This folder contains figure source data for the figures presented in the *Results* section of: RAA Ince, BL Giordano, C Kayser, GA Rousselet, J Gross and PG Schyns "A statistical framework based on a novel mutual information estimator utilizing a Gaussian copula" ## `Fig11A.mat` - `ks1000` : KS test ground-truth (1000 trials) [time x sensors] - `Icop100` : Gaussian-copula MI (100 trials) [time x sensors] - `tt100` : t-test (100 trials) [time x sensors] - `gtsig`, `Icop100sig`, `tt100sig` : p=0.01 statistical significance for the above measures from permutation test + max statistics - `time` : time index - `chanlocs` : EEGLAB channel structure ## `Fig11C.mat` This includes confusion matrices from multiple repitions of calculations of a number of statistics: - `Ib2` : binned MI, 2 bins - `Ib4` : binned MI, 4 bins - `Ib8` : binned MI, 8 binss - `Icop` : gauss-copula MI - `ks` : Kolnogorov-Smirnov test - `t` : t-test The results variables are: - `trials_cm` : structure with an element for each of the statistics. Each element is a 4d array [2 x 2 x repetitions x trials used], the confusion matrix (compared to ground truth) for each repitition and data size - `trials` : number of trials used (last axis above) - `corrupt_cm` : structure with an element for each of the statistics. Each element is a 4d array [2 x 2 x repetitions x data corruption level], the confusion matrix (compared to groun truth) for each repitition and level of corruption - `corrupt_prct` : percentage of corrupted trials (last axis above) ## `Fig12A.mat` - `Icop2d` : MI in 2d planar gradient [sensors x lags] - `Icopamp` : MI in planar gradient amplitude (pythagorean sum) [sensors x lags] - `Icopdir` : MI in planar gradient direction [sensors x lags] - `Icop2dsig`, `Icopampsig`, `Icopdirsig` : p=0.01 statistical significance for the above measures from permutation test + max statistics - `delays` : lags axis (ms) - `chanlocs` : EEGLAB channel structure ## `Fig12C.mat` This includes confusion matrices from multiple repitions of calculations of a number of statistics: - `Ib2` : binned MI, 2 bins - `Ib4` : binned MI, 4 bins - `Ib8` : binned MI, 8 binss - `Icop` : gauss-copula MI - `sp` : Spearman's rank correlation - `pe` : Pearson correlation The results variables are: - `length_cm` : structure with an element for each of the statistics. Each element is a 4d array [2 x 2 x repetitions x data used], the confusion matrix (compared to ground truth) for each repitition and data size - `lengths` : length of data used (last axis above) - `corrupt_cm` : structure with an element for each of the statistics. Each element is a 4d array [2 x 2 x repetitions x data corruption level], the confusion matrix (compared to groun truth) for each repitition and level of corruption - `corrupt_prct` : percentage of corrupted trials (last axis above) ## `Fig13A-E.mat` - `time` : time axis (ms) - `conderp` : stimulus conditional ERPs [time x deciles] - `Ip`, `Iv`, `Ipv` : Gaussian-copula MI in respectively raw signal (p for position), gradient (v for velocity) and the bivariate response considering raw signal and gradient jointly (pv) - `xIp`, `xIpv` : cross-temporal interaction information for the raw signal (p) and the bivariate raw + gradient signal (pv) - `inttime` : time index for interaction matrices (ms) ## `Fig13F.mat` - `Iemerge` : time course of novel MI emergence - `time` : time axis (ms) ## `Fig13G.mat` - `Icopamp` : GCMI in planar gradient amplitude - `Icop2d` : GCMI in 2d planar gradient - `Icop4d` : GCMI in 2d planar gradient + temporal derivative of each component - `lages` : stimulus-MEG lags (ms) ## `Fig14A.mat` / `Fig14B.mat` - `Ib12`, `Ib4`, `Ib8`: binned MI values, repititions x correlation x number of samples - `Icop` : GCMI - `Ik1` : kNN MI values ## `Fig14C.mat` / `Fig14D.mat` - `Ib2`, `Ib4` : binned MI, repititions x number of samples - `Ib2mm`, `Ib4mm` : binned MI with Miller-Madow correction - `Icop` : GCMI - `Ik` : kNN -