Coupling functions for the nonlinear models.

<p>a) Coupling function for the CLMM and n = 1 iteration. b) Coupling function for the CLMM and n = 2 iterations. c) Coupling function for the CLMM and n = 3 iterations. d) Coupling function for the CSEM.</p

Group delay for different cut-off frequencies of a Butterworth low-pass filter.

<p>a) Group delay as a function of frequency for low-pass filter of 320 Hz (bottom), 160 Hz (middle) and 80 Hz (top). b) Mean group delay over pass-band frequency. lp: low-pass. Error-bars indicate standard deviation.</p

Results for the CLMM.

<p>False detections in percent for different branches of CLMM reflecting different degrees of nonlinearity for a) FNDC, b) FNIC, and c) FP. d) Delay deviation in samples for different degrees of nonlinearity. LP: low-pass filter, HP: high-pass filter, Dec: decimation, X i: ith branch of the CLMM, All: average results for all degrees of nonlinearity. Asterisks indicate results significantly different from control (a-c: Fisher’s exact test, p < = 0.05, Bonferroni corrected, d: Wilcoxon rank-sum test, p < = 0.05). Error bars indicate standard deviation.</p

Influence of filtering on delay estimation.

<p>Mean TE as a function of tested interaction delays for the coupling of channels 1 and 2 of the CSEM after low-pass filtering and for the control. Dotted lines represent standard deviation. The black bar at u = 6 indicates the modeled interaction delay. Note that, while TE is defined to be positive semidefinite, estimated TE can be negative due to estimation bias [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188210#pone.0188210.ref057" target="_blank">57</a>].</p

Delay deviation in samples for the Kus-model and increasing low-pass filter orders.

<p>Asterisks indicate results significantly different from control (Wilcoxon rank-sum test, p < = 0.05, Bonferroni corrected). Error bars indicate standard deviation.</p

Coupling scheme for the CSEM.

<p>Gaussian white noise is used as input (channel 1). The data is then time-shifted by the interaction delay of δ = 6 samples, passed to the sigmoid coupling function and independent white noise is added to generate channel 2 and so forth for channels 3 and 4. External white noise (not shown) is added to all channels. Direct and indirect couplings are indicated by solid and dotted arrows, respectively.</p

Analyzed preprocessing types.

<p>Analyzed preprocessing types.</p

Coupling functions for the nonlinear models.

<p>a) Coupling function for the CLMM and n = 1 iteration. b) Coupling function for the CLMM and n = 2 iterations. c) Coupling function for the CLMM and n = 3 iterations. d) Coupling function for the CSEM.</p

Mean power spectra of channel 2 (source) and channel 3 (target) low-pass filtered at 80 Hz.

<p>The mean is taken over 20 trials x 100 datasets.</p

Coupling scheme for the CLMM.

<p>The first index of each channel denotes the number of times the logistic map is iterated, while the second index denotes the channel index within each of the three resulting branches. As X_i receives input from X₁₂, X₂₂ and X₃₂ it is considered to be part of all three branches. Gaussian white noise ns is used as input (channel X_ns1). The data is then time-shifted by the interaction delay of δ = 6 samples, passed to the logistic coupling function and independent white noise is added to generate channel X_*2 and so forth. External white noise (not shown) is added to all channels.</p