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 …

A central problem in unsupervised deep learning is how to find useful representations of
high-dimensional data, sometimes called" disentanglement." Most approaches are heuristic …

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 …

Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …

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 …

Learning time-series representations when only unlabeled data or few labeled samples are
available can be a challenging task. Recently, contrastive self-supervised learning has …

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 …

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 …

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 …

Our ability to use deep learning approaches to decipher neural activity would likely benefit
from greater scale, in terms of both the model size and the datasets. However, the …