DSpace Repository

Some aspects of deep representation learning on transformed EEG data

Show simple item record

dc.contributor.author IAPĂSCURTĂ, Victor
dc.date.accessioned 2023-09-18T08:32:44Z
dc.date.available 2023-09-18T08:32:44Z
dc.date.issued 2023
dc.identifier.citation IAPĂSCURTĂ, Victor. Some aspects of deep representation learning on transformed EEG data. In: Conferinţa tehnico-ştiinţifică a studenţilor, masteranzilor şi doctoranzilor, Universitatea Tehnică a Moldovei, 5-7 aprilie 2023. Chișinău, 2023, vol. 2, pp. 207-211. ISBN 978-9975-45-956-3. ISBN 978-9975-45-957-7 (Vol.2). en_US
dc.identifier.isbn 978-9975-45-956-3
dc.identifier.isbn 978-9975-45-957-7
dc.identifier.uri http://repository.utm.md/handle/5014/24032
dc.description.abstract Visualizing high-dimensional datasets can be challenging. While it is possible to plot data in two or three dimensions to reveal the data's innate structure, analogous high-dimensional representations are significantly less understandable. A dataset's structure must be shown to some extent, hence the dimension must be decreased. Principal component analysis (PCA) and linear discriminant analysis (LDA) were the two historically the first methods. Several nonlinear techniques were afterwards developed, including locally linear embedding (LLE), multi-dimensional scaling (MDS), isometric feature mapping (Isomap), stochastic neighborhood embedding (t-SNE), etc. In the current study, several nonlinear representation learning techniques are used for electroencephalography (EEG) data with the ultimate objective of categorizing the EEG signal. en_US
dc.language.iso en en_US
dc.publisher Universitatea Tehnică a Moldovei en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject manifold learning en_US
dc.subject algorithmic complexity en_US
dc.subject EEG signal en_US
dc.subject machine learning en_US
dc.title Some aspects of deep representation learning on transformed EEG data en_US
dc.type Article en_US


Files in this item

The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

Search DSpace


Advanced Search

Browse

My Account