diff --git a/matlab_code/filtered_movement_regressors.m b/matlab_code/filtered_movement_regressors.m
index ff58083..d11cef1 100755
--- a/matlab_code/filtered_movement_regressors.m
+++ b/matlab_code/filtered_movement_regressors.m
@@ -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);
@@ -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
 
 
@@ -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;
             
@@ -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);
@@ -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)));