Machine learning-based identification and brain network characterization of flight cadets compared with air traffic control (ATC) students using static and dynamic functional connectivity.
- DOI
- 10.3389/fnhum.2026.1873697
- Published
- 2026
- Container
- Frontiers in human neuroscience
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3389/fnhum.2026.1873697,
title = {Machine learning-based identification and brain network characterization of flight cadets compared with air traffic control (ATC) students using static and dynamic functional connectivity.},
author = {Ye L and Zhang Y and Ba L and Yan D},
year = {2026},
journal = {Frontiers in human neuroscience},
doi = {10.3389/fnhum.2026.1873697},
url = {https://doi.org/10.3389/fnhum.2026.1873697}
}RIS
TY - JOUR TI - Machine learning-based identification and brain network characterization of flight cadets compared with air traffic control (ATC) students using static and dynamic functional connectivity. AU - Ye L AU - Zhang Y AU - Ba L AU - Yan D PY - 2026 JO - Frontiers in human neuroscience DO - 10.3389/fnhum.2026.1873697 UR - https://doi.org/10.3389/fnhum.2026.1873697 ER -
APA
L, Y., Y, Z., L, B., & D, Y. (2026). Machine learning-based identification and brain network characterization of flight cadets compared with air traffic control (ATC) students using static and dynamic functional connectivity.. Frontiers in human neuroscience. https://doi.org/10.3389/fnhum.2026.1873697
Source records
- pubmed · retrieved 2026-09-25T10:41:33.314Z