Semi-supervised generative and discriminative adversarial learning for motor imagery-based brain–computer interface

Wonjun Ko, Eunjin Jeon, Jee Seok Yoon, Heung Il Suk

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)

    Abstract

    Convolutional neural networks (CNNs), which can recognize structural/configuration patterns in data with different architectures, have been studied for feature extraction. However, challenges remain regarding leveraging advanced deep learning methods in BCIs. We focus on problems of small-sized training samples and interpretability of the learned parameters and leverages a semi-supervised generative and discriminative learning framework that effectively utilizes synthesized samples with real samples to discover class-discriminative features. Our framework learns the distributional characteristics of EEG signals in an embedding space using a generative model. By using artificially generated and real EEG signals, our framework finds class-discriminative spatio-temporal feature representations that help to correctly discriminate input EEG signals. It is noteworthy that the framework facilitates the exploitation of real, unlabeled samples to better uncover the underlying patterns inherent in a user’s EEG signals. To validate our framework, we conducted experiments comparing our method with conventional linear models by utilizing variants of three existing CNN architectures as generator networks and measuring the performance on three public datasets. Our framework exhibited statistically significant improvements over the competing methods. We investigated the learned network via activation pattern maps and visualized generated artificial samples to empirically justify the stability and neurophysiological plausibility of our model.

    Original languageEnglish
    Article number4587
    JournalScientific reports
    Volume12
    Issue number1
    DOIs
    Publication statusPublished - 2022 Dec

    Bibliographical note

    Funding Information:
    This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government under Grant 2017-0-00451 (Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning) and Grant 2019-0-00079 (Department of Artificial Intelligence, Korea University).

    Publisher Copyright:
    © 2022, The Author(s).

    ASJC Scopus subject areas

    • General

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