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arXiv Machine Learning By Ivo Pascal de Jong, Andreea Ioana Sburlea, Matias Valdenegro-Toro

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

Read the original on arXiv Machine Learning →

The review examines how Uncertainty Quantification (UQ) can enhance machine learning applied to biosignals such as EEG, ECG, EOG, and EMG. It surveys 53 papers, outlining current methods, shortcomings, and theoretical frameworks, while highlighting misconceptions and gaps in diagnostic and prosthetic control contexts. The authors recommend further research on human-system interaction with UQ models in clinical settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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