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Review of solutions for the application of example of machine learning methods for Motor Imagery in correlation with Brain-Computer Interfaces

2021-11-02Artigo científico Acesso restrito
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg15:Proteger a Vida Terrestre
dc.contributor.authorPaszkiel, Szczepan
dc.contributor.authorRojek, Ryszard
dc.contributor.authorLei, Ningrong
dc.contributor.authorCastro, Maria Antonio
dc.date.accessioned2026-04-30T13:33:32Z
dc.date.available2026-04-30T13:33:32Z
dc.date.issued2021-11-02
dc.descriptionLink de acesso ao documento: https://scholar.google.com/scholar?q=Review%20of%20solutions%20for%20the%20application%20of%20example%20of%20machine%20learning%20methods%20for%20Motor%20Imagery%20in%20correlation%20with%20Brain-Computer%20Interfaces
dc.description.abstractPresently, numerous public databases presenting the collected EEG signals, including the ones in the scope of Motor Imagery (MI), are available. Simultaneously, machine-learning methods, which enable effective and fast discovering of information, also in the sets of biomedical data, are constantly being developed. In this paper, a set of 30 of some of the latest scientific publications from the years 2016-2021 has been analyzed. The analysis covered, among others: public data repositories in the form of EEG signals as input data; numbers and types of the analyzed tasks in the scope of MI in the above-mentioned databases; and Deep Learning (DL) architectures.eng
dc.description.abstractObecnie dostępne są liczne ogólnodostępne bazy danych prezentujące zebrane sygnały EEG, w tym z zakresu obrazowania motorycznego (MI). Jednocześnie stale rozwijane są metody uczenia maszynowego, które umożliwiają efektywne i szybkie odkrywanie informacji, także w zbiorach danych biomedycznych. W niniejszym artyule przeanalizowano zestaw 30 spośród najnowszych publikacji naukowych z lat 2016-2021. Analizie poddano m.in.: publiczne repozytoria danych w postaci sygnałów EEG jako dane wejściowe; liczby i rodzaje analizowanych zadań z zakresu obrazowania motorycznego w ww. bazach; i architektury Deep Learning (DL). (Przegląd rozwiązań do zastosowania metod uczenia maszynowego na potrzeby obrazowania motorycznego w korelacji z interfejsami mózg-komputer).mul
dc.description.sponsorshipThis research was funded in whole by National Science Center, [Grant number: 2021/05/X/ST7/00034, MINIATURA 5]. Project title: Pilot research on the application of a training system based on motor imagery in the field of real object control.
dc.identifier.citationPaszkiel, S., Rojek, R., Lei, N., & Castro, M. A. (2021). Review of solutions for the application of example of machine learning methods for Motor Imagery in correlation with Brain-Computer Interfaces. Przegląd Elektrotechniczny, 97. DOI: https://doi.org/10.15199/48.2021.11.20.
dc.identifier.doi10.15199/48.2021.11.20
dc.identifier.eissn2449-9544
dc.identifier.issn0033-2097
dc.identifier.urihttp://hdl.handle.net/10400.8/16219
dc.language.isoeng
dc.peerreviewedyes
dc.publisherWydawnictwo SIGMA-NOT, sp. z.o.o.
dc.relation.hasversionhttps://sigma-not.pl/publikacja-134132-2021-11.html
dc.relation.ispartofPRZEGLĄD ELEKTROTECHNICZNY
dc.rights.uriN/A
dc.subjectMotor Imagery
dc.subjectEEG
dc.subjectBCI
dc.subjectmachine learning
dc.subjectdeep learning
dc.subjectdeep neural networks
dc.subjectobrazowanie motoryczne
dc.subjectuczenie maszynowe
dc.subjectgłębokie uczenie
dc.subjectgłębokie sieci neuronowe
dc.titleReview of solutions for the application of example of machine learning methods for Motor Imagery in correlation with Brain-Computer Interfaceseng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage116
oaire.citation.issue11
oaire.citation.startPage111
oaire.citation.titlePrzeglad Elektrotechniczny
oaire.citation.volume1
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCastro
person.givenNameMaria António
person.identifier851910
person.identifier.ciencia-id3914-A4F3-5395
person.identifier.orcid0000-0002-1500-355X
person.identifier.ridK-7369-2013
person.identifier.scopus-author-id55195135300
relation.isAuthorOfPublication5cfd85f0-ad20-4c05-bb00-c6ba98b4cbdf
relation.isAuthorOfPublication.latestForDiscovery5cfd85f0-ad20-4c05-bb00-c6ba98b4cbdf

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Presently, numerous public databases presenting the collected EEG signals, including the ones in the scope of Motor Imagery (MI), are available. Simultaneously, machine-learning methods, which enable effective and fast discovering of information, also in the sets of biomedical data, are constantly being developed. In this paper, a set of 30 of some of the latest scientific publications from the years 2016-2021 has been analyzed. The analysis covered, among others: public data repositories in the form of EEG signals as input data; numbers and types of the analyzed tasks in the scope of MI in the above-mentioned databases; and Deep Learning (DL) architectures.
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