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http://dx.doi.org/10.3389/fnhum.2020.00144 | DOI Listing |
Front Bioeng Biotechnol
January 2025
University of Connecticut Health Center, University of Connecticut, Storrs, CT, United States.
Front Hum Neurosci
December 2024
Department of Biomedical Engineering, Izmir Katip Celebi University, Izmir, Türkiye.
Introduction: Motor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms.
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December 2024
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China.
Front Neurorobot
December 2024
LaSEEB, Department of Bioengineering, Institute for Systems and Robotics (ISR-Lisboa), Instituto Superior Técnico, Lisbon, Portugal.
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.
View Article and Find Full Text PDFFront Hum Neurosci
December 2024
UMR7020 Laboratoire d'Informatique et Systèmes (LIS), Marseille, France.
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