The development of medical applications of machine learning has required manual
annotation of data, often by medical experts. Yet, the availability of large-scale unannotated …

Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …

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 …

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 …

Objective. Supervised learning paradigms are often limited by the amount of labeled data
that is available. This phenomenon is particularly problematic in clinically-relevant data …

The lack of labeled data is a key challenge for learning useful representation from time
series data. However, an unsupervised representation framework that is capable of …

The main purpose of this paper is to provide information on how to create a convolutional
neural network (CNN) for extracting features from EEG signals. Our task was to understand …

We propose a foundation model named Brant for modeling intracranial recordings, which
learns powerful representations of intracranial neural signals by pre-training, providing a …