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NIRS hyperscanning data analysis (4) coherence analysis – Xu Cui while(alive){learn;}
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NIRS hyperscanning data analysis (4)
coherence analysis
November 2, 2015 · 1 min read
https://www.sciencedirect.com/science/article/pii/S1053811911010421
NIRS hyperscanning data analysis (1)
NIRS hyperscanning data analysis (2)
NIRS hyperscanning data analysis (3)
NIRS hyperscanning data analysis (4)
How do we analyze hyperscanning data to find out the relationship between two
brains? Obviously correlation is the first thought. However, raw correlation
analysis might be susceptible to noise in the data (e.g. physiological noise such as
heart beating, or motion noise). Wavelet coherence is a great way to find
correlation between two signals – at the same time not affected by physiological
noises. To understand wavelet coherence, you might want to read:
1. Wavelet coherence
2. What does a wavelet coherence plot tell you?
Let’s look at the data of a pair of subjects (please click the image to zoom):
Wavelet coherence of a pair of subjects
There are 22 channels in total in each participant. Each small plot in the figure
above is for one channel. The plots are arranged following the real position of the
channel on the participant’s head. How did I arrange them? I cut and paste and
arrange in PowerPoint 🙂 You can imagine it took me some time. I wish there is a
handy tool.
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NIRS hyperscanning data analysis (4) coherence analysis – Xu Cui while(alive){learn;}
By looking at this figure, a pattern pops out – there is a band in the period 6.4s in
multiple channels. Maybe this band (increase of coherence) is a signature of
collaboration. To quantify this, we average the coherence value around the band
(between periods 3.2 and 12.8s in the task block, and the rest period in between):
[Rsq,period,scale,coi,sig95] = wtc(signal1,signal2,'mcc',0, 'ms',128);
period32 = find(period>32);
period32 = period32(1);
b1 = mean(mean(Rsq(period32:end, marktime(1):marktime(2))));
bi = mean(mean(Rsq(period32:end, marktime(2):marktime(3))));
b2 = mean(mean(Rsq(period32:end, marktime(3):marktime(4))));
Now we get a 3 values for each channel. I normally define “coherence increase” as
the average of the coherence during task minus the coherence in rest. So CI =
(b1+b2)/2 – bi
For each channel in each subject pair, we got a single value (CI). Then for each
channel, we can do a group analysis using T-test. After we get all the T-values for
all channels, then we can plot the map using plotTopoMap
Group coherence T map
This is basic analysis. You can go much deeper from here. Below are some
examples:
1. Is there a learning effect? i.e. is the performance during the 2nd block better
than the 1st block? Is the coherence increase higher than the 1st block?
2. Does coherence increase in the collaboration block differ from other blocks
(such as competition, independent blocks)?
3. Does the phase of the coherence carry any information?
4. …
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4 Replies to “NIRS hyperscanning data analysis (4) coherence analysis”
Jiaxin Yang Says:

September 18, 2018 at 5:58 pm
Dear Dr.Cui:
I am little confused about the formual” b1 = mean(mean(Rsq(period32:end,
marktime(1):marktime(2))))”,especiall the period32:end.
In the context, you said the period between 3.2-12.8(32-128) was your instrested,
so if you want to extra the correlation value of that band, the formal is
Rsq(period32:period128, marktime(1):marktime(2)), right?
Xu Cui Says:

September 18, 2018 at 7:50 pm
The ‘ms’ argument in the previous line specified the max period.
Jiaxin Yang Says:

September 19, 2018 at 1:23 am
@Xu Cui
sorry for miss this key point!!
Thanks a lot!!
SamZebrado Says:

March 1, 2019 at 7:53 am
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NIRS hyperscanning data analysis (4) coherence analysis – Xu Cui while(alive){learn;}
Hi Prof. Cui,
I’m trying to add support for 3×10 to plotTopoMap.
In plotTopoMap.m, during testing of my new version, I’ve found m(10,2:3) and
m(4,12) miscomputed (divided by a wrong number). Maybe your team (if not done
yet) would like to program it in an more efficient way:) It really took me several
hours to read and understand and generate a new one>_< Haha!
best,
Sam
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