{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T05:27:35Z","timestamp":1775194055233,"version":"3.50.1"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3389\/fnbot.2024.1491721","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T06:31:43Z","timestamp":1733207503000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["EEG-based action anticipation in human-robot interaction: a comparative pilot study"],"prefix":"10.3389","volume":"18","author":[{"given":"Rodrigo","family":"Vieira","sequence":"first","affiliation":[]},{"given":"Plinio","family":"Moreno","sequence":"additional","affiliation":[]},{"given":"Athanasios","family":"Vourvopoulos","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1109\/TNSRE.2012.2227278","article-title":"Constrained blind source extraction of readiness potentials from EEG","volume":"21","author":"Ahmadian","year":"2013","journal-title":"IEEE Trans. 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