Markus Siegel

This study shows that deficits in extended high-frequency hearing reduce cortical response amplitudes to speech, even in young adults, and highlights the need to include EHF hearing in clinical assessments and hearing aid design.

The paper compares complexity-based metrics from surface electromyography (sEMG) and magnetomyography (MMG) during muscle fatigue, finding that complexity captures signal organization changes not explained by amplitude or spectral features.

This paper identifies electrophysiological lag threads in resting-state MEG data, revealing a frequency-specific temporal hierarchy of the human cortex that links fast neural dynamics to slower hemodynamic propagation.

This study compares motor unit number estimation using optically pumped magnetometer-based magnetomyography (OPM-MMG) and surface electromyography (EMG), finding that OPM-MMG can estimate motor unit numbers contactlessly with comparable accuracy when signal-to-noise ratio is equalized.

The paper investigates how vlPFC encodes visual information in two stages during natural vision, showing an early coarse estimate followed by a rich representation.

This study uses an artificial neural network to decode visual functions from invasive EEG data in monkeys, identifying region-specific roles in color, shape, and motion processing.

This paper demonstrates that a portable OPM-MMG system can decode fine finger movements and recover EMG-like patterns without skin contact or a magnetically shielded room.

The paper investigates how different visual features contribute to neural selectivity for natural stimuli using MEG and multivariate pattern analysis.

The paper shows that brain regions and deep learning models stretch representations along task-relevant dimensions to optimize performance through error minimization.

This paper introduces a method to measure tCS effects by comparing neural activity during simultaneous stimulation with predictions from linear superposition, finding phase-dependent interactions between tACS and visual flicker.

The paper investigates how action requirements influence temporal prediction errors in the human brain using MEG and computational modeling.

This study shows humans can localize sounds without primary interaural cues, unlike deep neural networks, highlighting differences in auditory processing strategies.

This study demonstrates that magnetomyography (MMG) signals can detect individual motor units non-invasively using SQUIDs and OPMs, validated by simultaneous intramuscular EMG recordings.

The paper shows that abstract choice representations exist in the human brain even when choice-response mappings are stable, using MEG during a motion discrimination task.

The paper introduces a framework for comparing neural population activity using shape distances, demonstrating its application across species and behavioral tasks.

This study shows predictive learning reshapes the brain's representational geometry to align with sensory input statistics, with prediction error encoding strength correlating with representational shifts.

This study investigates cortico-subcortical dynamics in primate working memory by recording neural activity during color and spatial tasks, revealing differential encoding and task-specific interactions.

This study introduces a novel spectral waveform analysis method to identify distinct human cortical alpha rhythms and finds at least four previously unknown alpha rhythms in specific brain regions.

This study compares optically pumped magnetometers (OPM) and EMG for finger movement discrimination, finding both achieve over 89% accuracy with high agreement.

This study investigates neural representations of speech content and production using MEG, finding dissociable patterns in the left hemisphere with dynamic production signals and stable content signals.

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 investigates phase-amplitude coupling in resting-state MEG, showing that non-harmonic coupling is absent and harmonic coupling is widespread.

The paper investigates fractal neuronal activity in the human brain using MEG and finds that its cortical correlation structure is distinct from oscillatory activity.

The paper shows that abstract perceptual choices are represented in the human brain in a motor-independent manner, even when linked to actions, using MEG and multivariate decoding.

The paper investigates how motion coherence and luminance contrast interact to drive gamma-band activity in visual cortex using MEG, finding a multiplicative interaction supporting a gain mechanism.

The paper shows that task states reduce neural correlations and variability across cortical areas in both primate and human data, increasing information coding capacity.

The paper systematically compares cortical phase- and amplitude-coupling patterns in the human brain, finding they differ across frequencies and regions and reflect distinct neuronal mechanisms.

The paper identifies four functionally distinct cell classes in primate cortex using extracellular spike waveform clustering during a visuomotor task.

The paper models neural interactions during a sensorimotor decision-making task using deep neural networks and compares brain responses to domain geometry and deep network predictions.

The paper uses monkey EEG and MEG to show similar neural processing of color and motion across species and spatial scales.