Oskari Ahola

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 investigates how pre-stimulus mu rhythm dynamics affect the consistency of corticospinal responses to TMS, finding that mu power and functional connectivity reduce MEP variability.

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.

This paper develops an automated algorithm for determining motor hotspots and thresholds in TMS, achieving faster and more accurate results than previous methods.

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