Ulf Ziemann

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 shows that pre-stimulus aperiodic neural activity, not periodic alpha/beta power, better predicts cortical excitability measured via TMS-EEG in motor and prefrontal regions.

The paper investigates how sleep spindles affect corticospinal excitability and GABAergic inhibition during NREM sleep using TMS and EEG, finding no significant phase-dependent modulation of SICI.

This study uses EEG-triggered rTMS during specific sleep spindle states to induce motor cortical plasticity, finding that spindle-trough stimulation consistently reduces corticospinal excitability.

This paper demonstrates theta-phase-locked fronto-parietal cortico-cortical paired associative stimulation (ccPAS) in humans, showing distinct changes in EEG activity and network connectivity compared to controls.

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 investigates how aperiodic and periodic EEG features influence corticospinal excitability measured by motor evoked potentials in EEG-TMS experiments.

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.

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

This study investigates how the phase of the ongoing micro-rhythm in motor cortices affects long-term changes in interhemispheric inhibition using cortico-cortical paired associative stimulation.

The paper uses real-time spindle detection to show that sleep spindles suppress corticospinal excitability during their falling flank, contributing to phase-dependent plasticity.

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

This study investigates how the orientation of TMS-induced electric fields affects short-interval intracortical inhibition and facilitation in the motor cortex, finding optimal orientations for maximizing MEP suppression and facilitation.

This study investigates how pre-stimulation EEG connectivity of the primary motor cortex relates to motor network state and its impact on transcranial magnetic stimulation responses.

The paper investigates how PMd stimulation affects M1 neuroplasticity in young and older adults, finding that PMd disrupts specific I-wave facilitation in both groups.

The paper develops a multistage analysis approach to identify altered phase- and amplitude-coupling in Multiple Sclerosis using MEG data, achieving 84% classification accuracy between patients and controls.

This study shows that artifacts in EEG signals are more informative than brain activity for classification, highlighting the need for careful feature design in BCI applications.

This paper decodes 5 finger movements from 2 EMG channels with 94% accuracy using kNN and demonstrates real-time application in VR for HCI.

This study uses TMS-EEG to show that anti-glutamatergic drugs affect specific TEP components, indicating distinct roles for NMDA and AMPA receptors in cortical excitability.

The paper compares two EEG filtering methods for extracting the μ-rhythm and assesses their impact on corticospinal excitability measured via TMS.

The paper identifies low-frequency coding variants contributing to multiple sclerosis risk using a large case-control study.

The paper uses EEG-triggered TMS to show that motor evoked potentials are more modulated by micro-rhythm phase at weaker stimulation intensities, enabling better differentiation of brain states.

This study investigates how the spectral features of TEPs from M1 TMS depend on MEP amplitude, finding a late ERD linked to MEP strength.

This paper reviews tDCS studies on motor learning and memory, highlighting moderate effects, variability, and the need for improved reproducibility and standardization.