[Eeglablist] Motor execution and motor imagination paradigma classification

Clement Lee cll008 at eng.ucsd.edu
Tue Mar 9 09:17:57 PST 2021


Dear Vera,

Hopefully someone with more ME and MI expertise can comment, but in the
meantime, did you model your experiment based on something in the
literature? What sorts of analysis did they do?

In the case of sliding window methods, the time axis visuzlied will be
shorter than the data of the acquisition. This is similar to calculating a
moving average from a series of data, where the results will have less
entries than the input series.

Best,
Clement Lee
Applications Programmer
Swartz Center for Computational Neuroscience
Institute for Neural Computation, UC San Diego
858-822-7535


On Sun, Mar 7, 2021 at 8:02 AM Vera Gramigna <veragramigna at gmail.com> wrote:

> Dear all,
> sorry for this question but I'm new in EEG data analysis.
> I would like to ask for the courtesy to have more information about the
> approach and the pipeline that I could use for the classification of my
> data.
> I have some EEG EPOC+/Flex acquisitions that are organized as follows:
> - 1 min rest->1 min ME RH->1 min ME LH->1 min MI RH->1 min MI LH,
> considering for ME motor execution and for MI motor imagination.
> I need to evaluate the differences in mu rhythm between the five
> conditions.
> During the condition of motor execution and imagination the subject
> performed or imagined the related movement for 1 minute.
> Is EEGLAB time frequency transforms (no baseline) using FFT or wavelet in
> 8-12 Hz range, a correct way to visualize the difference between 5
> conditions?
> In this case, if I consider as sub-epoch time limits the total duration of
> acquisition (5 min), why in ERSP results, in time axis I visualize a
> shorter duration of acquisition (even if I selected no baseline)?
> Can you please suggest to me the best way to analyse my data?
> Thanks a lot
> _______________________________________________
> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
> To unsubscribe, send an empty email to
> eeglablist-unsubscribe at sccn.ucsd.edu
> For digest mode, send an email with the subject "set digest mime" to
> eeglablist-request at sccn.ucsd.edu
>


More information about the eeglablist mailing list