Electrophysiological signals such as the EEG, MEG, or LFPs have been extensively studied over the last decades, and elaborate signal processing algorithms have been developed for their analysis. Many of these methods are based on time-frequency decomposition to account for the signals' spectral properties while maintaining their temporal dynamics. However, the data typically exhibit intra- and interindividual variability. Existing algorithms often do not take into account this variability, for instance by using fixed frequency bands. This shortcoming has inspired us to develop a new robust and flexible method for time-frequency analysis and signal feature extraction using the novel smooth natural Gaussian extension (snaGe) model. The model is nonlinear, and its parameters are interpretable. We propose an algorithm to derive initial parameters based on dynamic programming for nonlinear fitting and describe an iterative refinement scheme to robustly fit high-order models. We further present distance functions to be able to compare different instances of our model. The method's functionality and robustness are demonstrated using simulated as well as real data. The snaGe model is a general tool allowing for a wide range of applications in biomedical data analysis.
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