Using the anti-correlation between oxy and deoxy hemoglobin for NIRS data quality

46 sec read

Back in 2009 we published a paper titled “Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics“. In a nutshell, we found the oxy- and deoxy-Hb are negatively correlated when noise level is low. When noise level increases, their correlation becomes more and more positive.

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

Based on this phenomenon we can check the noise level of a channel using the correlation. Below is the script. You simply input the hbo and hbr data (both matrix), and the output is the bad channels.

function badChannels = checkDataQuality(hbo,hbr)

% function badChannels = checkDataQuality(hbo,hbr)
% Check data quality using correlation between hbo and hbr as indicator
% if the correlation is strictly -1, then bad channel
% if the correlation is > 0.5, then bad channel
% Input: hbo and hbr are NxM matrix, N is number of scan, and M number of
% channels
% output: array of bad channels
%
% Xu Cui
% 2009/11/25

n = size(hbo,2);
for ii=1:n
    tmp = corrcoef(hbo(:,ii), hbr(:,ii));
    c(ii) = tmp(2);
end

pos = find(c==-1);
if ~isempty(pos)
    disp(['Channels with -1 correlation: ' num2str(pos)])
end

pos2 = find(c>0.5);
if ~isempty(pos2)
    disp(['Channels with >0.5 correlation: ' num2str(pos2)])
end

badChannels = [pos pos2];

第三十六期 fNIRS Journal Club 通知 2022/12/03,9am 蔡林博士

蔡林博士于2021年9月毕业于日本庆应义塾大学,并获得工学博士学位,在攻读博士学位期间,师从日本著名近红外成像专家冈田英史教授与婴儿研究专家皆川泰代教授,主要从事近红外光学成像的空间定位研究以及利用近
Xu Cui
14 sec read

第三十五期 fNIRS Journal Club 视频 刘汉莉教授

经颅光生物调节 (tPBM) 是一种能够安全有效地调节神经认知功能的新型脑刺激技术。来自美国德克萨斯大学阿灵顿分校的刘汉莉教授为大家分享了他们近期利用近红外技术揭示tPBM对大脑血流动力学及脑网络的影
Xu Cui
11 sec read

第三十五期 fNIRS Journal Club 通知 2022/10/29,9am 刘汉莉教授

经颅光生物调节 (tPBM) 是一种能够安全有效地调节神经认知功能的新型脑刺激技术。来自美国德克萨斯大学阿灵顿分校的刘汉莉教授将为大家分享他们近期利用近红外技术揭示tPBM对大脑血流动力学及脑网络的影
Xu Cui
7 sec read

7 Replies to “Using the anti-correlation between oxy and deoxy hemoglobin for…”

  1. @Gu Yue
    Good question! I however do not know the answer. You can try it and see if it increase your data quality. I’d love to know the result.

  2. Dear Cui,

    I’m wondering how to deal with these bad channels. Can I use the interpolation method which has been frequently employed in processing EEG data? I mean used the average value of whole channels to replace the bad channels Or just delete data on the bad channels?

    I’m looking forward to hearing from you!

    Yafeng Pan

  3. Dear Pan,
    Have you used the average value of whole channels to replace the bad channels of fNIRS? could you tell me your article name? thank you

    wenfeng Wu

  4. Dear Xu Cui,

    How/why did you choose the threshold of 0.5 (if if the correlation is > 0.5, then bad channel)? I read your paper but I could not figure out why 0.5 should be the right value, but I am very curious to know! Thank you.

    Kind regards,
    Carly

    1. Carly,

      The threshold 0.5 is arbitrarily chosen based on our observation. There is no theoretical basis. Please feel free to use your own threshold.

      Xu

Leave a Reply

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