression analysis, I recommend to apply reasonable high/low-pass filters to your raw data, which may significantly reduce the noise level of your results. For "normal" evoked responses, 1-40Hz would be a good choice. Here's one way of doing it, using the MNE utility mne_process_raw:
{{ % This script uses mne_process_raw in order to high/low-pass filter MEG data % cell array "fiff_files{}" specifies a list of files to be filtered % "filters.high"/"filters.low" specify cut-off frequencies for high/low-pass filters, respectively % "out_ext" specifies the suffix that will be attached to the input files after filtering ('_t' is default) % OH, March 2009
if exist('fiff_files')~=1, % you can pre-specify a list of files here
- fiff_files = {'/fullpath/file4subj1.fif', ...
- '/fullpath/file4subj2.fif', ... '/fullpath/file4subj3.fif'};
end;
if exist('out_ext')~=1, % suffix for filtered output files
- out_ext = '_f';
end;
if exist('filters')~=1, % default cut-off frequencies for high/low-pass filters
- filters.high = 1; % default 1Hz for high-pass filters.low = 40; % default 40Hz for low-pass
end;
nr_files = length(fiff_files);
for ff = 1:nr_files,
- [thispath, thisfile,thisext,thisversn] = fileparts(fiff_files{ff}); fiff_outfile = fullfile(thispath, [thisfile out_ext '.fif']); % Run filter using mne_process_raw eval( sprintf('!/imaging/local/linux/mne_lws/bin/mne/mne_process_raw --digtrig STI101 --raw %s --highpass %f --lowpass %f --save %s', fiff_files{ff}, filters.high, filters.low, fiff_outfile) );
end; }}