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| http://dx.doi.org/10.3389/fnbot.2022.952495 | DOI Listing |
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EEG-based action anticipation in human-robot interaction: a comparative pilot study.
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 PDFNeural speech tracking and auditory attention decoding in everyday life.
Front Hum Neurosci
November 2024
Department of Psychology, Neuropsychology Lab, University of Oldenburg, Oldenburg, Germany.
Introduction: In our complex world, the auditory system plays a crucial role in perceiving and processing our environment. Humans are able to segment and stream concurrent auditory objects, allowing them to focus on specific sounds, such as speech, and suppress irrelevant auditory objects. The attentional enhancement or suppression of sound processing is evident in neural data through a phenomenon called neural speech tracking.
View Article and Find Full Text PDFHeterogeneity and convergence across seven neuroimaging modalities: a review of the autism spectrum disorder literature.
Front Psychiatry
October 2024
Section on Social and Cognitive Developmental Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States.
Background: A growing body of literature classifies autism spectrum disorder (ASD) as a heterogeneous, complex neurodevelopmental disorder that often is identified prior to three years of age. We aim to provide a narrative review of key structural and functional properties that differentiate the neuroimaging profile of autistic youth from their typically developing (TD) peers across different neuroimaging modalities.
Methods: Relevant studies were identified by searching for key terms in PubMed, with the most recent search conducted on September 1, 2023.
Advancing passive BCIs: a feasibility study of two temporal derivative features and effect size-based feature selection in continuous online EEG-based machine error detection.
Front Neuroergon
May 2024
Chair of Neuroadaptive Human-Computer Interaction, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany.
The emerging integration of Brain-Computer Interfaces (BCIs) in human-robot collaboration holds promise for dynamic adaptive interaction. The use of electroencephalogram (EEG)-measured error-related potentials (ErrPs) for online error detection in assistive devices offers a practical method for improving the reliability of such devices. However, continuous online error detection faces challenges such as developing efficient and lightweight classification techniques for quick predictions, reducing false alarms from artifacts, and dealing with the non-stationarity of EEG signals.
View Article and Find Full Text PDFReliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.
Epilepsia
July 2024
Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA.
Objective: The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy.
Methods: This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors.
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