[Cui Hong]

Thank you for the information.

Just to confirm my understanding… So the values in the ‘scaled’ columns exported from EmotivPro are NOT the ‘final’ scaled values — they were scaled during the recording and were using the MIN and MAX values up to that moment.  After a study session completes, we need to use the data in the ‘raw’ columns to re-compute the scaled values with the ‘global’ MIN and MAX. Right?

Honestly, I do not see why EmotivPro does not do this ‘final’ scaling at the time of data export and includes the final scaled values in the exported CSV file. What is the use of the moment-by-moment ‘scaled’ value in the exported CSV file?

So if I conduct this final scaling on my raw values, what would the correlation between my raw and final scaled values be, according to your experience? I am new to EEG and would appreciate your expert’s opinion as to what to expect. Also do you recommend using scaled values to assess the correlation between a subject’s EEG values and their self-reported stress levels? Since it is within-subject, I feel using raw or scaled should not make a big difference (actually, prefer raw more than scaled).

Again, thank you for answering my questions.

[Cui Hong]

HI, I believe we used low-resolution PM because we got 1 PM score every 10 seconds in the exported csv file.

In https://www.screencast.com/t/2MbZkserYM, “raw” and “scaled” refer to the raw and scaled PM scores found in the exported csv file (as described in https://emotiv.gitbook.io/emotivpro/exported_data_files/csv_files).

 

[Cui Hong]

Related, even though I have read the description for the scaled and raw metric data in https://emotiv.gitbook.io/emotivpro/exported_data_files/csv_files multiple times, I am not sure I really get it.

I expected the correlation between raw and scaled data (exported from EmotivPro) to be really high (like above 0.9) because they are alternative representations of the same event, but I got only 0.7 from several sets of data (see for example the data and graph in https://www.screencast.com/t/2MbZkserYM). In the graph, I found a number of places (marked with arrows) where the direction of change is different between raw and scaled data (one goes up while the other goes down as compared to the neighboring points).

Can anyone help me understand under what kind of circumstance would these kinds of differences happen? When you perform correlation analyses, should you use raw or scaled metrics?

Your help is much appreciated.

 

[Cui Hong]

Another puzzle I try to figure out is about the performance metrics during the close-eye and open-eye calming-down periods. In the image linked here (https://www.screencast.com/t/4MFgl8m0IJ), the calming-down periods are the first 36 seconds (white background). The grey line=interests, the orange line=stress, and the blue line=engage. I expected all three lines to be low during the calming down periods, but EmotivPro shows a very high interest (actually higher than the interest level invoked by all stimli tested, the referenced image does not show the complete interest line). Can you help me understand this? Thank you!

[Cui Hong]

Thank you Luckham for your response. We were using Insight.

[Cui Hong]

Hi Carrasco, were you able to connect the Insight via the USB dongle on a windows computer?

[Cui Hong]

Hi,

I am a user experience researcher. I just started to set up an Insight model to study users’ cognitive load when using a research software application.

I’ve read through the manual but didn’t find much information on how to collect data using an external device or the USB drive that came with the package. Any information on this will be appreciated.

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