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

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? Met...

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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2019.2913914