Lisa Haxel

This paper introduces EOIS, a framework to separate evoked, ongoing, and induced neural activity from single-trial data, enabling component-resolved analysis and applications in biomarker refinement and neuromodulation.

This study evaluates a reusable non-adhesive wearable that estimates respiratory rate during rest and exercise using acoustic and inertial sensing, showing high accuracy in most conditions.

The paper develops a method to predict TMS-evoked responses using pre-stimulus EEG features and finds that alpha, beta, and gamma oscillations correlate with TEP and MEP amplitudes.

PRIME predicts cortical excitability from EEG in real-time, enabling personalized TMS interventions by adapting to individual brain states.

NeuroSimo provides open-source software for real-time closed-loop EEG-TMS stimulation, enabling flexible and precise control with low timing error.

This study develops a machine learning framework to predict motor excitability states using EEG features, achieving 72% mean accuracy in healthy participants.