Abstract
The increasing availability of network data is leading to a growing interest in processing of signals on graphs. One notable tool for extending conventional signal-processing operations to networks is the graph Fourier transform that can be obtained as the eigendecomposition of the graph Laplacian. In this letter, we used the graph Fourier transform to define a new method for generating surrogate graph signals. The approach is based on sign-randomization of the graph Fourier coefficients and, therefore, the correlation structure of the surrogate graph signals (i.e., smoothness on the graph topology) is imposed by the measured data. The proposed method of surrogate data generation can be widely applied for nonparametric statistical hypothesis testing. Here, we showed a proof-of-concept with a high-density electroencephalography dataset.
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