@inproceedings{fa4c6599b7c44b7982780b4a6085d974,
title = "Research on Non-Interruptive Situational Awareness Measurement and Analysis Method Based on Machine Learning",
abstract = "This study proposes a machine learning- based noninterruptive framework to address limitations of traditional situational awareness (SA) measurement methods in human-machine collaboration, which disrupt task continuity and data accuracy. Using EEG signals and behavioral data from 12 operators via the MATB platform, we developed multi-domain cognitive features and trained SVM/Random Forest models to predict SA during continuous tasks. By integrating EEG and behavioral analysis, the framework enhances measurement precision while preserving operational fluency, offering a novel approach to optimize humanmachine efficiency in complex systems.",
keywords = "EEG signals, Machine learning, Non-interruptive, Situational awareness measurement",
author = "Yingyue Zhang and Xing Pan",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 16th International Conference on Reliability, Maintainability and Safety, ICRMS 2025 ; Conference date: 27-07-2025 Through 30-07-2025",
year = "2025",
doi = "10.1109/ICRMS65480.2025.00102",
language = "英语",
series = "Proceedings - 2025 16th International Conference on Reliability, Maintainability and Safety, ICRMS 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "562--567",
booktitle = "Proceedings - 2025 16th International Conference on Reliability, Maintainability and Safety, ICRMS 2025",
address = "美国",
}