30 citations · 31 across the 3 of their papers we have counts for
4 papers
emgforge: an automated end-to-end pipeline for simulating surface EMG on MRI-based volume conductors
Dimitrios Halatsis, Noura Ezaz-Nikpay, Pranav Mamidanna +1
Simulated electromyograms are used to understand what an electrode records, to test decomposition and estimation algorithms on signals with known ground truth, and to train learnin…
A Biophysical-Model-Informed Source Separation Framework For EMG Decomposition
D. Halatsis, P. Mamidanna, J. Pereira +1
Recent advances in neural interfacing have enabled significant improvements in human-computer interaction, rehabilitation, and neuromuscular diagnostics. Motor unit (MU) decomposit…
Quasi-Linear ICA for Motor Unit Decomposition during Dynamic Contractions
Alexander Kenneth Clarke, Dimitrios Halatsis, Agnese Grison +6
Decomposing surface electromyography (EMG) into the spike trains of individual motor neurons is a long-standing inverse problem and a key step toward motor-neuron-driven neural int…
Markerless tracking of user-defined features with deep learning
Alexander Mathis, Pranav Mamidanna, Taiga Abe +4
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, y…