Personalized real-time inference of momentary excitability from human EEG

Abstract

The efficacy of transcranial magnetic stimulation (TMS) is often limited by non-adaptive protocols that disregard instantaneous brain states, potentially constraining therapeutic outcomes. Current EEG-guided approaches are hindered by their reliance on motor-evoked potentials (MEPs), which confound cortical and spinal excitability and restrict applications to the motor cortex, and a dependence on static biomarkers that cannot adapt to changing neurophysiological patterns. We introduce PRIME (Personalized Real-time Inference of Momentary Excitability), a deep learning framework that predicts cortical excitability, quantified by TMS-evoked potential (TEP) amplitude, from raw EEG signals. By targeting cortical excitability directly, PRIME provides a framework that could potentially extend brain state-dependent stimulation beyond the motor cortex, though validation in other cortical regions remains to be established. PRIME incorporates transfer learning and continual adaptation to automatically identify personalized biomarkers, allowing stimulation timing to be adapted across individuals and sessions. PRIME successfully predicts cortical excitability with minimal latency, providing a computational foundation for next-generation, personalized closed-loop TMS interventions.