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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

SN Computer Science

ISSN

2661-8907

e-ISSN

2661-8907

Svazek periodika

6

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

9

Strana od-do

1-9

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85219683342