130139 20241125101150.0 doi 10.1109/TNSRE.2023.3315146 sideral 136504 ART-2023-136504 eng Lubel, Emma Non-linearity in motor unit velocity twitch dynamics: implications for ultrafast ultrasound source separation 2023 Access copy available to the general public Unrestricted Ultrasound (US) muscle image series can be used for peripheral human-machine interfacing based on global features, or even on the decomposition of US images into the contributions of individual motor units (MUs). With respect to state-of-the-art surface electromyography (sEMG), US provides higher spatial resolution and deeper penetration depth. However, the accuracy of current methods for direct US decomposition, even at low forces, is relatively poor. These methods are based on linear mathematical models of the contributions of MUs to US images. Here, we test the hypothesis of linearity by comparing the average velocity twitch profiles of MUs when varying the number of other concomitantly active units. We observe that the velocity twitch profile has a decreasing peak-to-peak amplitude when tracking the same target motor unit at progressively increasing contraction force levels, thus with an increasing number of concomitantly active units. This observation indicates non-linear factors in the generation model. Furthermore, we directly studied the impact of one MU on a neighboring MU, finding that the effect of one source on the other is not symmetrical and may be related to unit size. We conclude that a linear approximation is partly limiting the decomposition methods to decompose full velocity twitch trains from velocity images, highlighting the need for more advanced models and methods for US decomposition than those currently employed. info:eu-repo/grantAgreement/EC/H2020/899822/EU/Ultrasound peripheral interface and in-vitro model of human somatosensory system and muscles for motor decoding and restoration of somatic sensations in amputees/SOMA This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 899822-SOMA info:eu-repo/grantAgreement/ES/MICINN/RYC2021-031905-I info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 4.8 2023 REHABILITATION 5 / 170 = 0.029 2023 Q1 T1 ENGINEERING, BIOMEDICAL 32 / 123 = 0.26 2023 Q2 T1 1.315 2023 Biomedical Engineering 2023 Q1 Computer Science Applications 2023 Q1 Rehabilitation 2023 Q1 Medicine (miscellaneous) 2023 Q1 Neuroscience (miscellaneous) 2023 Q1 Internal Medicine 2023 Q1 8.6 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Sgambato, Bruno Grandi Rohlén, Robin Ibánez, Jaime Universidad de Zaragoza (orcid)0000-0001-8439-151X Barsakcioglu, Deren Y. Tang, Meng-Xing Farina, Dario 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 31 (2023), 3699-3710 IEEE trans. neural syst. rehabil. eng. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 1534-4320 14847574 http://zaguan.unizar.es/record/130139/files/texto_completo.pdf Versión publicada 3492503 http://zaguan.unizar.es/record/130139/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:130139 articulos driver 2024-11-22-12:06:42 ARTICLE