The Neurophysiological Biomarker Toolbox (NBT)

# Script to rank subjective alpha-modulation scores

you want to have downloaded first all the analysis files from the server in one, folder, and make sure that the subjects are ordered from 1 to 13 in the folder. now you will extract the information from the biomarkers that have been already stored in the analysis files (by Richard). this uses the same procedure described in here How to extract biomarkers from multiple conditions in here we will compute the median for each signal, then compute the diference between the median of alpha up and the median of alpha down, for each subject. these are considered our alpha-modulation-scores per participant.

%% choose feedbacksignal, set in order aldo alup ecr, then choose
%% feedbacksignal
nbt_getBiomarkerSeveralConditions(3);
%% the following extract the informations stored in the 3 structures.c1 /c2/ c3. and put them into matrixes.
%% might be different the name of the superordinate structure. check what name they have in your workspace.
%% a structure is a hierarchical structure, so the logic is big.middle.small.smaller.
%% like a folder in your computer you can have many different subfolders in each of them.
aldosig=ans.c1;
alupsig=ans.c2;
ecrsig=ans.c3;
mealdosig=median(aldosig(200:end,:));
mealupsig=median(alupsig(200:end,:));
meecrsig=median(ecrsig(200:end,:));

deltafeed=mealupsig-mealdosig
[sortedelta,index]= sort (deltafeed)
hold on;  plot(mealdosig(index));
hold on; plot(mealupsig(index),'r');
hold on; plot(meecrsig(index),'g');
set(gca,'xtick',1:length(mealdosig))
set(gca,'xticklabel',index)

now you can also plot together some information reguarding the subjects, as age, sex, sensation of control…etc now we will do it with the sensation of control.

%% subject info
subject =[13:-1:1];                                 subject= fliplr (subject);
age=[28 24 NaN 25 NaN 22 21 25 23 29 28 23 22];     age= fliplr (age);
hand=[1 1 NaN 1 NaN 1 1 1 NaN NaN NaN NaN NaN ];     hand= fliplr (hand);
sex=[0 1 0 0 1 0 0 1 0 0 1 1 0];                    sex = fliplr (sex);
ctrl_alup= [ 6 8 8 7 8 7 6 7 7 8 NaN NaN 7];         ctrl_alup= fliplr (ctrl_alup);
ctrl_aldown= [5.5 5.5 7 6 5 6 3 6 5 NaN 5 NaN 3];       ctrl_aldown = fliplr (ctrl_aldown);
%% plot a dot over the median of the feedback signal, with an area representing the control sensation.
scatter(subject,mealdosig(index),([ctrl_aldown(index)]).^3, 'b' );
hold on
scatter(subject,mealupsig(index),([ctrl_alup(index)]).^3 ,'r' );
ctrl_aldown= [5.5 5.5 7 6 5 6 3 6 5 5 5 5 3];       ctrl_aldown = fliplr (ctrl_aldown);
[R,P]=corrcoef(ctrl_aldown, mealdosig)
ctrl_alup= [ 6 8 8 7 8 7 6 7 7 8 5 5 7];         ctrl_alup= fliplr (ctrl_alup);
[R,P]=corrcoef(ctrl_alup, mealupsig)