NIRS hyperscanning data analysis (3) quality check

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NIRS hyperscanning data analysis (1)
NIRS hyperscanning data analysis (2)
NIRS hyperscanning data analysis (3)
NIRS hyperscanning data analysis (4)

Data quality check

1. Behavior data

To identify behavior abnormalities, we need to plot the behavior data for each individual subject. In this case, we plotted the reaction time vs trial. An example is shown below. It apparent that subject 2 (red) did something strange in trial 20.

behavior data (reaction time)
behavior data (reaction time)

We can also plot the difference of reaction time and the threshold to win. Obviously they only win 1 trial and this is unusual.

Reaction time different and threshold
Reaction time different and threshold

We can also find the mean, min and max of the reaction times. Below is the script:

hold on;plot([1:40],subjectData.reaction2,'s-r')

hold on;plot([1:40],subjectData.winthreshold,'s-m')
legend({'reaction time difference','threshold'})

winNum = sum((subjectData.winthreshold - abs(subjectData.reaction1-subjectData.reaction2))>0);
disp('winning trials = ')




2. NIRS data

The 1st way to identify abnormalities in NIRS data is to plot all channel’s time series in one figure, like the figure below. In the following figure, the time series for each channel is plotted and aligned vertically. It’s easy to identify that the green channel (channel #44) has much more noise than others.

NIRS time series

Another way is to use wavelet transform. If the NIRS signal was acquired well, then the heart beating signal should be captured, leaving a bright brand in the frequency ~1Hz in the wavelet transform plot, just like the left plot in the figure below (the band close to period 8). If there is no such band, it does not necessarily mean the signal is trash, but you need to be cautious.

NIRS wavelet
NIRS wavelet


The third way is to check the correlation between hbo and hbr. They are supposed to have negative correlation. If not, or if they have perfect negative correlation (-1), then they might contain too much noise. We have a separate article on this method. Please check out




for ii=1:52; wt(hbo(:,ii)); pause; end

[badchannels] = checkDataQuality(hbo,hbr);

3. Digitizer data

You want to make sure the measure digitizer data is reasonable by looking at the probe positions in a 3D space.

Digitizer data
Digitizer data
figure;plot3(pos_data(:,1),pos_data(:,2),pos_data(:,3),'o');axis equal;

第十二期 fNIRS Journal Club 视频

在2020/9/26日, 华东师范大学李先春教授和他的学生陈美为大家讲解他们今年发表的一篇用近红外超扫描揭示欺骗行为神经机制的文章。视频如下: Youtube:
Xu Cui
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第十三期 fNIRS Journal Club 通知 2020/10/24,10am

北京时间2020年10月24日周六上午10点, 华东师范大学青少年健康评价与运动干预教育部重点实验室、华东师范大学体育与健康学院李琳教授将为大家讲解她们今年发表的一篇用近红外超扫描揭示团体体育运动(篮球)增强合作行为的文章。欢迎大家参加并参与讨论。 时间: 北京时间2020年10月24日周六上午10点地点: https://zoom.com房间号: 865 4354 8112密码: 497127 她要讲解的文献如下: Li, Wang, Luo, Zhang, Zhang, Li (2020) Interpersonal Neural Synchronization During Cooperative Behavior of...
Xu Cui
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第十二期 fNIRS Journal Club 通知 2020/9/26,10am

北京时间2020年9月26日周六上午10点, 华东师范大学李先春教授将为大家讲解他们刚刚发表在 Human brain mapping 的一篇用近红外超扫描揭示欺骗行为的男女差别的文章。欢迎大家参加并参与讨论。 时间: 北京时间2020年9月26日周六上午10点地点: https://zoom.com房间号: 841 2136 8036密码: 603763 他要讲解的文献如下: Chen, Zhang, Zhang, Wang, Yin, Li, Liu, Liu, Li (2020)...
Xu Cui
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