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The power spectrum, which characterizes these properties, is often summarized by partitioning frequencies into standard bands and averaging power within bands. These summary measures are regularly used for analysis in practice, but are not guaranteed to optimally retain differences in power spectra across signals from different participants. We propose a data\u2010adaptive method for identifying frequency band summary measures that preserve spectral variability within a population of interest. The method can also identify subpopulations with distinct power spectra and summary measures that best characterize local dynamics. Numerical selection criteria are developed to select a reasonable number of bands and subpopulations that best characterize overall dynamics. A genetic algorithm is designed to simultaneously identify subpopulations and their corresponding summary measures. 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