Year: 2024
Type: article
Abstract: 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.
Source: Frontiers in Neurorobotics
Authors: Rodrigo Vieira, Plinio Moreno, Athanasios Vourvopoulos
Institution: Instituto Superior Técnico
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,295
Awards: LARSyS - Laboratório de Robótica e Sistemas de Engenharia, Laboratório de Robótica e Sistemas de Engenharia, Sistemas Neuroadaptativos para Restauração do Cérebro baseados em Realidade Virtual e Interfaces Cérebro-Computador, G4680999436, G4693478082, G4964156841, G5808023565, G6997658380, G7354346225, G787757375, G8815410947
OpenAlex ID: W4404980254