Deep learning for electroencephalogram (EEG) classification tasks: a review
Objective. Electroencephalography (EEG) analysis has been an important tool in
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
Deep learning-based electroencephalography analysis: a systematic review
Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …
of training, as well as advanced signal processing and feature extraction methodologies to …
A survey on explainable artificial intelligence (xai): Toward medical xai
Recently, artificial intelligence and machine learning in general have demonstrated
remarkable performances in many tasks, from image processing to natural language …
remarkable performances in many tasks, from image processing to natural language …
Sleeptransformer: Automatic sleep staging with interpretability and uncertainty quantification
Background: Black-box skepticism is one of the main hindrances impeding deep-learning-
based automatic sleep scoring from being used in clinical environments. Methods: Towards …
based automatic sleep scoring from being used in clinical environments. Methods: Towards …
LUCID: A practical, lightweight deep learning solution for DDoS attack detection
R Doriguzzi-Corin, S Millar… - … on Network and …, 2020 - ieeexplore.ieee.org
Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's
Internet, disrupting the availability of essential services. The challenge of DDoS detection is …
Internet, disrupting the availability of essential services. The challenge of DDoS detection is …
XSleepNet: Multi-view sequential model for automatic sleep staging
Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve
millions experiencing sleep deprivation and disorders and enable longitudinal sleep …
millions experiencing sleep deprivation and disorders and enable longitudinal sleep …
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
S Chambon, MN Galtier, PJ Arnal… - … on Neural Systems …, 2018 - ieeexplore.ieee.org
Sleep stage classification constitutes an important preliminary exam in the diagnosis of
sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of …
sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of …
Contrastive representation learning for electroencephalogram classification
MN Mohsenvand, MR Izadi… - Machine Learning for …, 2020 - proceedings.mlr.press
Interpreting and labeling human electroencephalogram (EEG) is a challenging task
requiring years of medical training. We present a framework for learning representations …
requiring years of medical training. We present a framework for learning representations …
U-time: A fully convolutional network for time series segmentation applied to sleep staging
Neural networks are becoming more and more popular for the analysis of physiological time-
series. The most successful deep learning systems in this domain combine convolutional …
series. The most successful deep learning systems in this domain combine convolutional …
Automated sleep scoring: A review of the latest approaches
Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a
human expert, according to official standards. It could appear then a suitable task for modern …
human expert, according to official standards. It could appear then a suitable task for modern …