-
Notifications
You must be signed in to change notification settings - Fork 27
Downsampling
The answer boils down to how many trials do you have. At 1st glance, LIMO MEEG is just doing a GLM and thus it seems like you always have enough trials since you will (almost) always have more trials than regressors (conditions and covariates). This is true only when using an Ordinary Least Squares (OLS) estimation.
In the paper describing how LIMO MEEG derives a single weight per trials we also looked at what is the most efficient way to decompose trials over time or frequencies for data at 1000Hz, %00Hz and 250Hz (see supplementary table 4 at the very end of the paper) and results showed that:
- having more trials than time frames always gives better classification results
- down-sampling gives better results than using ill-conditioned data
- in very noisy conditions, downsampling is not a good option and it is better to let use method do it's thing
Downsampling or not before analyzing
Defining conditions defining
~ categorical.txt ~continuous.txt
EEGLAB-STUDY: run, session, condition and group
Basic Stats: LIMO tests and CI
Repeated measures ANOVA
Results in the workspace
Results in LIMO.cache
Checking data under the plots
Reordering plots
Compute & Plot conditions
Compute & Plot differences
Channel neighbourhood
Editing a neighbourhood matrix
Scripting 1st level
Debugging 1st level errors
Skip 1st level
Scripting 2nd level
Getting stats results with a script