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 statisticstime
: time indexchanlocs
: EEGLAB channel structureFig11C.mat
This includes confusion matrices from multiple repitions of calculations of a number of statistics:
Ib2
: binned MI, 2 binsIb4
: binned MI, 4 binsIb8
: binned MI, 8 binssIcop
: gauss-copula MIks
: Kolnogorov-Smirnov testt
: t-testThe 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 sizetrials
: 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 corruptioncorrupt_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 statisticsdelays
: lags axis (ms)chanlocs
: EEGLAB channel structureFig12C.mat
This includes confusion matrices from multiple repitions of calculations of a number of statistics:
Ib2
: binned MI, 2 binsIb4
: binned MI, 4 binsIb8
: binned MI, 8 binssIcop
: gauss-copula MIsp
: Spearman’s rank correlationpe
: Pearson correlationThe 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 sizelengths
: 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 corruptioncorrupt_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 emergencetime
: time axis (ms)Fig13G.mat
Icopamp
: GCMI in planar gradient amplitudeIcop2d
: GCMI in 2d planar gradientIcop4d
: GCMI in 2d planar gradient + temporal derivative of each componentlages
: stimulus-MEG lags (ms)