mlsanityData to 14 Oct 2026

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

arXiv 2312.09454 ↗First posted 15 Nov 2023eess.SP cs.HC cs.LGcited by 7

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Abstract (arXiv)

Purpose: 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. Methods: 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. Results: 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. Conclusion: 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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 48 of its 157 references that are in the map. First posted on arXiv 15 Nov 2023.

Builds on

48 of 157 references.

References · 48

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36 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 3.

7 papers.

  1. 2025PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representationcited by 12
  2. 2024Uncertainty Quantification for cross-subject Motor Imagery classificationcited by 3
  3. 2026Is the Last Layer Sufficient for Uncertainty Quantification?cited by 2
  4. 2025Uncertainty Quantification for Motor Imagery BCI -- Machine Learning vs. Deep Learningcited by 1
  5. 2024Bayesian uncertainty-aware deep learning with noisy labels: Tackling annotation ambiguity in EEG seizure detectioncited by 0
  6. 2026AISA: AI Safety Assistant Framework for Continuous Improvement of Highway Constructioncited by 0
  7. 2026Does Model Uncertainty Track Human Ambiguity? Evidence from Multi-Annotator Vision Benchmarkscited by 0