Digital repository of Slovenian research organisations

Show document
A+ | A- | Help | SLO | ENG

Title:A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics
Authors:ID Dillen, Arnau (Author)
ID Lathouwers, Elke (Author)
ID Miladinović, Aleksandar (Author)
ID Marušič, Uroš (Author)
ID Ghaffari, Fakhreddine (Author)
ID Romain, Olivier (Author)
ID Meeusen, Romain (Author)
ID De Pauw, Kevin (Author)
Files:.pdf PDF - Presentation file, download (858,15 KB)
MD5: CA23B57241CA67C0CDF770C944FE66DE
 
URL URL - Source URL, visit https://doi.org/10.3389/fnhum.2022.949224
 
Language:English
Typology:1.01 - Original Scientific Article
Organization:Logo ZRS Koper - Science and Research Centre Koper
Abstract: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.
Keywords:neuroprosthetics, brain-computer interface, machine learning, electroencephalography, data-driven learning, lower limb amputation
Publication status:Published
Publication version:Version of Record
Article acceptance date:30.06.2022
Publication date:19.07.2022
Year of publishing:2022
Number of pages:str. 1-15
Numbering:Vol. 16, art. 949224
PID:20.500.12556/DiRROS-15311 New window
UDC:615.477.22:004.85
ISSN on article:1662-5161
DOI:10.3389/fnhum.2022.949224 New window
COBISS.SI-ID:116118019 New window
Copyright: © 2022 Dillen, Lathouwers, Miladinovic, Marusic, Ghaari, Romain, Meeusen and De Pa
Note: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;
Publication date in DiRROS:21.07.2022
Views:935
Downloads:642
Metadata:XML DC-XML DC-RDF
:
Copy citation
  
Share:Bookmark and Share


Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

Record is a part of a journal

Title:Frontiers in human neuroscience
Shortened title:Front. hum. neurosci.
Publisher:Frontiers Research Foundation
ISSN:1662-5161
COBISS.SI-ID:49074786 New window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:19.07.2022

Secondary language

Language:Undetermined
Keywords:nevroprostetika, učenje na podlagi podatkov, amputacija spodnjega uda


Back