Table 2 The parameters for single dataset analysis

surrogatetype (only in TEsurrogatestats.m)

'trialshuffling'

string

surrogate data for trial(n) will be created by replacing trial n of one channel:

'trialshuffling': trial(n+1)

'trialreverse': reverse of trial(n)

'blockresampling': cuts trial(n) at random point and resamples the trial

'blockreverse1': reverse after blockresampling

'blockreverse2': reverse first block after blockresam-pling

'blockreverse3': reverse second block after blockre-sampling

shifttest

'yes'

string

perform shift test to identify instantaneous mixing between the signal pairs. Values: 'yes' or 'no'

shifttesttype

'TE > TEshift'

string

The shift test can be calculated for the direction TE value of original data greater than the TE values of shifted data (value = 'TE > TEshift') or vice versa (value = 'TEshift > TE'). In this case the alpha level for the shift test is set to 0.1.

shifttype

'predicttime'

string

time shift used in shift test: 'onesample' - shift by one sample into the past; 'predicttime' - shift by the time specified in cfg.predicttime_u in TEprepare.m

permstatstype

'indepsamplesT'

string

'mean' to use the distribution of the mean differences and 'depsamplesT' or 'indepsamplesT' for distribution of the t-values.

numpermutation

190100

integer number

number of permutations in the permutation test

tail

2

integer number

1 or 2 tailed test of significance in the permutation test

alpha

.05

number

significance level for the permutation test

correctm

'FDR'

string

correction method used for correction of the multiple comparison problem - false discovery rate 'FDR' or Bonferroni correction 'BONF'

fileidout

string

the first part of the output filename

dim

optimal embedding dimension found in TEprepare

integer number

number of embedding dimensions; if not specified, the optimal dimension found in TEprepare will be used (recommended option!)