A Type of EEG-ITNet for Motor Imagery EEG Signal Classification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43971595" target="_blank" >RIV/49777513:23520/24:43971595 - isvavai.cz</a>
Result on the web
<a href="https://www.scitepress.org/Link.aspx?doi=10.5220/0012569400003657" target="_blank" >https://www.scitepress.org/Link.aspx?doi=10.5220/0012569400003657</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0012569400003657" target="_blank" >10.5220/0012569400003657</a>

Result language
angličtina
Original language name
A Type of EEG-ITNet for Motor Imagery EEG Signal Classification
Original language description
The brain-computer interface (BCI) is an emerging technology that has the potential to revolutionize the world, with numerous applications ranging from healthcare to human augmentation. Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms used extensively in healthcare applications such as rehabilitation. Recently, neural networks, particularly deep architectures, have received substantial attention for analyzing EEG signals (BCI applications). EEG-ITNet is a classification algorithm proposed to improve the classification accuracy of motor imagery EEG signals in a noninvasive brain-computer interface. The resulting EEG-ITNet classification accuracy and precision were 75.45% and 76.43%, using a motor imagery dataset of 29 healthy subjects, including males aged 21-26 and females aged 18-23. Three different methods have also been implemented to augment this dataset.
Czech name
—
Czech description
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Type
O - Miscellaneous
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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