BCI-Based Motor Imagery EEG Signal Classification Using a Novel Method (EEG-ITT) in Upper-Limb Exoskeleton
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43975639" target="_blank" >RIV/49777513:23520/24:43975639 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10947606" target="_blank" >https://ieeexplore.ieee.org/document/10947606</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/BIBM62325.2024.10947606" target="_blank" >10.1109/BIBM62325.2024.10947606</a>
Alternative languages
Result language
angličtina
Original language name
BCI-Based Motor Imagery EEG Signal Classification Using a Novel Method (EEG-ITT) in Upper-Limb Exoskeleton
Original language description
Brain-computer interface (BCI) is an emerging technology that receives, processes, and converts brain signals into commands sent to output devices to perform desired tasks. Motor imagery (MI) based on electroencephalograms (EEGs) is one of the most widely used BCI paradigms, and it has demonstrated potential as an effective tool for neurorehabilitation. Recently, neural networks-in particular, deep architectures-have received substantial attention for the analysis of EEG signals (BCI applications). This paper proposes a new classification algorithm called EEG-ITT to increase the accuracy of classification motor imagery EEG signals using a non-invasive brain-computer interface. Utilizing a motor imagery dataset of 29 healthy subjects, including males aged 2126 and females aged 18-23, the proposed model demonstrated the highest accuracy, at 79.53 %. The noise injection method has also been implemented for data augmentation.
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
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
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
ISBN
979-8-3503-8622-6
ISSN
2156-1125
e-ISSN
2156-1133
Number of pages
5
Pages from-to
4199-4203
Publisher name
IEEE
Place of publication
Lisabon
Event location
Lisabon
Event date
Dec 3, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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