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Self-supervised Contrastive Few-Shot Learning for Motor Imagery Brain-Computer Interfaces

Published: 28 February 2024 Publication History

Abstract

Motor imagery brain-computer interfaces (BCIs) are integral to the field of intelligent rehabilitation. However, the collection of motor imagery data is time-consuming and expensive, while traditional deep learning models often face the risk of overfitting when handling limited data samples. In this paper, we propose a motor imagery recognition network that merges a self-supervised contrastive framework with few-shot learning. The proposed model is first trained on the dataset without labels using self-supervised contrastive to obtain the optimal model. The model parameters obtained are then used as initialization parameters for the prototypical network, which improves in few-shot learning. The results show that on the BCICIV2A and BCICIV2B datasets, the proposed approach achieves accuracies of 69.12% and 80.27%, respectively, representing improvements of 6.79% and 4.14% compared to the baseline models. This research achievement holds significant implications for advancing the application of motor imagery BCIs in the domain of intelligent rehabilitation.

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  • (2024)EEG-Oriented Self-Supervised Learning With Triple Information Pathways NetworkIEEE Transactions on Cybernetics10.1109/TCYB.2024.341084454:11(6495-6508)Online publication date: Nov-2024

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    ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
    October 2023
    589 pages
    ISBN:9798400707988
    DOI:10.1145/3633637
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 28 February 2024

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    Author Tags

    1. Few-Shot Learning
    2. Motor Imagery
    3. Prototypical Network
    4. Self-supervised Contrastive Learning
    5. Subject-Independent

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    • (2024)EEG-Oriented Self-Supervised Learning With Triple Information Pathways NetworkIEEE Transactions on Cybernetics10.1109/TCYB.2024.341084454:11(6495-6508)Online publication date: Nov-2024

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