An automated derivative-based method for detection of motor evoked potential onset latencies in multi-muscle...

Motor evoked potential (MEP) onset latency is a useful neurophysiological measure, but manual measurement is time-consuming in transcranial magnetic stimulation (TMS) studies with large numbers of trials. This is particularly relevant in cortical mapping studies recording from multiple muscles simultaneously, where automated methods could support more scalable analyses. Existing onset-detection methods have shown promise in more restricted datasets, but their performance in heterogeneous multi-muscle mapping data remains uncertain. In this pilot validation study, three healthy adults underwent TMS cortical mapping with simultaneous EMG recording from eight upper-limb muscles during resting and active conditions, yielding 3,840 EMG epochs. Three independent raters classified MEP presence and marked onset latency for all trials. Inter-rater agreement was assessed using Fleiss' κ for detection and ICC(2,1) for latency. Human majority vote for MEP presence and mean latency ratings were used as the reference standard to benchmark a novel derivative-ratio algorithm against an existing method. Human raters showed moderate to strong agreement for MEP detection (Fleiss' κ = 0.69) and high reliability for latency ratings (ICC(2,1) = 0.95), with a pooled mean absolute pairwise difference of 0.88 ms. The derivative-ratio algorithm showed strong detection performance and human-like latency estimates, outperforming a previously published algorithm. These pilot validation data suggest that the derivative-ratio method provides promising automatic MEP onset latency detection in complex multi-muscle cortical mapping data and may provide a basis for scalable latency analysis. By enabling reproducible extraction of conduction-related MEP features, this approach may support future biomarker studies in neurological disorders characterised by altered corticospinal excitability or corticospinal conduction, pending further validation in larger and clinically diverse datasets.