Automatic Motor Imagery Classification by CNN-Transformer-LSTM Using Multi-Channel EEG Signals
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43973266" target="_blank" >RIV/49777513:23520/24:43973266 - isvavai.cz</a>
Result on the web
<a href="https://ebooks.iospress.nl/doi/10.3233/FAIA241048" target="_blank" >https://ebooks.iospress.nl/doi/10.3233/FAIA241048</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3233/FAIA241048" target="_blank" >10.3233/FAIA241048</a>
Alternative languages
Result language
angličtina
Original language name
Automatic Motor Imagery Classification by CNN-Transformer-LSTM Using Multi-Channel EEG Signals
Original language description
The brain-computer interface (BCI) is a promising technology that could bring about a significant revolution in various fields, including healthcare and human enhancement. One commonly used BCI method in healthcare, particularly in rehabilitation, is the analysis of motor imagery (MI) through an electroencephalogram (EEG). Our study introduces a hybrid deep learning model called CNN-Transformer-LSTM, which utilizes multi-channel EEG signals to classify MI binary and multiclass automatically. Our experiments have shown that this proposed method is more effective than previous state-of-the-art studies at accurately classifying MI using multi-channel EEG signals.
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
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Frontiers in Artificial Intelligence and Applications
ISBN
978-1-64368-548-9
ISSN
0922-6389
e-ISSN
1879-8314
Number of pages
8
Pages from-to
4555-4562
Publisher name
IOS Press
Place of publication
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Event location
Santiago de Compostela, Španělsko
Event date
Oct 19, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001593512300578