Classification of MI EEG Signal Using Deep Learning Architectures for a Lower-Limb Rehabilitation Exoskeleton
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Classification of MI EEG Signal Using Deep Learning Architectures for a Lower-Limb Rehabilitation Exoskeleton
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Intelligent Computing. CompCom 2025. Lecture Notes in Networks and Systems
ISBN
978-3-031-92604-4
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
12
Pages from-to
424-435
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
Londýn
Event date
Jun 19, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001562650200026