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

A brain-computer interface (BCI) enables a user to communicate directly with a computer using the brain signals. Electroencephalogram (EEG) is the most frequently used input signal in BCIs. However, EEG signals are weak, easily contaminated by interferences and noise, non-stationary for the same subject, and varying among different subjects. So, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, in different sessions, for different devices and tasks. Usually a calibration session is needed to collect some subject-specific data for a new subject, which is time-consuming and user-unfriendly. Transfer learning (TL), which can utilize data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate the learning for a new subject/session/device/task, is frequently used to alleviate this calibration requirement. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications -- motor imagery (MI), event related potentials (ERP), steady-state visual evoked potentials (SSVEP), affective BCIs (aBCI), regression problems, and adversarial attacks -- are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions.