  The pattern of Electroencephalogram (EEG) signal differs significantly across
different subjects, and poses challenge for EEG classifiers in terms of 1)
effectively adapting a learned classifier onto a new subject, 2) retaining
knowledge of known subjects after the adaptation. We propose an efficient
transfer learning method, named Meta UPdate Strategy (MUPS-EEG), for continuous
EEG classification across different subjects. The model learns effective
representations with meta update which accelerates adaptation on new subject
and mitigate forgetting of knowledge on previous subjects at the same time. The
proposed mechanism originates from meta learning and works to 1) find feature
representation that is broadly suitable for different subjects, 2) maximizes
sensitivity of loss function for fast adaptation on new subject. The method can
be applied to all deep learning oriented models. Extensive experiments on two
public datasets demonstrate the effectiveness of the proposed model,
outperforming current state of the arts by a large margin in terms of both
adapting on new subject and retain knowledge of learned subjects.
