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

By He He, Dongrui Wu. IEEE Transactions on Biomedical Engineering, 2018

459 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/2509582652372582c2d2cf37cfef7ef3f77e6cb7

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Citations in the corpus

Artificial Intelligence for the Metaverse: A Survey (2022, methodology): “With electroencephalogram (EEG) signal as one of the most popular inputs of BCI systems, the work in [128] studied a”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, methodology): “lity of reducing the number of training templates for an SSVEP BCI. Inter- and intra-subject transfer learning is also applied to unsupervised conditions when no labelled data is available. He and Wu [67] presented a method to align EEG trails directly in the Euclidean space across different subject”
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, unknown): “For example, EA and LA data alignment schemes have been combined with TL classifiers [50], [60], and CSDF-ReliefF and CSDF-mRMR feature selection approaches have also been integrated with TL classifiers [114].”