Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance

Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance

Riemannian geometry-based classification (RGBC) gained popularity in the field of brain-computer interfaces (BCIs) lately, due to its ability to deal with non-stationarities arising in electroencephalography (EEG) data. Domain adaptation, however, is most often performed on sample covariance matrice...

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Bibliographic Details
Main Authors: Frigyes Samuel Racz, Satyam Kumar, Zalan Kaposzta, Hussein Alawieh, Deland Hu Liu, Ruofan Liu, Akos Czoch, Peter Mukli, José del R. Millán
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-03-01
Series:Frontiers in Neuroscience
Subjects:
brain-computer interface
detrended cross-correlation analysis
Reimannian geometry
motor imagery
detrended fluctuation analysis
fractal connectivity
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2024.1271831/full

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https://www.frontiersin.org/articles/10.3389/fnins.2024.1271831/full

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