Feasibility of local interpretable model-agnostic explanations (LIME) algorithm as an effective and interpretable feature selection method: comparative fNIRS study
Author:
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
Reference33 articles.
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2. Yucel MA, et al. Best practices for fNIRS publications (vol 8, 012101, 2020). Neurophotonics. 2021;8(1):019802. https://doi.org/10.1117/1.NPh.8.1.019802.
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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