Independent EEG components are meaningful (for BCI based on motor imagery)
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Independent EEG components are meaningful (for BCI based on motor imagery)
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science 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)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Network World
ISSN
1210-0552
e-ISSN
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Volume of the periodical
31
Issue of the periodical within the volume
5
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
21
Pages from-to
355-375
UT code for WoS article
000739166400004
EID of the result in the Scopus database
2-s2.0-85123375812