Wavelet toolbox is a useful tool to study hyperscanning data. Many recent publications on NIRS hyperscanning use wavelet coherence to quantify the relationship between two interacting brains (e.g. Baker et al 2016, Nozawa et al 2016). You can see more information about wavelet coherence at http://www.alivelearn.net/?p=1169

wavelet

Here are some tips to use the toolbox:

1. It often takes a long time to run Monte Carlo simulation. You may use ‘mcc’=0 to disable it.

figure;wtc(signal(:,jj),signal(:,jj+22),'mcc',0);

2. If you need to get the values of the result (instead of the graphic), you may specify the return value

[Rsq,period,scale,coi,sig95] = wtc(signal1,signal2,'mcc',0); %Rsq is a complex matrix

3. If you are only interested in a certain band, you can specify the MaxScale (i.e. ms) parameter. More information at http://www.alivelearn.net/?p=1518

[Rsq,period,scale,coi,sig95] = wtc(signal1,signal2,'mcc',0, 'ms', 128);

4. If you are interested in finding the “phase” information (visualized by the arrows), you may use xwt function. The returned value is a complex matrix and you can calculate the phase.

[Rsq,period,scale,coi,sig95] = xwt(signal(:,jj),signal(:,jj+22));
figure;imagesc(angle(Rsq));

5. To visualize the power of a single signal, you may use wt, which I personally feel much better than FFT.

figure;wt(signal(:,jj));

6. To change the density of the arrows, you may specify the ArrowDensity parameter

figure;wtc(signal(:,jj),signal(:,jj+22),'mcc',0,'ArrowDensity',[30 30]);

Do you have any tips? Please let me know.