Dania Humaidan

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.

This study uses pharmaco-EEG to show diazepam alters alpha oscillation shape features in specific brain regions, supporting the neurophysiological relevance of waveform shape in non-invasive human recordings.

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.

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