Feasibility of local interpretable model-agnostic explanations (LIME) algorithm as an effective and interpretable feature selection method: comparative fNIRS study-Reference-Cited by-同舟云学术

Feasibility of local interpretable model-agnostic explanations (LIME) algorithm as an effective and interpretable feature selection method: comparative fNIRS study

Published:2023-06-07 Issue:4 Volume:13 Page:689-703
ISSN:2093-9868
Container-title:Biomedical Engineering Letters
language:en
Short-container-title:Biomed. Eng. Lett.

Author:

Shin Jaeyoung^ORCID

Funder

Wonkwang University

Publisher

Springer Science and Business Media LLC

Subject

Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s13534-023-00291-x.pdf

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3. von Luhmann A, Ortega-Martinez A, Boas DA, Yucel MA. Using the general linear model to improve performance in fNIRS single trial analysis and classification: a perspective (in English). Front Hum Neurosci. 2020;14(30):30. https://doi.org/10.3389/fnhum.2020.00030.

4. Paulmurugan K, Vijayaragavan V, Ghosh S, Padmanabhan P, Gulyas B. Brain-computer interfacing using functional near-infrared spectroscopy (fNIRS). Biosensors. 2021;11(10):389. https://doi.org/10.3390/bios11100389.

5. von Luhmann A, et al. Toward Neuroscience of the Everyday World (NEW) using functional near-infrared spectroscopy. Curr Opin Biomed Eng. 2021;18:100272. https://doi.org/10.1016/j.cobme.2021.100272.

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