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LIMO files
| LIMO.Level | = 1 |
| LIMO.Analysis | = specifies the analysis domain: Time, Frequency, or Time-Frequency |
| LIMO.Type | = specifies the analysis space: Channels, Components, Sources |
| LIMO.dir | = indicates the location of current LIMO.mat and associated files |
| LIMO.data | = information about the data |
|---|---|
| LIMO.data.data | = file name |
| LIMO.data.data_dir | = directory where to read data |
| LIMO.data.chanlocs | = import channel location information |
| LIMO.data.sampling_rate | = sampliong rate of the data |
| LIMO.data.start | = when to start the analysis in ms |
| LIMO.data.trim1 | = where to start the analysis as matrix index |
| LIMO.data.end | = when to stop the analysis in ms |
| LIMO.data.trim2 | = where to stop the analysis as matrix index |
| LIMO.data.timevect | = vectors of time frames in ms - used when LIMO.Analysis = Time |
| LIMO.data.tf_times | = vectors of time frames in ms - used when LIMO.Analysis = Time-Frequency |
| LIMO.data.lowf | = which freq to start the analysis in Hz |
| LIMO.data.trim_lowf | = where to start the analysis as matrix index |
| LIMO.data.highf | = which freq to stop the analysis in Hz |
| LIMO.data.trim_highf | = where to stop the analysis as matrix index |
| LIMO.data.freqvect | = vectors of frequency frames in Hz - used when LIMO.Analysis = Frequency |
| LIMO.data.tf_freqs | = vectors of frequency frames in Hz - used when LIMO.Analysis = Time-Frequency |
| LIMO.data.Cat | = Categorical variable(s) |
| LIMO.data.Cont | = Continuous variable(s) |
| LIMO.data.neighbouring_matrix | = matrix describing which electrodes are neighbours - used if bootstrap is request |
| LIMO.data.size4D | = data dimension - used when LIMO.Analysis = Time-Frequency |
| LIMO.data.size3D | = data dimension repeating time frames at each frequency - used when LIMO.Analysis = Time-Frequency |
Note about time/frequency vectors: the epochs can be for instance from -500ms to 1500ms and in LIMO tools choose to analyse shorter durations e.g. [-50 800] corresponding to LIMO.data.start and LIMO.data.end. If data are sampled at 250Hz this means there are 501 frames per epoch and LIMNO tools will trim the data to match the requested start and end time frames i.e. 113 (which would then actually be -52ms) and 326. The same logic applies to frequencies.
| LIMO.design | = information about the design |
|---|---|
| LIMO.design.fullfactorial | = 0/1 specify if interaction should be included |
| LIMO.design.zscore | = 0/1 zscoring of continuous regressors |
| LIMO.design.method | = 'OLS',’WLS’ or ‘IRLS’ |
| LIMO.design.type_of_analysis | = ‘Mass-univariate’ |
| LIMO.design.bootstrap | = 0/1 indicates if bootstrap should be performed or not |
| LIMO.design.tfce | = 0/1 indicates to compute TFCE or not |
by default, we use a weighted least squares approach but ordinary least squares are useful if you don't have enough frames relative to the total number of trials.
Once data are imported, the design matrix is created checking dimensions, regressors, etc.
| LIMO.design.X | = 2 dimensional matrix that describes the experiments' events |
| LIMO.design.nb_conditions | = vector that returns the number of conditions per factor e.g. [2 2 2] |
| LIMO.design.nb_interactions | = vector that returns the number of conditions per interaction e.g. [4 4 4] |
| LIMO.design.nb_continuous | = scalar that returns the number of continuous variables e.g. [3] |
| LIMO.design.name | = name of the design |
| LIMO.design.status | = 'to do' |
Once the design is created, parameters (and effects) are estimated.
| LIMO.design.weights | = matrix of trial weights |
| LIMO.design.status | = 'to do' |
| LIMO.model | = information about the statistics |
|---|---|
| LIMO.model.model_df | = df [effect, error] |
| LIMO.model.conditions_df | = df [effect, error] |
| LIMO.model.interactions_df | = df [effect, error] |
| LIMO.model.continuous_df | = df [effect, error] |
| LIMO.Level | = 2 |
| LIMO.Analysis | = specifies the analysis domain: Time, Frequency, or Time-Frequency |
| LIMO.Type | = specifies the analysis space: Channels, Components, Sources |
| LIMO.dir | = indicates the location of current LIMO.mat and associated files |
| LIMO.data | = information about the data |
|---|---|
| LIMO.data.chanlocs | = channel locations for the cap across all subjects |
| LIMO.data.neighbouring_matrix | = neighbourhood matrix |
All other fields are the same as 1st level - except those specified below.
