Papers EEG based sleep staging
“EEG based sleep staging” 태그가 달린 논문 10편 · 필터 해제
SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEG
The non-stationary nature of electroencephalography (EEG) introduces distribution shifts across domains (e.g., days and subjects), posing a significant challenge to EEG-based neurotechnology generalization. Without label…
Brain Computer InterfaceDomain AdaptationEEGEEG based sleep staging+2Structure-Preserving Transformers for Sequences of SPD Matrices
In recent years, Transformer-based auto-attention mechanisms have been successfully applied to the analysis of a variety of context-reliant data types, from texts to images and beyond, including data from non-Euclidean g…
EEGEEG based sleep stagingSleep Stage DetectionSleep StagingEEG-based Sleep Staging with Hybrid Attention
Sleep staging is critical for assessing sleep quality and diagnosing sleep disorders. However, capturing both the spatial and temporal relationships within electroencephalogram (EEG) signals during different sleep stages…
EEGEEG based sleep stagingElectroencephalogram (EEG)Sleep Quality+1EEG aided boosting of single-lead ECG based sleep staging with Deep Knowledge Distillation
An electroencephalogram (EEG) signal is currently accepted as a standard for automatic sleep staging. Lately, Near-human accuracy in automated sleep staging has been achievable by Deep Learning (DL) based approaches, ena…
ECG based Sleep StagingEEGEEG based sleep stagingElectroencephalogram (EEG)+2A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data
Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works ha…
Domain AdaptationEEGEEG based sleep stagingElectroencephalogram (EEG)+3A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging
Automatic Sleep Staging study is presently done with the help of Electroencephalogram (EEG) signals. Recently, Deep Learning (DL) based approaches have enabled significant progress in this area, allowing for near-human a…
ECG based Sleep StagingEEGEEG based sleep stagingElectroencephalogram (EEG)+5Self-supervised Contrastive Learning for EEG-based Sleep Staging
EEG signals are usually simple to obtain but expensive to label. Although supervised learning has been widely used in the field of EEG signal analysis, its generalization performance is limited by the amount of annotated…
Contrastive LearningEEGEEG based sleep stagingElectroencephalogram (EEG)+2ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training
Sleep staging is of great importance in the diagnosis and treatment of sleep disorders. Recently, numerous data-driven deep learning models have been proposed for automatic sleep staging. They mainly train the model on a…
Automatic Sleep Stage ClassificationDomain AdaptationEEGEEG based sleep staging+2MRNet: a Multi-scale Residual Network for EEG-based Sleep Staging
Sleep staging based on electroencephalogram (EEG) plays an important role in the clinical diagnosis and treatment of sleep disorders. In order to emancipate human experts from heavy labeling work, deep neural networks ha…
EEGEEG based sleep stagingElectroencephalogram (EEG)Sleep StagingUncovering the structure of clinical EEG signals with self-supervised learning
Objective. Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG), w…
EEGEEG based sleep stagingElectroencephalogram (EEG)Self-Supervised Learning+1