  The electroencephalogram (EEG) is the most widely used input for brain
computer interfaces (BCIs), and common spatial pattern (CSP) is frequently used
to spatially filter it to increase its signal-to-noise ratio. However, CSP is a
supervised filter, which needs some subject-specific calibration data to
design. This is time-consuming and not user-friendly. A promising approach for
shortening or even completely eliminating this calibration session is transfer
learning, which leverages relevant data or knowledge from other subjects or
tasks. This paper reviews three existing approaches for incorporating transfer
learning into CSP, and also proposes a new transfer learning enhanced CSP
approach. Experiments on motor imagery classification demonstrate their
effectiveness. Particularly, our proposed approach achieves the best
performance when the number of target domain calibration samples is small.
