AIMC Topic: Electroencephalography
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Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation.
Neural networks : the official journal of the International Neural Network Society
Aug 8, 2024
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
Aug 8, 2024
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
Aug 8, 2024
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
Aug 8, 2024
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
Aug 8, 2024
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
Aug 7, 2024
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
Aug 6, 2024
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...
Automatically Extracting and Utilizing EEG Channel Importance Based on Graph Convolutional Network for Emotion Recognition.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Graph convolutional network (GCN) based on the brain network has been widely used for EEG emotion recognition. However, most studies train their models directly without considering network dimensionality reduction beforehand. In fact, some nodes and ...
Riemannian Locality Preserving Method for Transfer Learning With Applications on Brain-Computer Interface.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Brain-computer interfaces (BCIs) have been widely focused and extensively studied in recent years for their huge prospect of medical rehabilitation and commercial applications. Transfer learning exploits the information in the source domain and appli...
DSFE: Decoding EEG-Based Finger Motor Imagery Using Feature-Dependent Frequency, Feature Fusion and Ensemble Learning.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Accurate decoding finger motor imagery is essential for fine motor control using EEG signals. However, decoding finger motor imagery is particularly challenging compared with ordinary motor imagery. This paper proposed a novel EEG decoding method of ...
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