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 …
Toward open-world electroencephalogram decoding via deep learning: A comprehensive survey
Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and
cognitive content of neural processing based on noninvasively measured brain activity …
cognitive content of neural processing based on noninvasively measured brain activity …
Decoding speech perception from non-invasive brain recordings
Decoding speech from brain activity is a long-awaited goal in both healthcare and
neuroscience. Invasive devices have recently led to major milestones in this regard: deep …
neuroscience. Invasive devices have recently led to major milestones in this regard: deep …
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 …
Augmentation-free self-supervised learning on graphs
Inspired by the recent success of self-supervised methods applied on images, self-
supervised learning on graph structured data has seen rapid growth especially centered on …
supervised learning on graph structured data has seen rapid growth especially centered on …
BENDR: Using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data
Deep neural networks (DNNs) used for brain–computer interface (BCI) classification are
commonly expected to learn general features when trained across a variety of contexts, such …
commonly expected to learn general features when trained across a variety of contexts, such …
Contrastive representation learning for electroencephalogram classification
Interpreting and labeling human electroencephalogram (EEG) is a challenging task
requiring years of medical training. We present a framework for learning representations …
requiring years of medical training. We present a framework for learning representations …
Contrastive learning of subject-invariant EEG representations for cross-subject emotion recognition
EEG signals have been reported to be informative and reliable for emotion recognition in
recent years. However, the inter-subject variability of emotion-related EEG signals still poses …
recent years. However, the inter-subject variability of emotion-related EEG signals still poses …
[HTML][HTML] Self-supervised representation learning from 12-lead ECG data
Abstract Clinical 12-lead electrocardiography (ECG) is one of the most widely encountered
kinds of biosignals. Despite the increased availability of public ECG datasets, label scarcity …
kinds of biosignals. Despite the increased availability of public ECG datasets, label scarcity …
Cocoa: Cross modality contrastive learning for sensor data
Self-Supervised Learning (SSL) is a new paradigm for learning discriminative
representations without labeled data, and has reached comparable or even state-of-the-art …
representations without labeled data, and has reached comparable or even state-of-the-art …