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.

第十六期 fNIRS Journal Club 视频

北京时间2021年1月23日周日下午1点, 瑞典 Karolinska Institutet的潘亚峰博士为大家讲解了他们最近发布的一篇用超扫描研究教师学生关系的文章。视频如下: Youtube: Youku:
Xu Cui
5 sec read

第十六期 fNIRS Journal Club 通知 2021/01/23,1pm

瑞典 Karolinska Institutet的潘亚峰博士将为大家讲解他们最近发布的一篇用超扫描研究教师学生关系的文章。热烈欢迎大家参与讨论。潘博士为了这次报告,需要一大早就起床。因此本次报告的时间比过去要稍晚一点。 时间: 北京时间2021年1月23日周六下午1点地点: https://zoom.com房间号: 815 4986 9861密码: 796475 Pan, Guyon, Borragán, Hu, Peigneux (2020) Interpersonal brain synchronization with instructor compensates for learner’s...
Xu Cui
53 sec read

第十五期 fNIRS Journal Club 视频

北京时间2020年12月27日周日上午10点, 香港中文大学二年级博士生胡玥讲了一篇用神经网络去除运动伪迹的文章。视频如下: Youtube: Youku:
Xu Cui
4 sec read

Leave a Reply

Your email address will not be published.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