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 …
Self-supervised contrastive learning for medical time series: A systematic review
Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
Learning topology-agnostic eeg representations with geometry-aware modeling
Large-scale pre-training has shown great potential to enhance models on downstream tasks
in vision and language. Develo** similar techniques for scalp electroencephalogram …
in vision and language. Develo** similar techniques for scalp electroencephalogram …
A multi-view spectral-spatial-temporal masked autoencoder for decoding emotions with self-supervised learning
Affective Brain-computer Interface has achieved considerable advances that researchers
can successfully interpret labeled and flawless EEG data collected in laboratory settings …
can successfully interpret labeled and flawless EEG data collected in laboratory settings …
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand …
Self-supervised EEG emotion recognition models based on CNN
Emotion plays crucial roles in human life. Recently, emotion classification from
electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid …
electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid …
Pre-training in medical data: A survey
Medical data refers to health-related information associated with regular patient care or as
part of a clinical trial program. There are many categories of such data, such as clinical …
part of a clinical trial program. There are many categories of such data, such as clinical …
Transformer-based self-supervised multimodal representation learning for wearable emotion recognition
Recently, wearable emotion recognition based on peripheral physiological signals has
drawn massive attention due to its less invasive nature and its applicability in real-life …
drawn massive attention due to its less invasive nature and its applicability in real-life …
Label-efficient time series representation learning: A review
Label-efficient time series representation learning, which aims to learn effective
representations with limited labeled data, is crucial for deploying deep learning models in …
representations with limited labeled data, is crucial for deploying deep learning models in …
Self-supervised learning for label-efficient sleep stage classification: A comprehensive evaluation
The past few years have witnessed a remarkable advance in deep learning for EEG-based
sleep stage classification (SSC). However, the success of these models is attributed to …
sleep stage classification (SSC). However, the success of these models is attributed to …