3 ways to check NIRS data quality

1 min read

Before performing any data analysis, we should check the data quality first. Below are 3 ways to do so.

1. Visual check of the time series

The best pattern detector is our eyes and brains! In many cases, if we visually see the data, we know what is wrong. You may use the method and program in this post to plot the time courses of all channels (not just one).

Visual data quality check of NIRS time courses
Visual data quality check of NIRS time courses

In the plot above, all 48 channels are plotted together (the y-axis). We can clearly see two types of noise:

  1. The spikes which occur in most channels after time point 7000. These spikes are caused by head motion.
  2. The high noise level in the “red” channels (channels 39, 34, 31). This is more evident if we plot the variance of each channel (figure below). As we can easily see, the variance of channels 39, 34 and 31 is much higher than other channels.

    Variance vs Channel
    Variance vs Channel

2. Existence of the “heart-beat” band

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. To use the wavelet transform toolbox, please download here: https://www.alivelearn.net/?p=1561


NIRS wavelet
NIRS wavelet
Example:
figure;wt(hbo(:,1))

3. Correlation between hbo and hbr

The third way is to check the correlation between hbo and hbr. They are supposed to have negative correlation, at least in young healthy subjects. 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 https://www.alivelearn.net/?p=1767

Correlation between oxy and deoxy-Hb
Correlation between oxy and deoxy-Hb

Below is the scripts used for the 3 methods.

[hbo,hbr,mark]=readHitachData('SA06_MES_Probe1.csv');

figure;plotTraces(hbr,1:52,mark)

figure;wt(hbo(:,1))

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

[badchannels] = checkDataQuality(hbo,hbr);

Do you have other ways to check data quality? Please let me know!



写作助手,把中式英语变成专业英文


AI writing papers with real references


Want to receive new post notification? 有新文章通知我

第七十四期fNIRS Journal Club视频 曾令伟博士

Youtube: https://youtu.be/WSh0fmADeZw 优酷:https://v.youku.com/v_show/id_XNjU2MzcyOTU3Ng==.html 认知状态解码
Wanling Zhu
21 sec read

第七十四期fNIRS Journal Club通知2026/9/5, 10am 曾令伟博士

认知状态解码是脑机接口与神经调控研究的重要基础,但如何利用fNIRS实现有效解码并进一步用于个体化认知干预仍有待探索。来自空军军医大学的曾令伟博士将介绍他们围绕fNIRS认知状态解码与闭环神经调控开展
Wanling Zhu
16 sec read

第七十三期fNIRS Journal Club视频 梁正博士 赵庆柏课题组

Youtube: https://youtu.be/5r8OR9rmDxo 优酷:https://v.youku.com/v_show/id_XNjU0ODIxNDEwNA==.html 创造性思维是
Wanling Zhu
20 sec read

Leave a Reply

Your email address will not be published. Required fields are marked *