Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach

Objective: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Met...

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Veröffentlicht in:	IEEE transactions on biomedical engineering Jg. 67; H. 2; S. 399 - 410
Hauptverfasser:	He, He, Wu, Dongrui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Alignment Brain Brain-computer interface Brain-Computer Interfaces Classification Computational neuroscience Computer Simulation Covariance matrices data alignment Data processing Databases, Factual EEG Electroencephalography Electroencephalography - classification Euclidean geometry Euclidean space Event-related potentials Evoked Potentials - physiology Feature extraction Humans Image classification Imagination - physiology Interfaces Learning algorithms Machine Learning Machine learning algorithms Mental task performance Microsoft Windows Riemannian geometry Signal processing Signal Processing, Computer-Assisted Task analysis Transfer learning
ISSN:	0018-9294, 1558-2531, 1558-2531
Online-Zugang:	Volltext
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