Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review

TL;DR

This review paper discusses the current state and challenges of applying Uncertainty Quantification in machine learning models for biosignal analysis, emphasizing medical interpretability and robustness.

Contribution

It provides a comprehensive overview of existing UQ methods, identifies gaps, and offers recommendations for future research in biosignal applications.

Findings

Promising UQ methods exist for biosignals.

Current research gaps in clinical and prosthetic applications.

Need for studies on human-system interaction with uncertainty models.

Abstract

Uncertainty Quantification (UQ) has gained traction in an attempt to improve the interpretability and robustness of machine learning predictions. Specifically (medical) biosignals such as electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), and electromyography (EMG) could benefit from good UQ, since these suffer from a poor signal-to-noise ratio, and good human interpretability is pivotal for medical applications. In this paper, we review the state of the art of applying Uncertainty Quantification to Machine Learning tasks in the biosignal domain. We present various methods, shortcomings, uncertainty measures and theoretical frameworks that currently exist in this application domain. We address misconceptions in the field, provide recommendations for future work, and discuss gaps in the literature in relation to diagnostic implementations as well as control for prostheses or brain-computer interfaces. Overall it can be concluded that promising UQ methods are available, but that research is needed on how people and systems may interact with an uncertainty-model in a (clinical) environment