Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 28; no. 5; pp. 1117 - 1127
• Main Authors: Zhang, Wen, Wu, Dongrui
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain-computer interfaces; Classification; Computational neuroscience; Covariance matrices; Covariance matrix; Domains; EEG; electroencephalogram; Electroencephalography; Feature extraction; Geometry; Human-computer interface; Interfaces; Knowledge management; Learning; Manifolds; Probability distribution; Riemann manifold; Riemannian manifold; Symmetric matrices; Task analysis; Transfer learning

Transfer learning makes use of data or knowledge in one problem to help solve a different, yet related, problem. It is particularly useful in brain-computer interfaces (BCIs), for coping with variations among different subjects and/or tasks. This paper considers offline unsupervised cross-subject el...