We contributed to MatLab (wavelet toolbox)

2 min read

We use MatLab a lot! It’s the major program for brain imaging data analysis in our lab. However, I never thought we could actually contribute to MatLab’s development.

In MatLab 2016, there is a toolbox called Wavelet Toolbox. If you read the document on wavelet coherence (link below), you will find that they used our NIRS data as an example:


Back in 2015/4/9, Wayne King from MathWorks contacted us, saying that they are developing the wavelet toolbox and asking if we can share some data as an example. We did. I’m glad that it’s part of the package now.

The following section are from the page above:

Find Coherent Oscillations in Brain Activity

In the previous examples, it was natural to view one time series as influencing the other. In these cases, examining the lead-lag relationship between the data is informative. In other cases, it is more natural to examine the coherence alone.

For an example, consider near-infrared spectroscopy (NIRS) data obtained in two human subjects. NIRS measures brain activity by exploiting the different absorption characteristics of oxygenated and deoxygenated hemoglobin. The data is taken from Cui, Bryant, & Reiss (2012) and was kindly provided by the authors for this example. The recording site was the superior frontal cortex for both subjects. The data is sampled at 10 Hz. In the experiment, the subjects alternatively cooperated and competed on a task. The period of the task was approximately 7.5 seconds.

load NIRSData;
hold on
legend('Subject 1','Subject 2','Location','NorthWest')
title('NIRS Data')
grid on;
hold off;

Obtain the wavelet coherence as a function of time and frequency. You can use wcoherence to output the wavelet coherence, cross-spectrum, scale-to-frequency, or scale-to-period conversions, as well as the cone of influence. In this example, the helper function helperPlotCoherence packages some useful commands for plotting the outputs of wcoherence.

[wcoh,~,F,coi] = wcoherence(NIRSData(:,1),NIRSData(:,2),10,'numscales',16);

In the plot, you see a region of strong coherence throughout the data collection period around 1 Hz. This results from the cardiac rhythms of the two subjects. Additionally, you see regions of strong coherence around 0.13 Hz. This represents coherent oscillations in the subjects’ brains induced by the task. If it is more natural to view the wavelet coherence in terms of periods rather than frequencies, you can use the ‘dt’ option and input the sampling interval. With the ‘dt’ option, wcoherence provides scale-to-period conversions.

[wcoh,~,P,coi] = wcoherence(NIRSData(:,1),NIRSData(:,2),seconds(0.1),...
    'Time (secs)','Periods (Seconds)');

Again, note the coherent oscillations corresponding to the subjects’ cardiac activity occurring throughout the recordings with a period of approximately one second. The task-related activity is also apparent with a period of approximately 8 seconds. Consult Cui, Bryant, & Reiss (2012) for a more detailed wavelet analysis of this data.


In this example you learned how to use wavelet coherence to look for time-localized coherent oscillatory behavior in two time series. For nonstationary signals, it is often more informative if you have a measure of coherence that provides simultaneous time and frequency (period) information. The relative phase information obtained from the wavelet cross-spectrum can be informative when one time series directly affects oscillations in the other.


Cui, X., Bryant, D.M., and Reiss. A.L. “NIRS-Based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation”, Neuroimage, 59(3), pp. 2430-2437, 2012.

Grinsted, A., Moore, J.C., and Jevrejeva, S. “Application of the cross wavelet transform and wavelet coherence to geophysical time series”, Nonlin. Processes Geophys., 11, pp. 561-566, 2004.

Maraun, D., Kurths, J. and Holschneider, M. “Nonstationary Gaussian processes in wavelet domain: Synthesis, estimation and significance testing”, Phys. Rev. E 75, pp. 016707(1)-016707(14), 2007.

Torrence, C. and Webster, P. “Interdecadal changes in the ESNO-Monsoon System,” J.Clim., 12, pp. 2679-2690, 1999.

