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Brain Relevance Feedback for Interactive Image Generation

Published: 20 October 2020 Publication History

Abstract

Brain-computer interfaces (BCIs) are increasingly used to perform simple operations such as a moving a cursor, but have remained of limited use for more complex tasks. In our new approach to BCI, we use brain relevance feedback to control a generative adversarial network (GAN). We obtained EEG data from 31 participants who viewed face images while concentrating on particular facial features. Following, an EEG relevance classifier was trained and propagated as feedback on the latent image representation provided by the GAN. Estimates for individual vectors matching the relevant criteria were iteratively updated to optimize an image generation process towards mental targets. A double-blind evaluation showed high performance (86.26% accuracy) against random feedback (18.71%), and not significantly lower than explicit feedback (93.30%). Furthermore, we show the feasibility of the method with simultaneous task targets demonstrating BCI operation beyond individual task constraints. Thus, brain relevance feedback can validly control a generative model, overcoming a critical limitation of current BCI approaches.

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    cover image ACM Conferences
    UIST '20: Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology
    October 2020
    1297 pages
    ISBN:9781450375146
    DOI:10.1145/3379337
    Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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    Published: 20 October 2020

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    Author Tags

    1. brain-computer interfaces
    2. generative models
    3. image search

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    • (2024)SpaceEditing: A Latent Space Editing Interface for Integrating Human Knowledge into Deep Neural NetworksProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645211(489-503)Online publication date: 18-Mar-2024
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