LIMO files

1st level analysis using mass univariate approach

At import

LIMO.Level = 1

LIMO.data = information about the data
LIMO.data.data_dir = directory where to read them
LIMO.data.data = file name
LIMO.data.chanlocs = import channel location information
LIMO.data.start = when to start the analysis
LIMO.data.end = when to stop the analysis
LIMO.data.sampling_rate = sampliong rate of the data
LIMO.data.Cat = Categorical variable(s)
LIMO.data.Cont = Continuous variable(s)
LIMO.data.neighbouring_matrix = matrix describing which electrodes are neighbours if bootstrap

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’ 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. Iterative reweighted least squares (different weights per frames) can also be used
LIMO.design.type_of_analysis = ‘Mass-univariate’ LIMO.design.bootstrap = 0/1 indicates if bootstrap should be performed or not (by default 0 for group studies)
LIMO.design.tfce = 0/1 indicates to compute TFCE or not

creation of the design matrix (limo_design_matrix.m)

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'

after estimation (limo_eeg(4) / limo_glm.m / limo_glm_boot.m)

LIMO.design.weights = matrix of trial weights

LIMO.model = information about the statistics LIMO.model.conditions_df = df [effect, error] LIMO.model.interactions_df = df [effect, error] LIMO.model.continuous_df = df [effect, error] LIMO.design.status = 'done'

2nd level analysis using mass univariate approach

One sample t-test (limo_trimci.m)

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.dir: where data are stored LIMO.Level = 2; LIMO.data.chanlocs: channel locations for the cap LIMO.data.neighbouring_matrix: neighbourhood matrix LIMO.data.data: names of the files to read LIMO.data.data_dir: directories of the files to read LIMO.data.sampling_rate: taken across all subjects LIMO.data.trim1: starting frame (the latest across subjects) LIMO.data.start: starting time LIMO.data.trim2: ending frame (the earliest across subjects) LIMO.data.end: ending time

Two samples t-test (limo_yuen_ttest.m):

Computes a two-samples t-test 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.dir: where is the LIMO.mat and Yr LIMO.data.chanlocs: chanel locations from the expected electrode file LIMO.data.neighbouring_matrix: binary matrix of neighbourhood LIMO.data.data: 2 sets of cells e.g. {{1x10 cell} {1x8 cell}} with the full name of the Betas or con files LIMO.data.data_dir: cells with directories of the Betas or con files LIMO.data.sampling_rate: should be the same across subjects LIMO.data.trim1: 1st data point to analyze LIMO.data.start: 1st data point to analyze in sec LIMO.data.trim2: last data point to analyze LIMO.data.end: last data point to analyze in sec LIMO.design.bootstrap: nb of bootstrap to perform (0 if none) LIMO.design.tfce: 0 or 1 LIMO.design.name: 'one 'two samples t-test all electrodes' LIMO.design.electrode: [] (or 1value or set of values for optimized electrode analysis) LIMO.design.X: [] LIMO.Level = 2;

Paired t-test (limo_yuend_ttest.m):

Computes a paired-samples t-test using 20% trimmed mean and winsorized variance. Once the data are selected the LIMO.mat contains the following information is created

LIMO.dir: where is the LIMO.mat and Yr LIMO.data.chanlocs: chanel locations from the expected electrode file LIMO.data.neighbouring_matrix: binary matrix of neighbourhood LIMO.data.data: cells with the full name of the Betas or con files LIMO.data.data_dir: cells with directories of the Betas or con files LIMO.data.sampling_rate: should be the same across subjects LIMO.data.trim1: 1st data point to analyze LIMO.data.start: 1st data point to analyze in sec LIMO.data.trim2: last data point to analyze LIMO.data.end: last data point to analyze in sec LIMO.design.bootstrap: nb of bootstrap to perform (0 if none) LIMO.design.tfce: 0 or 1 LIMO.design.name: 'paired t-test all electrodes' LIMO.design.electrode: [] (or 1value or set of values for optimized electrode analysis) LIMO.design.X: [] LIMO.Level = 2;

Regression, ANOVA and ANCOVA analyzes (limo_glm1.m):

This is the same information as for 1st level analysis.

Repeated measure ANOVA (limo_rep_anova.m):

Once the selection is done a LIMO.mat file is created with the following information LIMO.dir: where is the LIMO.mat and Yr LIMO.data.chanlocs: chanel locations from the expected electrode file LIMO.data.neighbouring_matrix: binary matrix of neighbourhood LIMO.data.data: cells with the full name of the Betas or con files LIMO.data.data_dir: cells with directories of the Betas or con files LIMO.data.sampling_rate: should be the same across subjects LIMO.data.trim1: 1st data point to analyze LIMO.data.start: 1st data point to analyze in sec LIMO.data.trim2: last data point to analyze LIMO.data.end: last data point to analyze in sec LIMO.design.bootstrap: nb of bootstrap to perform (0 if none) LIMO.design.tfce: 0 or 1 LIMO.design.name: ' Repeated measures ANOVA all electrodes' LIMO.design.electrode: [] (or 1value or set of values for optimized electrode analysis) LIMO.design.X: [] LIMO.Level = 2;