Key result
Uncertainty quantification in biosignal machine learning shows promise but requires further clinical interaction research.
Why the study?
Medical biosignals suffer from poor signal-to-noise ratios and require high interpretability, creating a need to determine how uncertainty estimation can be used for biosignal machine learning tasks.
Systematic Review (n=53)
This systematic review highlights the growing use of uncertainty quantification in biosignal machine learning and emphasizes the need for research on clinical implementation.
UQ may boost ML robustness for noisy biosignals; leaves open optimal methods for routine ECG/EEG applications.
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. To determine how uncertainty estimation can be used for biosignal tasks, we investigate current methods, use cases, applications, evaluations, and uncertainty measures. In this paper, we systematically review the state of the art of applying Uncertainty Quantification to Machine Learning tasks in the biosignal domain. All works from Web of Science, Scopus, IEEE XPlore and PsycINFO that discuss uncertainty in Machine Learning on one of the aforementioned biosignals is included. We present various methods, shortcomings, uncertainty measures and theoretical frameworks that currently exist in this application domain based on the 53 reviewed papers and related literature. 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.
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Jong et al. (2026) conducted a systematic review in Biosignal applications (EEG, ECG, EOG, EMG) (n=53). Uncertainty Quantification methods vs. Standard Machine Learning models was evaluated. Uncertainty quantification methods for biosignal machine learning are promising, but further research is needed on how people and systems interact with uncertainty models in clinical environments.