1 citations · 1 across the 2 of their papers we have counts for
6 papers
NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models
Konstantinos Barmpas, Na Lee, Dimitrios Chalatsis +7
Biosignals such as electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) encode physiological activity across multiple temporal and spectral scales, y…
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…
Intramuscular microelectrode arrays enable highly-accurate neural decoding of hand movements
Agnese Grison, Jaime Ibanez Pereda, Silvia Muceli +5
Decoding the activity of the nervous system is a critical challenge in neuroscience and neural interfacing. In this study, we present a neuromuscular recording system that enables…
Spiking Neural Network Decoders of Finger Forces from High-Density Intramuscular Microelectrode Arrays
Farah Baracat, Agnese Grison, Dario Farina +2
Restoring naturalistic finger control in assistive technologies requires the continuous decoding of motor intent with high accuracy, efficiency, and robustness. Here, we present a…
Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios
Shihan Ma, Bo Hu, Tianyu Jia +5
The cortico-spinal neural pathway is fundamental for motor control and movement execution, and in humans it is typically studied using concurrent electroencephalography (EEG) and e…
Unlocking the Full Potential of High-Density Surface EMG: Novel Non-Invasive High-Yield Motor Unit Decomposition
Agnese Grison, Irene Mendez Guerra, Alexander Kenneth Clarke +3
The decomposition of high-density surface electromyography (HD-sEMG) signals into motor unit discharge patterns has become a powerful tool for investigating the neural control of m…