ArticleSensors (Basel, Switzerland)2024
Detection of Pilots' Psychological Workload during Turning Phases Using EEG Characteristics.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Noise-robust temporal-spectral fusion transformers for EEG-based cognitive state classification in aviation environments.Frontiers in big data · 2026Article
- Study on the effects of mental training on brain functional networks in operators during the glide phase of simulated carrier-based aircraft landing.Frontiers in psychology · 2026Article
- Workload analysis of pilot steep turn maneuvers using SR20 aircraft and EEG data.Frontiers in neuroscience · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
Pilot behavior is crucial for aviation safety. This study aims to investigate the EEG characteristics of pilots, refine training assessment methodologies, and bolster flight safety measures. The collected EEG signals underwent initial preprocessing. The EEG characteristic analysis was performed during left and right turns, involving the calculation of the energy ratio of beta waves and Shannon entropy. The psychological workload of pilots during different flight phases was quantified as well. Based on the EEG characteristics, the pilots' psychological workload was classified through the use of a support vector machine (SVM). The study results showed significant changes in the energy ratio of beta waves and Shannon entropy during left and right turns compared to the cruising phase. Additionally, the pilots' psychological workload was found to have increased during these turning phases. Using support vector machines to detect the pilots' psychological workload, the classification accuracy for the training set was 98.92%, while for the test set, it was 93.67%. This research holds significant importance in understanding pilots' psychological workload.
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Registered trials
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