BCI-Based Motor Imagery EEG Signal Classification Using a Novel Method (EEG-ITT) in Upper-Limb Exoskeleton

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

BCI-Based Motor Imagery EEG Signal Classification Using a Novel Method (EEG-ITT) in Upper-Limb Exoskeleton

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

BCI-Based Motor Imagery EEG Signal Classification Using a Novel Method (EEG-ITT) in Upper-Limb Exoskeleton

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

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

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

ISBN

979-8-3503-8622-6

ISSN

2156-1125

e-ISSN

2156-1133

Počet stran výsledku

5

Strana od-do

4199-4203

Název nakladatele

IEEE

Místo vydání

Lisabon

Místo konání akce

Lisabon

Datum konání akce

3. 12. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—