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 supervised learning paradigm is limited by the cost-and sometimes the impracticality-of
data collection and labeling in multiple domains. Self-supervised learning, a paradigm …

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

We introduce and compare several strategies for learning discriminative features from
electroencephalography (EEG) recordings using deep learning techniques. EEG data are …

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 …

Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …

Interpreting and labeling human electroencephalogram (EEG) is a challenging task
requiring years of medical training. We present a framework for learning representations …

Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG),
often have noisy labels and have limited number of subjects (< 100). To handle these …

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 …

The study of psychiatric and neurologic disorders typically involves the acquisition of a wide
range of different types of data, such as brain images, electronic health records, and mobile …