Upper panels taken place in rows A, B, and C represent the magnitude T-F interdependence estimates using Equations (3), (9), and (11), respectively, for three subjects.
(A) T-F interdependence estimates using Hamming-weighting window of the width w f = 0.1 s and varying-width smoothing window, w t = 0.1,0.37143,…,2 s, rows (B G) illustrate the T-F interdependence estimates using different lengths of the weighting and smoothing windows, respectively.
Lower panels taken place in rows D, E, and F represent the frequency-domain zero-lag covariance matrix (using Equation (18)) of the magnitude T-F interdependence estimates shown in rows of A, B, C, respectively.
The T-F interdependences estimated with method 1 show high levels of correlations uniformly across time and frequency (Figure 7, row A).
This statistical model was not well-founded in our data (there was no symmetric ellipsoid around the minimum, and parameter estimates revealed interdependence of greatly varying degree).
In this article, we assess the use of non-identical smoothing operators for estimating T-F interdependence.
To test for statistical significance of the estimated T-F interdependence, sets of surrogate data of linear independent signals were created.
Estimated T-F interdependence was binarized by defining a 95% confidence interval using a resampling technique and all T-F points were set at 0 or 1 depending on this threshold.
To binarize the estimated T-F interdependence we again determined the 95% confidence interval by phase-randomizing the data (see Section 2.4.1), assigning all T-F points exceeding the threshold to 1 and the rest to 0. In addition, the z-scores were determined at each specific SNR as Z = X ̄ − μ σ x, (16).
From the GLCM, nine textural features describing the grey levels interdependence in the image were estimated (Fig. 4).
