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 …
A review of automated sleep stage based on EEG signals
X Zhang, X Zhang, Q Huang, Y Lv, F Chen - Biocybernetics and Biomedical …, 2024 - Elsevier
Sleep disorders have increasingly impacted healthy lifestyles. Accurate scoring of sleep
stages is crucial for diagnosing patients with sleep disorders. The precision of sleep staging …
stages is crucial for diagnosing patients with sleep disorders. The precision of sleep staging …
MAtt: A manifold attention network for EEG decoding
YT Pan, JL Chou, CS Wei - Advances in Neural Information …, 2022 - proceedings.neurips.cc
Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-
invasive brain-computer interfaces (BCIs). While recent advances of deep-learning (DL) …
invasive brain-computer interfaces (BCIs). While recent advances of deep-learning (DL) …
A transformer-based deep neural network model for SSVEP classification
Steady-state visual evoked potential (SSVEP) is one of the most commonly used control
signals in the brain–computer interface (BCI) systems. However, the conventional spatial …
signals in the brain–computer interface (BCI) systems. However, the conventional spatial …
[HTML][HTML] An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea
Automatic deep-learning models used for sleep scoring in children with obstructive sleep
apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings …
apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings …
Towards interpretable sleep stage classification using cross-modal transformers
Accurate sleep stage classification is significant for sleep health assessment. In recent
years, several machine-learning based sleep staging algorithms have been developed, and …
years, several machine-learning based sleep staging algorithms have been developed, and …
L-SeqSleepNet: Whole-cycle long sequence modelling for automatic sleep staging
Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal
dependency in the sleep data. Yet, exploring this long-term dependency when develo** …
dependency in the sleep data. Yet, exploring this long-term dependency when develo** …
SleepPPG-Net: A deep learning algorithm for robust sleep staging from continuous photoplethysmography
Sleep staging is an essential component in the diagnosis of sleep disorders and
management of sleep health. Sleep is traditionally measured in a clinical setting and …
management of sleep health. Sleep is traditionally measured in a clinical setting and …
Masksleepnet: A cross-modality adaptation neural network for heterogeneous signals processing in sleep staging
Deep learning methods have become an important tool for automatic sleep staging in recent
years. However, most of the existing deep learning-based approaches are sharply …
years. However, most of the existing deep learning-based approaches are sharply …
Dynamic alignment and fusion of multimodal physiological patterns for stress recognition
X Zhang, X Wei, Z Zhou, Q Zhao… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Stress has been identified as one of major causes of health issues. To detect the stress
levels with higher accuracy, fusion of multimodal physiological signals is a promising …
levels with higher accuracy, fusion of multimodal physiological signals is a promising …