Rethinking noise floor characterisation in motor-evoked potentials

<i>Objective</i>. Motor-evoked potentials (MEPs) in response to brain stimulation, such as transcranial magnetic stimulation (TMS), allow quantification of corticospinal excitability and have served in the design of almost all available neuromodulatory interventions. So-called thresholding of MEPs at a point not too far above the noise floor establishes the reference point for dosage and safety. Despite the fundamental importance of distinguishing true MEPs from background noise, the statistical properties of the noise floor are hardly known or characterised. Moreover, detecting pre-activation of the motor system by endogenous signals before a stimulus-which substantially distorts the subsequent stimulation response-practically involves distinguishing spontaneous activity from the background noise. However, current methods for this detection are largely ad hoc. This study aims to determine the probability distribution of the noise floor.<i>Approach</i>. We tested four probability distribution models (log-normal, gamma, generalised extreme value (GEV), and normal) in experimental data from 19 healthy subjects. Additionally, we employed a mixture model of Gaussian and Laplacian distributions to simulate background electromyography signals and tested these models on the resulting distributions.<i>Main results</i>. The distribution of the background noise floor was highly skewed, which contradicted the common assumption of normality or even log-normality. The GEV distribution model consistently outperformed other models in describing both experimental and simulated data. The gamma distribution model performed similarly to the GEV model in simulations but emerged as the second-best option with experimental data.<i>Significance</i>. The GEV and gamma distribution models enable more accurate characterisation of the background noise floor. Improvements by using these models could enhance the precision and reliability of MEP detection criteria, pre-activation identification, and variability analysis in research and motor thresholding in clinical TMS for a more accurate interpretation of neurophysiological data.