A review on transfer learning for brain-computer interface classification

Due to the non-stationarity nature and poor signal-to-noise ratio (SNR) of brain signals, repeated time-consuming calibration is one of the biggest problems for today's brain-computer interfaces (BCIs). In order to reduce calibration time, many transfer learning methods have been proposed to extract discriminative or stationary information from other subjects or prior sessions for target classification task. In this paper, we review the existing transfer learning methods used for BCI classification problems and organize them into three cases based on different transfer strategies. Besides, we list the datasets used in these BCI studies.