EEG-based action anticipation in human-robot interaction: a comparative pilot study

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...

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Bibliographic Details
Published in:Frontiers in neurorobotics 2024-12, Vol.18, p.1491721
Main Authors: Vieira, Rodrigo, Moreno, Plinio, Vourvopoulos, Athanasios
Format: Article
Language:English
Subjects:
action anticipation
brain-computer interfaces
Convolutional Neural Networks
EEG
human-robot interaction
Neuroscience
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Summary: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. We evaluated multiple Deep Learning classification models on a motor imagery (MI) dataset, achieving up to 80.90% accuracy. These results were further validated in a pilot experiment, where actions were accurately predicted several hundred milliseconds before execution. This research demonstrates the potential of combining EEG with deep learning to enhance real-time collaborative tasks, paving the way for safer and more efficient human-robot interactions.
ISSN:1662-5218
1662-5218
DOI:10.3389/fnbot.2024.1491721