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

• URL: http://arxiv.org/abs/2004.06286v4
• Date: Fri, 3 Jul 2020 23:34:11 GMT
• Title: Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016
• Authors: Dongrui Wu and Yifan Xu and Bao-Liang Lu
• Abstract summary: A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. EEG is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. It is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects.
• Score: 35.68916211292525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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