Hi Paul,
calculated the noise covariance matrix using the individual trial file for each subject using the pre-stimulus period of the condition with the most trials (this may be wrong, but I don't think it would explain the errors I eventually run into)
Why didn't you decide to compute the noise covariance using the pre-stim baselines of all the trials?
At this point, I add each the '...| zscore | norm' files to process 2, the pre files to A and the post files for B, trying to compare one condition in a pre-post fashion.
I'm not sure I understand what you mean with "pre" and "post".
For one given subject, you have two consecutive experimental conditions with the same duration and that can be compared time sample by time sample?
In a typical ERP analysis, within-subject tests between conditions are independent tests, not paired tests.
https://neuroimage.usc.edu/brainstorm/Tutorials/Statistics#Example_4:_Parametric_test_on_sources
A general advice: constrained source maps look uglier, but give similar information and are much easier to manipulate, especially for handling the statistical tests.
Here, I get a resulting 'stat' file titled 't-test paired', but after clicking it to see the results, I see a flat-line in regards to t-test values
If you see flat lines, this is a time-series figure, which indicates you are running tests at the sensor level, not at the source level? Haven't you forgotten to click on the "process sources" buttons in Process2?
Please post a few screen captures to illustrate your questions.
the warning pop up: 'Warning: Cannot determine t-test side'.
You can ignore this warning, it is related with the adjustment of the colormap to the range of significant values, to give more contrast to the images. If you don't have any significant data to display, this function doesn't work...
To see if the file you wanted to get was successfully computed, in the Stat tab, set the p-value threshold to 1, and the correction for multiple comparisons to "uncorrected". This will display the value of the T-statistic for each time sample and each space point (sensor or dipole).
When running different types of analyses, I also run into 'All Values are null. Please check your input file'.
This is because there is nothing significant in your comparison.
To explore the differences between datasets, before trying to localize them in space, you can use the machine learning tools available in Brainstorm (two new functions are not documented yet, but it will come soon):
https://neuroimage.usc.edu/brainstorm/Tutorials/Decoding
a) is my pipeline correct in the sense that I should be able to do source statistics on it
What you describe seems to match what is recommended in the online tutorial.
@pantazis @Sylvain @John_Mosher @leahy @hossein27en
This is related with this issue: Group analysis: recommended workflow and statistics · Issue #141 · brainstorm-tools/brainstorm3 · GitHub
Can you please give your point of view on these questions?