Computes a one-sample t-test using 20% trimmed mean and winsorized variance. Once the data are selected the LIMO.mat contains the following information is created
| LIMO.data.data | = 1*N cell array of file namess to read |
| LIMO.design | = information of the design |
|---|---|
| LIMO.design.bootstrap | = how many bootstrap performed |
| LIMO.design.tfce | = 0/1 to compute TFCE |
| LIMO.design.electrode | = vector of channels for 'virtual electrode' analysis' |
| LIMO.design.component | = vector of components to pull for analysis' |
| LIMO.design.parameters | = indicates which 1st level parameter was used |
| LIMO.design.name | = test name with analysis space |
| LIMO.design.X | = fake design matrix |
| LIMO.design.method | = 'Trimmed mean' is the default |
Compute t-tests based on 20% trimmed mean and winsorized variances across samples. Once the data are selected the LIMO.mat contains the following information is created
| LIMO.data.data | = 2*1 cell array of file namess to read |
| LIMO.design | = information of the design |
|---|---|
| LIMO.design.bootstrap | = how many bootstrap performed |
| LIMO.design.tfce | = 0/1 to compute TFCE |
| LIMO.design.electrode | = vector of channels for 'virtual electrode' analysis' |
| LIMO.design.component | = vector of components to pull for analysis' |
| LIMO.design.parameters | = indicates which 1st level parameters were used |
| LIMO.design.name | = test name with analysis space |
| LIMO.design.X | = [] |
| LIMO.design.method | = 'Yuen t-test (Trimmed means)' is the default |
Computes a regression using a standard GLM.
| LIMO.data.data | = 1*N cell array of file namess to read |
| LIMO.design | = information of the design |
|---|---|
| LIMO.design.bootstrap | = how many bootstrap performed |
| LIMO.design.tfce | = 0/1 to compute TFCE |
| LIMO.electrode | = vector of channels for 'virtual electrode' analysis' |
| LIMO.design.component | = vector of components to pull for analysis' |
| LIMO.design.parameters | = indicates which 1st level parameters were used |
| LIMO.design.name | = test name with analysis space |
| LIMO.design.X | = design matrix |
| LIMO.design.method | = 'OLS' default or 'IRLS' |
| LIMO.design.type_of_analysis | = 'Mass-univariate' |
| LIMO.design.fullfactorial | = always 0 |
| LIMO.design.status | = 'to do' or 'done' |
| LIMO.design.zscore | = 0/1 |
| LIMO.design.nb_conditions | = always 0 |
| LIMO.design.nb_interactions | = always 0 |
| LIMO.design.nb_continuous | = specifies how many regressors |
| LIMO.design.weights | = weights used in the GLM |
Computes if group differences exist based on trimmed means.
| LIMO.data.data | = N*1 cell array of file namess to read |
| LIMO.design | = information of the design |
|---|---|
| LIMO.design.bootstrap | = how many bootstrap performed |
| LIMO.design.tfce | = 0/1 to compute TFCE |
| LIMO.design.electrode | = vector of channels for 'virtual electrode' analysis' |
| LIMO.design.component | = vector of components to pull for analysis' |
| LIMO.design.parameters | = indicates which 1st level parameters were used in each group |
| LIMO.design.name | = test name with analysis space |
| LIMO.design.X | = design matrix |
| LIMO.design.method | = Generalized Welch's method |
| LIMO.design.type_of_analysis | = 'Mass-univariate' |
| LIMO.design.fullfactorial | = always 0 |
| LIMO.design.status | = 'to do' or 'done' |
| LIMO.design.zscore | = 0/1 |
| LIMO.design.nb_conditions | = indicates the number of groups |
| LIMO.design.nb_interactions | = always 0 |
| LIMO.design.nb_continuous | = always 0 |
Computes repeated effects, group and interactions using a Hotteling t^2 approach, i.e. accounting for specificity.
| LIMO.data.data | = group*repeated measures cell array of file namess to read |
| LIMO.design | = information of the design |
|---|---|
| LIMO.design.bootstrap | = how many bootstrap performed |
| LIMO.design.tfce | = 0/1 to compute TFCE |
| LIMO.design.electrode | = vector of channels for 'virtual electrode' analysis' |
| LIMO.design.component | = vector of components to pull for analysis' |
| LIMO.design.parameters | = indicates which 1st level parameters were used in each group |
| LIMO.design.name | = test name with analysis space |
| LIMO.design.X | = design matrix |
| LIMO.design.method | = 'Mean' |
| LIMO.design.type_of_analysis | = 'Mass-univariate' |
| LIMO.design.fullfactorial | = always 0 |
| LIMO.design.status | = 'to do' or 'done' |
| LIMO.design.zscore | = always 0 |
| LIMO.design.nb_conditions | = indicates the number of factors |
| LIMO.design.nb_interactions | = if group |
| LIMO.design.nb_continuous | = always 0 |
| LIMO.design.factor_names | = names of each factor |
| LIMO.design.effects | = which effects are computed |
| LIMO.design.C | = contrasts used to compute each effect |
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