Independent EEG components are meaningful (for BCI based on motor imagery)

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00549871" target="_blank" >RIV/67985807:_____/21:00549871 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.14311/NNW.2021.31.020" target="_blank" >http://dx.doi.org/10.14311/NNW.2021.31.020</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2021.31.020" target="_blank" >10.14311/NNW.2021.31.020</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Independent EEG components are meaningful (for BCI based on motor imagery)

Popis výsledku v původním jazyce

Eight methods of decomposition of a multichannel EEG signal are compared in terms of their ability to identify the most physiologically significant components. The criterion for the meaningfulness of a method is its ability to reduce mutual information between components, to create components that can be attributed to the activity of dipoles located in the cerebral cortex, find components that are provided by other methods and for this case, and, at the same time, these components should most contribute to the accuracy of the BCI based on imaginary movement. Independent component analysis methods AMICA, RUNICA and FASTICA outperform others in the first three criteria and are second only to the Common Spatial Patterns method in the fourth criterion. The components created by all methods for 386 experimental sessions of 27 subjects were combined into more than 100 clusters containing more than 10 elements. Additionally, the components of the 12 largest clusters were analyzed. They have proven to be of great importance in controlling BCI, their origins can be modeled using dipoles in the brain, and they have been detected by several degradation methods. Five of the 12 selected components have been identified and described in our previous articles. Even if the physiological and functional origins of the rest of identified components are to be the subject of further research, we have shown that their physiological nature is at least highly probable.

Název v anglickém jazyce

Independent EEG components are meaningful (for BCI based on motor imagery)

Popis výsledku anglicky

Eight methods of decomposition of a multichannel EEG signal are compared in terms of their ability to identify the most physiologically significant components. The criterion for the meaningfulness of a method is its ability to reduce mutual information between components, to create components that can be attributed to the activity of dipoles located in the cerebral cortex, find components that are provided by other methods and for this case, and, at the same time, these components should most contribute to the accuracy of the BCI based on imaginary movement. Independent component analysis methods AMICA, RUNICA and FASTICA outperform others in the first three criteria and are second only to the Common Spatial Patterns method in the fourth criterion. The components created by all methods for 386 experimental sessions of 27 subjects were combined into more than 100 clusters containing more than 10 elements. Additionally, the components of the 12 largest clusters were analyzed. They have proven to be of great importance in controlling BCI, their origins can be modeled using dipoles in the brain, and they have been detected by several degradation methods. Five of the 12 selected components have been identified and described in our previous articles. Even if the physiological and functional origins of the rest of identified components are to be the subject of further research, we have shown that their physiological nature is at least highly probable.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

—

Svazek periodika

31

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

21

Strana od-do

355-375

Kód UT WoS článku

000739166400004

EID výsledku v databázi Scopus

2-s2.0-85123375812