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.
A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. Electroencephalograms (EEGs) used in BCIs are weak, easily contaminated by interference and noise, non-stationary for the same subject, and varying across different subjects and sessions. Therefore , it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually , a calibration session is needed to collect some training data for a new subject, which is time consuming and user unfriendly . Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort . 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 , event-related potentials, steady-state visual evoked potentials , affective BCIs , 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.
D
directly
R
using the
directly using
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Electroencephalogram (EEG) is the most frequently used input signal in BCIs . However, EEG signals
Electroencephalograms (EEGs) used in BCIs
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interferences
interference
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among different subjects . So
across different subjects and sessions. Therefore
R
in
during
A
,
R
subject-specific
training
R
time-consuming and user-unfriendly
time consuming and user unfriendly
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can utilize
utilizes
D
the
R
alleviate this calibration requirement
reduce the amount of calibration effort
D
(MI), event related potentials(ERP)
A
event-related potentials,
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(SSVEP)
D
(aBCI)