A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
State-of-the-art on brain-computer interface technology
J Peksa, D Mamchur - Sensors, 2023 - mdpi.com
This paper provides a comprehensive overview of the state-of-the-art in brain–computer
interfaces (BCI). It begins by providing an introduction to BCIs, describing their main …
interfaces (BCI). It begins by providing an introduction to BCIs, describing their main …
EEG-based emotion recognition using spatial-temporal graph convolutional LSTM with attention mechanism
L Feng, C Cheng, M Zhao, H Deng… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
The dynamic uncertain relationship among each brain region is a necessary factor that limits
EEG-based emotion recognition. It is a thought-provoking problem to availably employ time …
EEG-based emotion recognition. It is a thought-provoking problem to availably employ time …
Multi-modal physiological signals based squeeze-and-excitation network with domain adversarial learning for sleep staging
Sleep staging is the basis of sleep medicine for diagnosing psychiatric and
neurodegenerative diseases. However, the existing sleep staging methods ignore the fact …
neurodegenerative diseases. However, the existing sleep staging methods ignore the fact …
Beyond supervised learning for pervasive healthcare
The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
End-to-end fatigue driving EEG signal detection model based on improved temporal-graph convolution network
H Jia, Z Xiao, P Ji - Computers in Biology and Medicine, 2023 - Elsevier
Fatigue driving is one of the leading causes of traffic accidents, so fatigue driving detection
technology plays a crucial role in road safety. The physiological information-based fatigue …
technology plays a crucial role in road safety. The physiological information-based fatigue …
Hybrid spiking neural network for sleep electroencephalogram signals
Sleep staging is important for assessing sleep quality. So far, many scholars have tried to
achieve automatic sleep staging by using neural networks. However, most researchers only …
achieve automatic sleep staging by using neural networks. However, most researchers only …
Spatial-temporal graph convolutional networks (STGCN) based method for localizing acoustic emission sources in composite panels
Z Zhao, NZ Chen - Composite Structures, 2023 - Elsevier
A novel spatial–temporal graph convolutional networks (STGCN) based method for the
regression task of localizing acoustic emission (AE) sources in composite panels is …
regression task of localizing acoustic emission (AE) sources in composite panels is …
A novel EEG-based graph convolution network for depression detection: incorporating secondary subject partitioning and attention mechanism
Z Zhang, Q Meng, LC Jin, H Wang, H Hou - Expert Systems with …, 2024 - Elsevier
Electroencephalography (EEG) is capable of capturing the evocative neural information
within the brain. As a result, it has been increasingly used for identifying neurological …
within the brain. As a result, it has been increasingly used for identifying neurological …
[PDF][PDF] Teacher Assistant-Based Knowledge Distillation Extracting Multi-level Features on Single Channel Sleep EEG.
Sleep stage classification is of great significance to the diagnosis of sleep disorders.
However, existing sleep stage classification models based on deep learning are usually …
However, existing sleep stage classification models based on deep learning are usually …