NIRS hyperscanning data analysis (2)

1 min read

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

Data collection

We used Hitachi’s ETG 4000 to collect data. This device normally collects data from a single head. In some of the configurations, it has a set of fibers collecting the signal from the left brain, the other set from the right brain. We thought, why don’t we use the second set to collect signal on another person?

That’s what we did. The ETG 4000 system still treated the two streams of signals from a single brain, but later when we do the data analysis, we separated them.

We used one 3×5 patch on one person’s head, and the other 3×5 patch on the 2nd person’s head. The patch covers only a small area of the brains, so we need to pick up which brain region we need to measure. We chose the frontal cortex. Frankly speaking we don’t have a solid reason to do so, except:

(1) for a high level cognitive task like collaboration, frontal cortex is probably involved;
(2) the frontal region is easier to place the NIRS patch (there is no hair! Separating hair to get a better signal drives me crazy!), and the signal-to-noise ratio is higher compared to other brain areas (the skull is thinner in this region, as demonstrated by our other publications, see figure below).

Skull thickness
Skull thickness

So we put the caps on the frontal lobe – mostly for easier of experiment. You can see a picture below. In the picture are Dan (researcher assistant at that time) and me.

NIRS cap on head
NIRS cap on head

We tried to put the cap in the same position for all subjects. Of course, everybody’s head shape is different, so it’s not possible to precisely put the cap in the same location. But we follow some standard techniques such as using the eyebrow and midline as the landmark. In the end, we find this approach works reasonably well.

For this experiment, we did not collect the 3D digitizer data (to localize the channel location more precisely). The current method of collecting 3D digitizer data is tedious and prone to errors. I wish in the future the NIRS device can automatically tell the locations of each probe.




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

第七十五期fNIRS Journal Club视频 张浩云团队

Youtube: https://youtu.be/MB3nIDg4fMo 优酷:https://v.youku.com/v_show/id_XNjU3Nzk4MDA0OA==.html 语言学习本质
Wanling Zhu
19 sec read

第七十五期fNIRS Journal Club通知2026/10/10, 10am 张浩云团队

语言学习本质上是社会性、交互性的过程,但现有对其神经机制的研究大多仅从学习者个体层面开展。来自澳门大学的张浩云团队将分享一项fNIRS超扫描研究,探讨语言学习中师生互动的神经机制,包括师生神经同步如何
Wanling Zhu
14 sec read

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

Leave a Reply

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