20.500.12556/DiRROS-15311 A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups. neuroprosthetics brain-computer interface machine learning electroencephalography data-driven learning lower limb amputation nevroprostetika učenje na podlagi podatkov amputacija spodnjega uda true false true Angleški jezik Ni določen © 2022 Dillen, Lathouwers, Miladinovic, Marusic, Ghaari, Romain, Meeusen and De Pa Neznano 2022-07-21 13:04:08 2022-07-21 13:04:08 2022-09-07 14:35:33 0000-00-00 00:00:00 2022 0 0 Nasl. z nasl. zaslona; Soavtorji: Elke Lathouwers, Aleksandar Miladinović, Uros Marusic, Fakhredinne Ghaffari, Olivier Romain, Romain Meeusen, Kevin De Pauw; Opis vira z dne 20. 7. 2022; str. 1-15 art. 949224 Vol. 16 Jul. 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 2022-06-30 2022-07-19 615.477.22:004.85 1662-5161 10.3389/fnhum.2022.949224 116118019 ZRSKP_Dillen_Arnau_i2022.pdf ZRSKP_Dillen_Arnau_i2022.pdf 1 CA23B57241CA67C0CDF770C944FE66DE 3c7a9fb5795dfb7e3f070bdb466236deaf1ef7d03dbbc114446b85382dd89d10 f877b92e-17b6-11ed-b6b8-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=20162 https://doi.org/10.3389/fnhum.2022.949224 1 https://dirros.openscience.si/Dokument.php?lang=slv&id=20160 Znanstveno-raziskovalno središče Koper / Centro di Ricerche Scientifiche Capodistria 0 0 0