Transfer Learning: A Riemannian Geometry Framework With Applications to Brain-Computer Interfaces.
Journal:
IEEE transactions on bio-medical engineering
Published Date:
Aug 21, 2017
Abstract
OBJECTIVE: This paper tackles the problem of transfer learning in the context of electroencephalogram (EEG)-based brain-computer interface (BCI) classification. In particular, the problems of cross-session and cross-subject classification are considered. These problems concern the ability to use data from previous sessions or from a database of past users to calibrate and initialize the classifier, allowing a calibration-less BCI mode of operation.