HIGHLIGHTS
SUMMARY
Although this assures high signal quality and reduces confounding factors, the accuracy of the obtained models can deteriorate when used in highly ecological settings (e_g,). Representative examples of the latter include methods that rely on data statistics (e_g, ), spectral/connectivity profiles (e_g, ), blind source separation (e_g, ), adaptive filtering (e_g, ), and, more recently, on machine and deep learning approaches (e_g, ). Combinations of multiple such approaches have also been proposed (e_g, ). It turns out that, e_g, disease and aging can affect such periodicities, thus making the modulation spectral signal representation useful for disease characterization. More recently . . .
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