channeltokeep

README.md

@@ -89,7 +89,7 @@ Note:  The name of csv files should be changed accordingly.
 
 ### Reproducing Figure 2D,Figure 3D using FieldTrip
 
-2. Run the analysis with [ftPreprocessing.m](src/fieldtrip/ftPreprocessing.m). 
+2. Run the analysis with [ftPreprocessing.m](src/fieldtrip/ftpreprocessing.m). 
 3. Create  figures 2D,3D and generate the related values in tables 1,2 using the R script [RewardProcessing_Plots_and_Statistics.R](src/graphiques/RewardProcessing_Plots_and_Statistics.R).Note: the name of csv files should be changed accordingly.
 
 ### Reproducing Figure 4

src/graphiques/Fig5.py

@@ -1,5 +1,24 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Sep 29 21:58:03 2022
+
+@author: ayakabbara
+"""
+import scipy.io
 import seaborn as sns
-import matplotlib.pyplot as plt % matplotlib inline
+import matplotlib.pyplot as plt
+
+mat = scipy.io.loadmat('mat_dif.mat')
+aa=mat.get("matt_corr_diff")
+names_list=['Reference','EEEGLAB','Brainstorm','FieldTrip']
 ax = sns.heatmap(aa,linewidths=.5,square=True,xticklabels=names_list,yticklabels=names_list)
 fig = ax.get_figure()
-fig.savefig("corrstudy_matrix.png",dpi=300) 
+fig.savefig("corrstudy_diff.png",dpi=300)
+
+mat = scipy.io.loadmat('mat_cor.mat')
+aa=mat.get("mat_cor")
+names_list=['Reference','EEEGLAB','Brainstorm','FieldTrip']
+ax = sns.heatmap(aa,linewidths=.5,square=True,xticklabels=names_list,yticklabels=names_list)
+fig = ax.get_figure()
+fig.savefig("corrstudy_gainloss.png",dpi=300) 

src/graphiques/similarity_calc.m

@@ -1,24 +1,26 @@
 function [loss_corr, gain_corr, diff_corr]=similarity_calc(All_ERP11,All_ERP12)
 
 nbchan=29;
-nbsubjects=498;
+nbsubjects=500;
 gain_cor=[];
 loss_cor=[];
 diff_cor=[];
+count=0;
 
 for subject=1:nbsubjects
 tt1=squeeze(All_ERP12(1,26,:,subject));
 tt2=squeeze(All_ERP11(26,:,1,subject));
 if(sum(tt1)*sum(tt2)>0)
+count=count+1;
     for chan=1:nbchan
             cc=corrcoef(squeeze(All_ERP2(1,chan,:,subject)),squeeze(All_ERP1(chan,:,1,subject)));
-            gain_cor(chan,count+1)=cc(1,2);
+            gain_cor(chan,count)=cc(1,2);
 
             cc=corrcoef(squeeze(All_ERP2(2,chan,:,subject)),squeeze(All_ERP1(chan,:,2,subject)));
-            loss_cor(chan,count+1)=cc(1,2);
+            loss_cor(chan,count)=cc(1,2);
 
             cc=corrcoef(squeeze(All_ERP2(1,chan,:,subject))-squeeze(All_ERP2(2,chan,:,subject)),squeeze(All_ERP1(chan,:,1,subject))-squeeze(All_ERP1(chan,:,2,subject)));
-            diff_cor(chan,count+1)=cc(1,2);
+            diff_cor(chan,count)=cc(1,2);
 
     end
 end
@@ -30,4 +32,4 @@
 
 loss_corr = loss_corr(~isnan(loss_corr));
 diff_corr = diff_corr(~isnan(diff_corr));
-gain_corr = gain_corr(~isnan(gain_corr));
+gain_corr = gain_corr(~isnan(gain_corr));