Sara J. Hussain

The paper compares micro-online and micro-offline gains in implicit motor sequence learning and finds that only micro-online gains correlate with skill improvement.

This study identifies EEG brain states reflecting ipsilesional M1 excitability after stroke, finding group-level mu suppression and inconsistent individual patterns.

The paper shows that including motor preparation time in micro-offline gain calculations changes the sign of the gain and strengthens its correlation with learning.

The paper shows that motor sequence learning induces corticospinal plasticity most prominent during mu peak phases, correlating with learning.

The paper develops a machine learning-based EEG-TMS system to predict corticospinal tract activation in real-time using personalized brain activity patterns in healthy adults.

This study uses machine learning to classify individual motor cortical excitability states from EEG signals, showing personalized classifiers can predict high and low excitability states with 80% accuracy.

The paper investigates the timing of motor command release during sensorimotor beta rhythms and finds preferential release at ~120 degrees of the beta cycle.

This study identifies TMS-evoked EEG potentials (TEPs) that are site- and stimulation-specific by analyzing cosine similarity across different stimulation sites and active vs. sham TMS.

This study investigates how beta rhythm events relate to corticospinal excitability and finds they predict MEP amplitudes but do not add variance beyond mean beta power.

This paper evaluates offline coil position denoising to improve detection of TMS effects by reducing variability from neuronavigated coil deviations.