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Update filtered_movement_regressors.m #30

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Update filtered_movement_regressors.m #30

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55 changes: 9 additions & 46 deletions matlab_code/filtered_movement_regressors.m
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Original file line number Diff line number Diff line change
Expand Up @@ -7,8 +7,6 @@ function filtered_movement_regressors(path_mov_reg, TR, filt_option, order, LP_f
% filt_option filter option, options 1:6, see below
% order order of the filter to be applied
% LP_freq_min Low pass frequency (in minutes) to be filtered
%% defaults
head_ratio_cm = 5;

%% when compiling comment out paths and uncomment this section
TR = str2num(TR);
Expand All @@ -27,43 +25,21 @@ function filtered_movement_regressors(path_mov_reg, TR, filt_option, order, LP_f
filt_method{5} = 'FiltFilt_all';
filt_method{6} = 'FiltFilt_only_Y';

switch filt_type
if filt_option>4
order = order/2; % because filtfilt does forward and backward filtering
end

fs = 1 / TR;
fNy = fs / 2;
switch filt_type
case 'lp'

hr_min = LP_freq_min; % recasted to reuse code
hr = hr_min/60;

fs = 1/TR;
fNy = fs/2;

fa = abs(hr - floor((hr + fNy) / fs) * fs);

% cutting frequency normalized between 0 and nyquist
Wn = min(fa)/fNy;
if ~isempty(order)
b_filt = fir1(order, Wn, 'low');
a_filt = 1;
end
num_f_apply = 0;

[b_filt,a_filt] = butter(order,rr/fNy,'stop');[b_filt,a_filt] = butter(order/2,hr/fNy,'low');
case 'notch'

fc_RR_bw = [fc_RR_min, fc_RR_max];
rr = fc_RR_bw / 60;

fs = 1 / TR;
fNy = fs / 2;

fa = abs(rr - floor((rr+fNy) / fs) * fs);

W_notch = fa / fNy;
Wn = mean(W_notch);
Wd = diff(W_notch);
bw = abs(Wd); % take the absolute value in order to ensure that the difference between the min and max is not negative
[b_filt, a_filt] = iirnotch(Wn, bw);
num_f_apply = floor(order / 2); % if order<4 apply filter 1x, if order=4 2x, if order=6 3x

[b_filt,a_filt] = butter(order,rr/fNy,'stop');
end


Expand All @@ -86,8 +62,7 @@ function filtered_movement_regressors(path_mov_reg, TR, filt_option, order, LP_f
MR_ld=make_friston_regressors(MR);%% Using this function to only get the linear displacements
MR_ld=MR_ld(:,1:6);

switch filt_option

switch filt_option
case 1 %'None';
MR_filt = MR_ld;

Expand All @@ -97,16 +72,10 @@ function filtered_movement_regressors(path_mov_reg, TR, filt_option, order, LP_f

case 3 %'Filt_all';
MR_filt = filter(b_filt,a_filt,MR_ld);
for i=1:num_f_apply-1
MR_filt = filter(b_filt,a_filt,MR_filt);
end

case 4 %'Filt_only_Y';
MR_filt = MR_ld;
MR_filt(:,2) = filter(b_filt,a_filt,MR_ld(:,2));
for i=1:num_f_apply-1
MR_filt(:,2) = filter(b_filt,a_filt,MR_filt(:,2));
end

case 5 %'FiltFilt_all';
MR_filt = filtfilt(b_filt,a_filt,MR_ld);
Expand All @@ -117,15 +86,9 @@ function filtered_movement_regressors(path_mov_reg, TR, filt_option, order, LP_f
case 6 %'FiltFilt_only_Y';
MR_filt = MR_ld;
MR_filt(:,2) = filtfilt(b_filt,a_filt,MR_ld(:,2));
for i=1:num_f_apply-1
MR_filt(:,2) = filtfilt(b_filt,a_filt,MR_filt(:,2));
end
end


hd_mm = 10 * head_ratio_cm; % cm to mm conversion
MR_backed = MR_filt;
MR_backed(:, 4:end) = 180*MR_backed(:,4:end)/(pi*hd_mm);

%% The last 6 movement regressors are derivatives of the first 6. Needed for task fMRI, but not used in the Fair Lab
second_derivs=cat(1, [0 0 0 0 0 0], diff(MR_backed(:,1:6)));
Expand Down