AIMC Topic: Electroencephalography

Clear Filters Showing 551 to 560 of 2295 articles

Cochlear Implant Artifacts Removal in EEG-Based Objective Auditory Rehabilitation Assessment.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Cochlear implant (CI) is a neural prosthesis that can restore hearing for patients with severe to profound hearing loss. Observed variability in auditory rehabilitation outcomes following cochlear implantation may be due to cerebral reorganization. E...

Detection of Pilots' Psychological Workload during Turning Phases Using EEG Characteristics.

Sensors (Basel, Switzerland)
Pilot behavior is crucial for aviation safety. This study aims to investigate the EEG characteristics of pilots, refine training assessment methodologies, and bolster flight safety measures. The collected EEG signals underwent initial preprocessing. ...

MFCC-CNN: A patient-independent seizure prediction model.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
BACKGROUND: Automatic prediction of seizures is a major goal in the field of epilepsy. However, the high variability of Electroencephalogram (EEG) signals in different patients limits the use of prediction models in clinical applications.

Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation.

Neural networks : the official journal of the International Neural Network Society
Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) systems. Most vigilance estimation methods rely on a large amount of labeled data to train a satisfactory model for the specific subject, which limits...

Proprioception enhancement for robot assisted neural rehabilitation: a dynamic electrical stimulation based method and preliminary results from EEG analysis.

Journal of neural engineering
In recent years, the robot assisted (RA) rehabilitation training has been widely used to counteract defects of the manual one provided by physiotherapists. However, since the proprioception feedback provided by the robotic assistance or the manual me...

Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space.

Journal of neural engineering
This review paper provides an integrated perspective of Explainable Artificial Intelligence (XAI) techniques applied to Brain-Computer Interfaces (BCIs). BCIs use predictive models to interpret brain signals for various high-stake applications. Howev...

An efficient channel recurrent Criss-cross attention network for epileptic seizure prediction.

Medical engineering & physics
Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurately predicting the onset of epilepsy can effectively improve the quality of life for patients with the condition. While there are many methods for det...

Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention.

PLoS computational biology
Human listeners have the ability to direct their attention to a single speaker in a multi-talker environment. The neural correlates of selective attention can be decoded from a single trial of electroencephalography (EEG) data. In this study, leverag...

Automated remote sleep monitoring needs uncertainty quantification.

Journal of sleep research
Wearable electroencephalography devices emerge as a cost-effective and ergonomic alternative to gold-standard polysomnography, paving the way for better health monitoring and sleep disorder screening. Machine learning allows to automate sleep stage c...

Free access via computational cloud to deep learning-based EEG assessment in neonatal hypoxic-ischemic encephalopathy: revolutionary opportunities to overcome health disparities.

Pediatric research
In this issue of Pediatric Research, Kota et al. evaluate a novel monitoring visual trend using deep-learning - Brain State of the Newborn (BSN)- based EEG as a bedside marker for severity of the encephalopathy in 46 neonates with hypoxic-ischemic en...