第十九期 fNIRS Journal Club 通知 2021/05/29,9:30am

美国普渡大学童云杰助理教授,将为大家讲解他们组最近被接受的一篇使用近红外相位信息研究脑血流变化的文章。热烈欢迎大家参与讨论。 时间: 北京时间2021年5月29日上午9:30地点: https://zoom.com房间号: 846 8391 7517密码: 805190 童云杰教授简介:普渡大学 生物医学工程助理教授、博士生导师。主攻方向是多模态脑成像, 包括核磁,fNIRS, EEG。关注脑功能及生理信号的提取与研究。发表论文九十余篇,引用上千次(H-index = 20)。 童教授要讲解的文章如下: Liang Z, Tian H, Yang HC, Arimitsu T, Takahashi...
Xu Cui
12 sec read

第十八期 fNIRS Journal Club 视频

北京时间2021年4月25日10点,北京师范大学的朱朝喆教授为大家讲解了他们最近几年在经颅脑图谱(Transcranial brain Atlas) 方面做的工作。视频如下: Youtube: https://youtu.be/EhYPuBPQ5uI Youku: 该视频在优酷上传后被优酷屏蔽,不清楚什么原因。申诉无效。
Xu Cui
3 sec read


会议日期:2021年5月22日-24日会议地点:天津师范大学 一、 会议简介       近红外光谱脑功能成像(fNIRS)具有设备购买与使用成本低、可在自然环境条件下使用、具有较高的时间分辨率和空间定位能力等特点,受到了脑科学研究的高度重视。“近红外光谱脑功能成像学术会议”是由北京师范大学认知神经科学与学习国家重点实验室朱朝喆教授发起并组织的全国性学术会议。已连续成功举办六届,共吸引全国近百家高校、科研院所及医院的六百余名学者参加。该会议已成为国内规模和影响力最大的fNIRS脑成像学术活动。       本届会议由北京师范大学与天津师范大学联合主办。会议将延用往届会议将学术报告与研究方法工作坊相结合的模式。学术报告模块(5月22日周六)将邀请心理学与认知神经科学领域、基础与临床医学领域以及工程技术领域知名学者汇报其fNIRS最新研究成果;工作坊模块(5月23-24日)由fNIRS领域一线研究者系统讲授fNIRS成像原理、fNIRS实验设计、fNIRS数据分析与统计、fNIRS论文写作以及fNIRS前沿技术等。除理论讲授外,还设置了fNIRS空间定位与数据分析操作(NIRS-KIT软件)环节,此外还安排充足的研讨答疑时间以便与会人员交流互动。       具体日程与详细内容等最新消息请关注后续通知,可通过天津师范大学心理部网站http://psych.tjnu.edu.cn/或北京师范大学国家重点实验室网站http://brain.bnu.edu.cn/,或者扫描下方二维码关注微信公众号-“fNIRS脑成像实验室”查阅更新信息,期盼在天津师范大学与您相聚! 二、会议组织机构 主办单位:教育部人文社会科学重点研究基地天津师范大学心理与行为研究院、天津师范大学心理学部、北京师范大学认知神经科学与学习国家重点实验室会议主席:白学军、朱朝喆组织委员会:赵春健、杨邵峰、侯鑫、曹正操 三、说明1.        学术报告模块注册费:人民币500元/人;工作坊模块注册费:人民币2500元/人。发票为电子发票,内容均为:“会议费”。两个模块各自独立收费,参会者可根据自己需要进行选择。2.        注册费包括各自模块的资料费、午餐费;其他费用自理。3.        会议报告人免除会议模块注册费,其他费用请自理。4.       ...
Xu Cui
18 sec read

7 Replies to “We contributed to MatLab (wavelet toolbox)”

  1. Dear Dr.Cui, I can not download helperPlotCoherence.mat on the internet, would you please send it to me ?

  2. Hello, thank you this is very helpful. I’m curious, how does the 1) length of the time series and 2) ‘numscales’ command constrain how finely the freqeuncies are parsed? for example why does the result from the NIRSData example always return 121 scales/frequencies?

Leave a Reply to Xu Cui Cancel reply

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