The brain’s electrical activity exhibits both periodic and aperiodic properties. Research decomposes neural spectra into canonical frequency band oscillations and a component that decays inversely to frequency. The innovative data-driven technique SpecParam by Donoghue et al. (2020) parameterizes neural signals into these periodic and aperiodic components. This method does not directly incorporate prior knowledge of the periodic activity’s expected location, which limits interpretability and performance, particularly on broadband neural spectra. To address these limitations, we developed the Spectral Parameterization for the Broadband Analysis of Neural Data (SP-BAND) algorithm. SP-BAND incorporates canonical frequency band information by constraining the oscillatory peaks to specified frequency bands before parameterizing the signal. We demonstrate that SP-BAND better fits to broadband noisy data than SpecParam on simulated and real EEG power spectra with the same number of parameters. To illustrate biomarker discovery with SP-BAND, we analyzed EEG data from mild traumatic brain injury subjects, which often manifests without imaging biomarkers. While the biomarkers identified by SP-BAND were not statistically discriminative on holdout data, they offer directions for future research. By incorporating canonical frequency bands, SP-BAND advances analysis methods for intricate brain activity across broad frequency bands, with potential application for detecting dysfunction in numerous neurological conditions. The open source code to use SP-BAND is available at https://github.com/aroyphillips/SP-BAND