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spm_eeg_filter.m
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spm_eeg_filter.m
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function D = spm_eeg_filter(S)
% Filter M/EEG data
% FORMAT D = spm_eeg_filter(S)
%
% S - input structure (optional)
% (optional) fields of S:
% S.D - MEEG object or filename of M/EEG mat-file
% S.filter - struct with the following fields:
% type - optional filter type, can be
% 'but' Butterworth IIR filter (default)
% 'fir' FIR filter using Matlab fir1 function
% order - filter order (default - 5 for Butterworth)
% band - filterband [low|high|bandpass|stop]
% PHz - cutoff frequency [Hz]
% dir - optional filter direction, can be
% 'onepass' forward filter only
% 'onepass-reverse' reverse filter only, i.e. backward in time
% 'twopass' zero-phase forward and reverse filter
%
% D - MEEG object (also written to disk)
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% Stefan Kiebel
% $Id: spm_eeg_filter.m 4127 2010-11-19 18:05:18Z christophe $
SVNrev = '$Rev: 4127 $';
%-Startup
%--------------------------------------------------------------------------
spm('FnBanner', mfilename, SVNrev);
spm('FigName','M/EEG filter'); spm('Pointer', 'Watch');
if nargin == 0
S = [];
end
%-Ensure backward compatibility
%--------------------------------------------------------------------------
S = spm_eeg_compatibility(S, mfilename);
%-Get MEEG object
%--------------------------------------------------------------------------
try
D = S.D;
catch
[D, sts] = spm_select(1, 'mat', 'Select M/EEG mat file');
if ~sts, D = []; return; end
S.D = D;
end
D = spm_eeg_load(D);
%-Get parameters
%--------------------------------------------------------------------------
if ~isfield(S, 'filter')
S.filter = [];
end
if ~isfield(S.filter, 'band')
S.filter.band = cell2mat(...
spm_input('filterband', '+1', 'm',...
'lowpass|highpass|bandpass|stopband',...
{'low','high','bandpass','stop'}));
end
if ~isfield(S.filter, 'type')
S.filter.type = 'butterworth';
end
if ~isfield(S.filter, 'order')
if strcmp(S.filter.type, 'butterworth')
S.filter.order = 5;
else
S.filter.order = [];
end
end
if ~isfield(S.filter, 'dir')
S.filter.dir = 'twopass';
end
if ~isfield(S.filter, 'PHz')
switch lower(S.filter.band)
case {'low','high'}
str = 'Cutoff [Hz]';
YPos = -1;
while 1
if YPos == -1
YPos = '+1';
end
[PHz, YPos] = spm_input(str, YPos, 'r');
if PHz > 0 && PHz < D.fsample/2, break, end
str = 'Cutoff must be > 0 & < half sample rate';
end
case {'bandpass','stop'}
str = 'band [Hz]';
YPos = -1;
while 1
if YPos == -1
YPos = '+1';
end
[PHz, YPos] = spm_input(str, YPos, 'r', [], 2);
if PHz(1) > 0 && PHz(1) < D.fsample/2 && PHz(1) < PHz(2), break, end
str = 'Cutoff 1 must be > 0 & < half sample rate and Cutoff 1 must be < Cutoff 2';
end
otherwise
error('unknown filter band.')
end
S.filter.PHz = PHz;
end
%-
%--------------------------------------------------------------------------
% generate new meeg object with new filenames
Dnew = clone(D, ['f' fnamedat(D)], [D.nchannels D.nsamples D.ntrials]);
% determine channels for filtering
Fchannels = unique([D.meegchannels, D.eogchannels]);
Fs = D.fsample;
if strcmp(D.type, 'continuous')
% continuous data
spm_progress_bar('Init', nchannels(D), 'Channels filtered'); drawnow;
if nchannels(D) > 100, Ibar = floor(linspace(1, nchannels(D),100));
else Ibar = [1:nchannels(D)]; end
% work on blocks of channels
% determine blocksize
% determine block size, dependent on memory
memsz = spm('Memory');
datasz = nchannels(D)*nsamples(D)*8; % datapoints x 8 bytes per double value
blknum = ceil(datasz/memsz);
blksz = ceil(nchannels(D)/blknum);
blknum = ceil(nchannels(D)/blksz);
% now filter blocks of channels
chncnt=1;
for blk=1:blknum
% load old meeg object blockwise into workspace
blkchan=chncnt:(min(nchannels(D), chncnt+blksz-1));
if isempty(blkchan), break, end
Dtemp=D(blkchan,:,1);
chncnt=chncnt+blksz;
%loop through channels
for j = 1:numel(blkchan)
if ismember(blkchan(j), Fchannels)
Dtemp(j, :) = spm_eeg_preproc_filter(S.filter, Dtemp(j,:), Fs);
end
if ismember(j, Ibar), spm_progress_bar('Set', blkchan(j)); end
end
% write Dtemp to Dnew
Dnew(blkchan,:,1)=Dtemp;
clear Dtemp;
end;
else
% single trial or epoched
spm_progress_bar('Init', D.ntrials, 'Trials filtered'); drawnow;
if D.ntrials > 100, Ibar = floor(linspace(1, D.ntrials,100));
else Ibar = [1:D.ntrials]; end
for i = 1:D.ntrials
d = squeeze(D(:, :, i));
for j = 1:nchannels(D)
if ismember(j, Fchannels)
d(j,:) = spm_eeg_preproc_filter(S.filter, double(d(j,:)), Fs);
end
end
Dnew(:, 1:Dnew.nsamples, i) = d;
if ismember(i, Ibar), spm_progress_bar('Set', i); end
end
disp('Baseline correction is no longer done automatically by spm_eeg_filter. Use spm_eeg_bc if necessary.');
end
spm_progress_bar('Clear');
%-Save new evoked M/EEG dataset
%--------------------------------------------------------------------------
D = Dnew;
D = D.history(mfilename, S);
save(D);
%-Cleanup
%--------------------------------------------------------------------------
spm('FigName','M/EEG filter: done'); spm('Pointer', 'Arrow');
%==========================================================================
function dat = spm_eeg_preproc_filter(filter, dat, Fs)
Fp = filter.PHz;
if isequal(filter.type, 'fir')
type = 'fir';
else
type = 'but';
end
N = filter.order;
dir = filter.dir;
switch filter.band
case 'low'
dat = ft_preproc_lowpassfilter(dat,Fs,Fp,N,type,dir);
case 'high'
dat = ft_preproc_highpassfilter(dat,Fs,Fp,N,type,dir);
case 'bandpass'
dat = ft_preproc_bandpassfilter(dat, Fs, Fp, N, type, dir);
case 'stop'
dat = ft_preproc_bandstopfilter(dat,Fs,Fp,N,type,dir);
end