Transfer Learning for EEG Based Brain Computer Interfaces: A Review of Progresses Since 2016
A brain computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non invasive BCI modality, electroencephalogram (EEG) is sensitive to noise/artifact and suffers between subject/within subject non stationarity. 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.
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