Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Generative adversarial networks in EEG analysis: an overview
Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as
engineering applications. However, one of the challenges associated with recording EEG …
engineering applications. However, one of the challenges associated with recording EEG …
Automatic sleep staging of EEG signals: recent development, challenges, and future directions
Modern deep learning holds a great potential to transform clinical studies of human sleep.
Teaching a machine to carry out routine tasks would be a tremendous reduction in workload …
Teaching a machine to carry out routine tasks would be a tremendous reduction in workload …
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 …
U-Sleep: resilient high-frequency sleep staging
Sleep disorders affect a large portion of the global population and are strong predictors of
morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence …
morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence …
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 …
BENDR: Using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data
Deep neural networks (DNNs) used for brain–computer interface (BCI) classification are
commonly expected to learn general features when trained across a variety of contexts, such …
commonly expected to learn general features when trained across a variety of contexts, such …
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 …
[HTML][HTML] Deep learning in physiological signal data: A survey
Deep Learning (DL), a successful promising approach for discriminative and generative
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
Exploring convolutional neural network architectures for EEG feature extraction
The main purpose of this paper is to provide information on how to create a convolutional
neural network (CNN) for extracting features from EEG signals. Our task was to understand …
neural network (CNN) for extracting features from EEG signals. Our task was to understand …
Automatic sleep stage classification using temporal convolutional neural network and new data augmentation technique from raw single-channel EEG
Background and objective: This paper presents a new framework for automatic classification
of sleep stages using a deep learning algorithm from single-channel EEG signals. Each …
of sleep stages using a deep learning algorithm from single-channel EEG signals. Each …