Classification of EEG Signal Using Deep Learning Architectures Based Motor-Imagery for an Upper-Limb Rehabilitation Exoskeleton

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43975153" target="_blank" >RIV/49777513:23520/25:43975153 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s42979-025-03743-6" target="_blank" >https://link.springer.com/article/10.1007/s42979-025-03743-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s42979-025-03743-6" target="_blank" >10.1007/s42979-025-03743-6</a>

Result language

angličtina

Original language name

Classification of EEG Signal Using Deep Learning Architectures Based Motor-Imagery for an Upper-Limb Rehabilitation Exoskeleton

Original language description

The brain-computer interface (BCI) is an emerging technology that enables people with physical disabilities to control and interact with devices only by using their minds and without being dependent on healthy people. One of the most popular BCI paradigms, motor imagery (MI) based on electroencephalograms (EEGs), is applied in healthcare, including rehabilitation. A significant challenge in classifying EEG signals using deep learning methods is the accurate recognition of MI signals. CNN-LSTM and CNN-Transformer are two classification algorithms proposed to improve the classification accuracy of Motor Imagery EEG signals in a noninvasive brain-computer interface. Three different methods, including noise injection (NI), conditional variational autoencoder (cVAE), and conditional GAN with Wasserstein price function and gradient penalty (cWGAN-GP), have also been implemented to augment this dataset. The best accuracy was achieved by the CNN-LSTM model, which is 79.06%, using an MI dataset involving hand movements. The dataset included 29 healthy subjects, with males aged 21–26 and females aged 18–23.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

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)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

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

Name of the periodical

SN Computer Science

ISSN

2661-8907

e-ISSN

2661-8907

Volume of the periodical

6

Issue of the periodical within the volume

3

Country of publishing house

SG - SINGAPORE

Number of pages

9

Pages from-to

1-9

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85219683342