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LIMO-EEG-Toolbox / limo_tools

Hierarchical Linear Modelling for MEEG data
https://limo-eeg-toolbox.github.io/limo_meeg/
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Summary of discussion 7/19 #169

Open arnodelorme opened 11 months ago

arnodelorme commented 11 months ago

To do LIMO

To do EEGLAB (pop_limo and std_limo)

arnodelorme commented 11 months ago

Screen Shot 2023-07-19 at 10 25 56 AM

CPernet commented 11 months ago
CPernet commented 11 months ago

problem to solve: when precompute boostrap --> plot --> the figure GUI cannot find the H0 file?? yet, reloading the figure makes it work -- ie path issue to fix when calling clustering

CPernet commented 11 months ago

Fix preloading LIMO expected channel --- I recommend not to This file should really be looked at as the automated stuff doesn't always do a good job ; if using STUDY it's there

CPernet commented 11 months ago

course plot -- fixed the GUI, @arnodelorme to play with limo_central_tendency_and_ci and see how to improve limo_add_plot

CPernet commented 11 months ago

add full factorial interaction --> won't be a simple fix because all we have are conditions, and conditions can be pulled in many different way to create factor -- we could make assumptions that event types pooled together (for which we compute contrasts) make factors and create new factor variables (there is no need to build the interaction, just pass the flag and limo_design_matrix builds it)

example: user selects famous, unfamiliar, scramble faces as variable 1 and repetition 1,2,3 as variable 2 --> currently still make 9 conditions but compute contrasts for faces and repetitions --> the proposed solution would be to create a variable face (single column with 1,2,3) and variable repetition (single column 1,2,3), these flag to trials, and send that to limo_batch with the option full factorial on (and it will deal making the 3+3+9 columns)

arnodelorme commented 10 months ago

To do LIMO

Think about course plot and how to make this interface better Fix contrast manager issue. To do EEGLAB (pop_limo and std_limo)

add full factorial interaction in case there is a single subject (add this as an option) full factorial - never do across subject (cannot do ANOVA across subject - no parameter interaction)