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 …
EEG-based brain-computer interfaces (BCIs): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their …
Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact
with the environment. Recent advancements in technology and machine learning algorithms …
with the environment. Recent advancements in technology and machine learning algorithms …
An attention-based deep learning approach for sleep stage classification with single-channel EEG
Automatic sleep stage mymargin classification is of great importance to measure sleep
quality. In this paper, we propose a novel attention-based deep learning architecture called …
quality. In this paper, we propose a novel attention-based deep learning architecture called …
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 …
SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging
Automatic sleep staging has been often treated as a simple classification problem that aims
at determining the label of individual target polysomnography epochs one at a time. In this …
at determining the label of individual target polysomnography epochs one at a time. In this …
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 …
Contrastive representation learning for electroencephalogram classification
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 …
A deep learning approach for intrusion detection in Internet of Things using focal loss function
Abstract Internet of Things (IoT) is likely to revolutionize healthcare, energy, education,
transportation, manufacturing, military, agriculture, and other industries. However, for the …
transportation, manufacturing, military, agriculture, and other industries. However, for the …
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 …
Analysis of the relationship among land surface temperature (LST), land use land cover (LULC), and normalized difference vegetation index (NDVI) with topographic …
Abstract Land Surface Temperature (LST) affects exchange of energy between earth surface
and atmosphere which is important for studying environmental changes. However, research …
and atmosphere which is important for studying environmental changes. However, research …