Classification of MI EEG Signal Using Deep Learning Architectures for a Lower-Limb Rehabilitation Exoskeleton

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43975790" target="_blank" >RIV/49777513:23520/25:43975790 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-92605-1_26" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-92605-1_26</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-92605-1_26" target="_blank" >10.1007/978-3-031-92605-1_26</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of MI EEG Signal Using Deep Learning Architectures for a Lower-Limb Rehabilitation Exoskeleton

Popis výsledku v původním jazyce

Recent advances in neuroscience and engineering have resulted in brain-computer interface (BCI) devices that enhance the quality of life for people with movement limitations. BCI enables external devices to perform tasks using brain signals that are received, processed, and converted into commands by the brain. A widely used BCI paradigm based on electroencephalograms (EEGs) is motor imagery (MI), which has demonstrated potential as a tool for neurorehabilitation. In recent years, deep learning architectures have gained considerable attention for their ability to analyze EEG signals. This review paper focuses on applying deep learning for MI EEG classification in controlling lower-limb rehabilitation exoskeletons. Finally, current issues and potential directions will be discussed.

Název v anglickém jazyce

Classification of MI EEG Signal Using Deep Learning Architectures for a Lower-Limb Rehabilitation Exoskeleton

Popis výsledku anglicky

Recent advances in neuroscience and engineering have resulted in brain-computer interface (BCI) devices that enhance the quality of life for people with movement limitations. BCI enables external devices to perform tasks using brain signals that are received, processed, and converted into commands by the brain. A widely used BCI paradigm based on electroencephalograms (EEGs) is motor imagery (MI), which has demonstrated potential as a tool for neurorehabilitation. In recent years, deep learning architectures have gained considerable attention for their ability to analyze EEG signals. This review paper focuses on applying deep learning for MI EEG classification in controlling lower-limb rehabilitation exoskeletons. Finally, current issues and potential directions will be discussed.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Intelligent Computing. CompCom 2025. Lecture Notes in Networks and Systems

ISBN

978-3-031-92604-4

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

12

Strana od-do

424-435

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Cham

Místo konání akce

Londýn

Datum konání akce

19. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001562650200026