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

By Dongrui Wu, Yifan Xu, Baoliang Lu. IEEE Transactions on Cognitive and Developmental Systems, 2020

399 citations recorded by the source corpus.

A brain–computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common noninvasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject nonstationarity. 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 article 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 article, which may point to future research directions.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f6995f54699cfdd19fb182020dd20847edfce13f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Dongrui Wu
2. Ganesh R. Naik

In Cognitive Neuroscience

1. Lingfei Deng
2. Wen Zhang
3. Lei Zhang
4. Dongrui Wu
5. Ganesh R. Naik
6. Mohammad Rostami
7. Aram Galstyan
8. Serban Stan

In Electrical and Electronic Engineering

1. Lingfei Deng
2. Dongrui Wu
3. Wen Zhang
4. Lei Zhang

Citations in the corpus

• Progress in Brain Computer Interface: Challenges and Opportunities (2019, methodology): “Transfer learning can also reduce the effects of session-to-session and subject-to-subject variabilities, by using systems that were trained on data from different people exploiting commonalties and reducing training requirements (Jayaram et al., 2016; Saha et al., 2017, 2018, 2019a; He and Wu, 2019”
• Overcoming Negative Transfer: A Survey (2020, background): “For example, two image datasets may be taken using cameras with different resolutions under different light conditions; different people may demonstrate strong individual differences in braincomputer interfaces [1].”
• A Survey on Negative Transfer (2020, background): “For example, two image datasets may contain images taken using cameras with different resolutions under different light conditions; different people may demonstrate strong individual differences in brain-computer interfaces [1].”