Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach

• Published in: IEEE transactions on biomedical engineering Vol. 67; no. 2; pp. 399 - 410
• Main Authors: He, He, Wu, Dongrui
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Alignment; Brain; Brain-computer interface; Brain-Computer Interfaces; Classification; Computational neuroscience; Computer Simulation; Covariance matrices; data alignment; Data processing; Databases, Factual; EEG; Electroencephalography; Electroencephalography - classification; Euclidean geometry; Euclidean space; Event-related potentials; Evoked Potentials - physiology; Feature extraction; Humans; Image classification; Imagination - physiology; Interfaces; Learning algorithms; Machine Learning; Machine learning algorithms; Mental task performance; Microsoft Windows; Riemannian geometry; Signal processing; Signal Processing, Computer-Assisted; Task analysis; Transfer learning
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2019.2913914

Objective: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Methods: We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject. Results: Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. Conclusion: The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Significance: Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs.