Synchronization of the Open sesame with skin conductance data (collected via OBIMON device)
Hi all,
I have a problem with matching timing of the stimuli presentation with the skin conductance data recorded via the OBIMON device. OBIMON is a very simple device that connects only via Bluetooth and thus does not allow sending triggers to it in a usual way via the parallel port, etc.. Its output is also very basic - it provides date, time (in the h/min/sec/ms format, with 8 measurements per sec, as its sampling rate is 8Hz), unix timestamp and skin conductance in nS. Thus, OBIMON manufacturer suggested to synchronize the timing of the PC on which the Open sesame script was run and the OBIMON device prior to the experiment in order to be able to link the stimuli triggers from OS with the OBIMON data during data analysis. I did it using https://time.is/
However, now I have difficulty in finding which time values in Open sesame output file I can use for synchronization - Open sesame output file does not seem to provide the overall experiment timing in the same format (h/min/sec/ms) - I can see only the starting point indicated (datetime column).
So, the only option to match the stimuli types with the specific skin conductance datapoint I can think about is to manually calculate the starting point of each trial (time_fixation dot) with respect to the experiment's starting point and then to manually insert the trigger codes for the respective trials into the OBIMON data file. However, due to the large number of participants (50) and trials (216), and since the timing of the trials varies in my experiment (go/no-go paradigm) this manual calculation for every subject trial-by-trial is obviously not an optimal way out.
Is there a better solution that I can use?
The data output file for OS and OBIMON for one participant as well as the script used can be seen via the link:
https://drive.google.com/file/d/14Gd-KnSxrAT1hLx3bfdzORKQyVGWQbWK/view?usp=sharing
Best regards,
EsLi
Comments
Is there a better solution that I can use?
Yes and no. The procedure you describe is the best way. However, I don't see why you would do it manually. Can't you write code for that? For example, adding the start time to each time stamp to get the system time for every trial? Maybe I am misunderstanding, but doesnt seem to be terribly difficult.
Eduard
Hi Eduard,
thanks for the comment, in the end I thought it is the best way indeed - I wrote the R code to calculate the new timestamp for each trial.
Esli