I am running ERSP analysis on my EEG data in EEGLab and the topographic map shows a significant effect but as I want to show that significant effect across time and plot the spectrogram the significant effect fades away. I tried all the correction for the multiple comparison methods.
Hi,
The significant effect discerned in ERSP analysis might not be apparent in the spectrogram because the two techniques gauge disparate aspects. The topographic map illustrates the spatial distribution of the EEG signal, whereas the spectrogram depicts the time-related changes of the EEG frequencies. Such an effect might be obscured by concurrent activities in the EEG signal, such as interference, distinct brain waves, or artefacts. One might consider augmenting the number of trials or adopting an alternative method of multiple comparison correction to enhance the likelihood of identifying the significant effect in the spectrogram.
Hope this helps.
That is probably because the effect that you observe is not consistent across trials and can be a result of some artifact.
Another issue can be a baseline window selection.
Hi,
I have the same problem. In my research, I can explain this by the fact that spectral changes are not continuous changes, but rather short changes for each person at different times. If you have found a technical solution to the problem, please let me know.
Hi Karolina Lorek ,
The issue we're encountering is likely due to the way statistical analysis is conducted by EEGLAB or Fieldtrip. When permutation testing is used and corrected for multiple comparisons, it takes into account the time points and trials in the multiple comparisons. Therefore, the results will appear significant if a large number of time points and trials are statistically different.
This issue can be mitigated by increasing the temporal resolution that is used in the ERSP calculation. When the statistics are run, you’ll have fewer time points in each bin, so fewer numbers need to be significant for the output to show significance.
Here’s how you can adjust your code:
# Increase 'ntimesout' to 700 [STUDY, ALLEEG] = std_precomp(STUDY, ALLEEG, {},'savetrials','on','interp','on','recompute','on','ersp','on', erspparams',{'cycles',[2 0.5] ,'nfreqs',200,'ntimesout',700,'freqs',[3 31] });
# Original code with 'ntimesout' set to 200 [STUDY, ALLEEG] = std_precomp(STUDY, ALLEEG, {},'savetrials','on','interp','on','recompute','on','ersp','on','erspparams',{'cycles',[2 0.5] ,'nfreqs',200,'ntimesout',200,'freqs',[3 31] });
You can also watch this for more insight:
https://www.youtube.com/watch?v=51y6OAGeS2Q
Yes, you are right, thanks for your helpful solution to this problem!
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