A common analysis measure for neuro-electrophysiological recordings is to compute the power ratio between two frequency bands. Applications of band ratio measures include investigations of cognitive processes as well as biomarkers for conditions such as attention-deficit hyperactivity disorder. Band ratio measures are typically interpreted as reflecting quantitative measures of periodic, or oscillatory, activity, which implicitly assumes that a ratio is measuring the relative powers of two distinct periodic components that are well captured by predefined frequency ranges. However, electrophysiological signals contain periodic components and a 1/f-like aperiodic component, which contributes power across all frequencies. In this work, we investigate whether band ratio measures reflect power differences between two oscillations, as intended. We examine to what extent ratios may instead reflect other periodic changes - such as in center frequency or bandwidth - and/or aperiodic activity. We test this first in simulation, exploring how band ratio measures relate to changes in multiple spectral features. In simulation, we show how multiple periodic and aperiodic features affect band ratio measures. We then validate these findings in a large electroencephalography (EEG) dataset, comparing band ratio measures to parameterizations of power spectral features. In EEG, we find that multiple disparate features influence ratio measures. For example, the commonly applied theta / beta ratio is most reflective of differences in aperiodic activity, and not oscillatory theta or beta power. Collectively, we show how periodic and aperiodic features can drive the same observed changes in band ratio measures. Our results demonstrate how ratio measures reflect different features in different contexts, inconsistent with their typical interpretations. We conclude that band ratio measures are non-specific, conflating multiple possible underlying spectral changes. Explicit parameterization of neural power spectra is better able to provide measurement specificity, elucidating which components of the data change in what ways, allowing for more appropriate physiological interpretations.
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