Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916779373101056 |
|---|---|
| author | de Jong, Ivo Pascal Sburlea, Andreea Ioana Valdenegro-Toro, Matias |
| author_facet | de Jong, Ivo Pascal Sburlea, Andreea Ioana Valdenegro-Toro, Matias |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_09454 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review de Jong, Ivo Pascal Sburlea, Andreea Ioana Valdenegro-Toro, Matias Signal Processing Human-Computer Interaction Machine Learning 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 |
| title | Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review |
| topic | Signal Processing Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2312.09454 |