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ORIGINAL RESEARCH article

Front. Neurorobot.
Volume 18 - 2024 | doi: 10.3389/fnbot.2024.1491721
This article is part of the Research Topic NeuroDesign in Human-Robot Interaction: The making of engaging HRI technology your brain can’t resist View all 5 articles

EEG-Based Action Anticipation in Human-Robot Interaction: A Comparative Pilot Study

Provisionally accepted
Rodrigo Vieira Rodrigo Vieira Plinio Moreno Plinio Moreno Athanasios Vourvopoulos Athanasios Vourvopoulos *
  • Laboratory for Robotics and Engineering Systems (LARSyS), Instituto Superior Técnico (ISR), Lisboa, Portugal

The final, formatted version of the article will be published soon.

    As robots become integral to various sectors, improving human-robot collaboration is crucial, particularly in anticipating human actions to enhance safety and efficiency. Electroencephalographic (EEG) signals offer a promising solution, as they can detect brain activity preceding movement by over a second, enabling predictive capabilities in robots. This study explores how EEG can be used for action anticipation in human-robot interaction (HRI), leveraging its high temporal resolution and modern deep learning techniques. Specifically, convolutional neural networks (CNNs) are employed for end-to-end classification of EEG data to predict human movement intentions with minimal latency. We evaluated multiple CNN-based

    Keywords: Brain-Computer Interfaces, human-robot interaction, action anticipation, Convolational neural networks, EEG

    Received: 05 Sep 2024; Accepted: 12 Nov 2024.

    Copyright: © 2024 Vieira, Moreno and Vourvopoulos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Athanasios Vourvopoulos, Laboratory for Robotics and Engineering Systems (LARSyS), Instituto Superior Técnico (ISR), Lisboa, Portugal

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.