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A brain-computer interface (BCI) enables a user to communicate with acomputer directly using brain signals. The most common non-invasive BCImodality, electroencephalogram (EEG), is sensitive to noise/artifact andsuffers between-subject/within-subject non-stationarity. Therefore, it isdifficult to build a generic pattern recognition model in an EEG-based BCIsystem that is optimal for different subjects, during different sessions, fordifferent devices and tasks. Usually, a calibration session is needed tocollect some training data for a new subject, which is time-consuming and userunfriendly. Transfer learning (TL), which utilizes data or knowledge fromsimilar or relevant subjects/sessions/devices/tasks to facilitate learning fora new subject/session/device/task, is frequently used to reduce the amount ofcalibration effort. This paper reviews journal publications on TL approaches inEEG-based BCIs in the last few years, i.e., since 2016. Six paradigms andapplications -- motor imagery, event-related potentials, steady-state visualevoked potentials, affective BCIs, regression problems, and adversarial attacks-- are considered. For each paradigm/application, we group the TL approachesinto cross-subject/session, cross-device, and cross-task settings and reviewthem separately. Observations and conclusions are made at the end of the paper,which may point to future research directions.

Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016