CNN-based Subject-Transfer Approach for Training Minimized Lower-Limb MI-BCIs

Ji Hyeok Jeong, Keun Tae Kim, Song Joo Lee, Dong Joo Kim, Hyungmin Kim

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    The subject-transfer approach has recently been proposed to overcome the limitation of requiring a long training time in the motor imagery (MI)-based brain-computer interfaces (BCIs). However, the applicability for reducing the training time for lower-limb MI-BCI has not been investigated yet. In this study, we proposed a dual-domain convolutional neural network (CNN)-based subject-transfer method. We investigated how the classification accuracy changes according to the reduced number of training trials. Two lower-limb MIs (gait and sit-down) and rest electroencephalography (EEG) data were collected from five healthy subjects. The CNN model was pre-trained using other subjects' data and fine-tuned with the target subject's training data. There was a significant increase in classification accuracy (7% with 15 and 10 trials) compared to the self-training approach using the same CNN model trained only with the target subject's training data. Based on these results, the subject-transfer approach can contribute to minimizing the training time of lower-limb MI-BCIs while preserving the classification accuracy.

    Original languageEnglish
    Title of host publication10th International Winter Conference on Brain-Computer Interface, BCI 2022
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781665413374
    DOIs
    Publication statusPublished - 2022
    Event10th International Winter Conference on Brain-Computer Interface, BCI 2022 - Gangwon-do, Korea, Republic of
    Duration: 2022 Feb 212022 Feb 23

    Publication series

    NameInternational Winter Conference on Brain-Computer Interface, BCI
    Volume2022-February
    ISSN (Print)2572-7672

    Conference

    Conference10th International Winter Conference on Brain-Computer Interface, BCI 2022
    Country/TerritoryKorea, Republic of
    CityGangwon-do
    Period22/2/2122/2/23

    Bibliographical note

    Publisher Copyright:
    © 2022 IEEE.

    Keywords

    • Brain-Computer interface
    • Convolutional Neural Network
    • Lower-Limb Motor Imagery
    • Subject-Transfer

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Human-Computer Interaction
    • Signal Processing

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