Riemannian Procrustes Analysis: Transfer Learning for Brain-Computer Interfaces

Objective: This paper presents a Transfer Learning approach for dealing with the statistical variability of electroencephalographic (EEG) signals recorded on different sessions and/or from different subjects. This is a common problem faced by brain-computer interfaces (BCI) and poses a challenge for...

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Veröffentlicht in:	IEEE transactions on biomedical engineering Jg. 66; H. 8; S. 2390 - 2401
Hauptverfasser:	Rodrigues, Pedro Luiz Coelho, Jutten, Christian, Congedo, Marco
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.08.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Schlagworte:	Algorithms Brain-computer interface Brain-Computer Interfaces Calibration Classification Computer simulation covariance matrices Data points Datasets EEG Electroencephalography Electroencephalography - methods Engineering Sciences Geometry Human-computer interface Humans Interfaces Learning Manifolds Matching Mathematical analysis Matrix methods Reuse Riemannian geometry Scaling Shape Signal and Image processing Signal Processing, Computer-Assisted Statistical analysis Statistical distributions Statistics Symmetric matrices Transfer learning Riemannian geometry Brain-Computer Interface Transfer Learning EEG Covariance Matrices
ISSN:	0018-9294, 1558-2531, 1558-2531
Online-Zugang:	Volltext
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