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Im C, Shin J, Lee WR, et al. Machine learning-based feature combination analysis for odor-dependent hemodynamic responses of rat olfactory bulb. Biosens Bioelectron. 2021;197:113782doi: 10.1016/j.bios.2021.113782.
Im, C., Shin, J., Lee, W. R., & Kim, J. M. (2022). Machine learning-based feature combination analysis for odor-dependent hemodynamic responses of rat olfactory bulb. Biosensors & bioelectronics, 197113782. https://doi.org/10.1016/j.bios.2021.113782
Im, Changkyun, et al. "Machine learning-based feature combination analysis for odor-dependent hemodynamic responses of rat olfactory bulb." Biosensors & bioelectronics vol. 197 (2022): 113782. doi: https://doi.org/10.1016/j.bios.2021.113782
Im C, Shin J, Lee WR, Kim JM. Machine learning-based feature combination analysis for odor-dependent hemodynamic responses of rat olfactory bulb. Biosens Bioelectron. 2022 Feb 01;197:113782. doi: 10.1016/j.bios.2021.113782. Epub 2021 Nov 12. PMID: 34814029.
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Biosens Bioelectron. 2022 Feb 01;197:113782. doi: 10.1016/j.bios.2021.113782. Epub 2021 Nov 12.

Machine learning-based feature combination analysis for odor-dependent hemodynamic responses of rat olfactory bulb.

Biosensors & bioelectronics

Changkyun Im, Jaewoo Shin, Woo Ram Lee, Jun-Min Kim

Affiliations

  1. Bio & Medical Health Division, Korea Testing Laboratory, Seoul, 08389, Republic of Korea.
  2. Hurvitz Brain Sciences Research Program, Biological Sciences, Sunnybrook Research Institute, Toronto, ON, M4N 3M5, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, M5S 1A8, Canada; Department of Neurosurgery, Brain Research Institute, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea.
  3. Department of Electronic Engineering, Gyeonggi University of Science and Technology, Siheung, 15073, Republic of Korea. Electronic address: [email protected].
  4. Department of Mechanical Systems Engineering Electronics, Hansung University, Seoul, 02876, Republic of Korea. Electronic address: [email protected].

PMID: 34814029 DOI: 10.1016/j.bios.2021.113782

Abstract

Rodents have a well-developed sense of smell and are used to detect explosives, mines, illegal substances, hidden currency, and contraband, but it is impossible to keep their concentration constantly. Therefore, there is an ongoing effort to infer odors detected by animals without behavioral readings with brain-computer interface (BCI) technology. However, the invasive BCI technique has the disadvantage that long-term studies are limited by the immune response and electrode movement. On the other hand, near-infrared spectroscopy (NIRS)-based BCI technology is a non-invasive method that can measure neuronal activity without worrying about the immune response or electrode movement. This study confirmed that the NIRS-based BCI technology can be used as an odor detection and identification from the rat olfactory system. In addition, we tried to present features optimized for machine learning models by extracting six features, such as slopes, peak, variance, mean, kurtosis, and skewness, from the hemodynamic response, and analyzing the importance of individuals or combinations. As a result, the feature with the highest F1-Score was indicated as slopes, and it was investigated that the combination of the features including slopes and mean was the most important for odor inference. On the other hand, the inclusion of other features with a low correlation with slopes had a positive effect on the odor inference, but most of them resulted in insignificant or rather poor performance. The results presented in this paper are expected to serve as a basis for suggesting the development direction of the hemodynamic response-based bionic nose in the future.

Copyright © 2021 Elsevier B.V. All rights reserved.

Keywords: Feature combination analysis; Hemodynamic response; Machine learning; Near-infrared spectroscopy; Rat olfactory bulb

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