Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review

Abstract

The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the same probability distribution. However, this may be violated in EEG processing. Variations across sessions/subjects result in a deviation of the feature distribution of EEG signals in the same task, which reduces the accuracy of the decoding model for mental tasks. Recently, transfer learning (TL) has shown great potential in processing EEG signals across sessions/subjects. In this work, we reviewed 80 related published studies from 2010 to 2020 about TL application for EEG decoding. Herein, we report what kind of TL methods have been used (e.g., instance knowledge, feature representation knowledge, and model parameter knowledge), describe which types of EEG paradigms have been analyzed, and summarize the datasets that have been used to evaluate performance. Moreover, we discuss the state-of-the-art and future development of TL for EEG decoding. The results show that TL can significantly improve the performance of decoding models across subjects/sessions and can reduce the calibration time of brain–computer interface (BCI) systems. This review summarizes the current practical suggestions and performance outcomes in the hope that it will provide guidance and help for EEG research in the future.

An Inter- and Intra-Subject Transfer Calibration Scheme for Improving Feedback Performance of Sensorimotor Rhythm-Based BCI Rehabilitation

“…As we know, the critical issue of BCI rehabilitation revolves around how to promote the biofeedback effect for active intervention (Ko et al, 2019 ). It was positive for rehabilitation outcomes, enhancing cortical activity for neural recovery, and increasing confidences of voluntary training (Zhang et al, 2020 ). Hence, the biofeedback of low-precision trials was negative for patients.…”

A Fuzzy Shell for Developing an Interpretable BCI Based on the Spatiotemporal Dynamics of the Evoked Oscillations

“…Different methods have been surveyed in [ 10 ]; however, the training is either features or application specific. Recently, the Transfer Learning (TL) in EEG decoding showed great potential in processing signals across sessions and subjects, as can be seen from [ 10 , 29 ]. The principle of TL is to transfer knowledge from different but related tasks using existing knowledge learned from already accomplished tasks to help with new tasks.…”

“…In order to train the feature extraction or classification model, the large-scale and high-quality datasets are used to obtain strong robustness and high classification accuracy for the new tasks. A lot of case-studies are surveyed in [ 29 ] showing how TL improves the cross-subject transfer and the practicality of real-world BCI applications for different features and tasks.…”

“…Different methods have been surveyed in [10]; however, the training is either features or application specific. Recently, the Transfer Learning (TL) in EEG decoding showed great potential in processing signals across sessions and subjects, as can be seen from [10,29].…”

“…In order to train the feature extraction or classification model, the large-scale and high-quality datasets are used to obtain strong robustness and high classification accuracy for the new tasks. A lot of case-studies are surveyed in [29] showing how TL improves the cross-subject transfer and the practicality of real-world BCI applications for different features and tasks. [30][31][32], support vector machine (SVM) [32][33][34][35], naive Bayes (NB) [32], linear discriminant analysis (LDA) [36,37], convolutional neural networks (CNN) [38], deep belief network (DBN) [39], AdaBoost ensemble learning [40], lattice computing [31], and fuzzy logic-based classifiers [32,41,[43][44][45][46].…”