[HTML][HTML] Wireless EEG: A survey of systems and studies
The popular brain monitoring method of electroencephalography (EEG) has seen a surge in
commercial attention in recent years, focusing mostly on hardware miniaturization. This has …
commercial attention in recent years, focusing mostly on hardware miniaturization. This has …
Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities
The vast proliferation of sensor devices and Internet of Things enables the applications of
sensor-based activity recognition. However, there exist substantial challenges that could …
sensor-based activity recognition. However, there exist substantial challenges that could …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
Deep learning for motor imagery EEG-based classification: A review
Objectives The availability of large and varied Electroencephalogram (EEG) datasets,
rapidly advances and inventions in deep learning techniques, and highly powerful and …
rapidly advances and inventions in deep learning techniques, and highly powerful and …
Learning adaptive spatial-temporal context-aware correlation filters for UAV tracking
Tracking in the unmanned aerial vehicle (UAV) scenarios is one of the main components of
target-tracking tasks. Different from the target-tracking task in the general scenarios, the …
target-tracking tasks. Different from the target-tracking task in the general scenarios, the …
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 …
Bidirectional spatial-temporal adaptive transformer for urban traffic flow forecasting
Urban traffic forecasting is the cornerstone of the intelligent transportation system (ITS).
Existing methods focus on spatial-temporal dependency modeling, while two intrinsic …
Existing methods focus on spatial-temporal dependency modeling, while two intrinsic …
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 …
Spatio-temporal-spectral hierarchical graph convolutional network with semisupervised active learning for patient-specific seizure prediction
Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced
technique for seizure prediction. Recent deep learning approaches, which fail to fully …
technique for seizure prediction. Recent deep learning approaches, which fail to fully …
[HTML][HTML] Multimodal detection of epilepsy with deep neural networks
Epilepsy constitutes a chronic noncommunicable disease of the brain affecting
approximately 50 million people around the world. Most of the existing research initiatives …
approximately 50 million people around the world. Most of the existing research initiatives …