| Title: | A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics |
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| 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) |
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| Files: |  PDF - Presentation file, download (858,15 KB)
MD5: CA23B57241CA67C0CDF770C944FE66DE
 URL - Source URL, visit https://doi.org/10.3389/fnhum.2022.949224
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  ZRS Koper - Science and Research Centre Koper
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| 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. |
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| Keywords: | neuroprosthetics, brain-computer interface, machine learning, electroencephalography, data-driven learning, lower limb amputation |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Article acceptance date: | 30.06.2022 |
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| Publication date: | 19.07.2022 |
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| Year of publishing: | 2022 |
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| Number of pages: | str. 1-15 |
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| Numbering: | Vol. 16, art. 949224 |
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| PID: | 20.500.12556/DiRROS-15311  |
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| UDC: | 615.477.22:004.85 |
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| ISSN on article: | 1662-5161 |
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| DOI: | 10.3389/fnhum.2022.949224 |
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| COBISS.SI-ID: | 116118019 |
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| Copyright: | © 2022 Dillen, Lathouwers, Miladinovic, Marusic, Ghaari, Romain, Meeusen and De Pa |
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| 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;
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| Publication date in DiRROS: | 21.07.2022 |
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| Views: | 546 |
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| Downloads: | 382 |
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