R. Rohlen

Identification of motor unit discharges from ultrasound images: Analysis of in silico and in vivo experiments

This study evaluates decoding algorithms for identifying motor unit discharges from ultrasound images under non-ideal conditions, finding they remain accurate despite varying twitch shapes and fusion.

Accurate Identification of Motoneuron Discharges from Ultrasound Images Across the Full Muscle Cross-Section

This paper develops a method to accurately identify motoneuron discharges from ultrasound images across the full muscle cross-section, achieving high agreement with surface EMG decomposition.

Combining high-density electromyography and ultrafast ultrasound to assess individual motor unit properties in vivo

This study compares two methods for identifying motor unit displacement areas using HDsEMG and UUS, finding that STICA offers more precise results despite higher computational complexity.

Spatial decomposition of ultrafast ultrasound images to identify motor unit activity - A validation study using intramuscular and surface EMG

This study validates blind source separation methods on ultrafast ultrasound images to identify motor unit activity using EMG data from eleven participants.

Spatially repeatable components from ultrafast ultrasound are associated with motor unit activity in human isometric contractions

This study uses ultrafast ultrasound to identify motor unit activity by analyzing spatial map repeatability, finding that repeatable components correlate with motor units and can reduce data.

Non-linearity in motor unit velocity twitch dynamics: Implications for ultrafast ultrasound source separation

The paper investigates non-linear dynamics in motor unit velocity twitch profiles using ultrasound data, showing that linear models are insufficient for accurate decomposition.

A fast blind source separation algorithm for decomposing ultrafast ultrasound images into spatiotemporal muscle unit kinematics

This paper introduces a fast blind source separation algorithm for decomposing ultrasound images into spatiotemporal muscle unit kinematics, showing improved speed and correlation compared to existing methods.

Estimating the neural spike train from an unfused tetanic signal of low threshold motor units using convolutive blind source separation

The paper estimates spike trains from unfused tetanic signals using convolutive blind source separation, showing high agreement with simulated and reference data.

Optimization and comparison of two methods for spike train estimation in an unfused tetanic contraction of low threshold motor units

The paper optimizes and compares two spike train estimation methods, finding that the continuous Haar wavelet transform method (HWM) outperforms the band-pass filter method (BPM) in accuracy and consistency.