EEG-based analysis for pilots’ at-risk cognitive competency identification using RF-CNN algorithm

Abstract

Cognitive competency is an essential complement to the existing ship pilot screening system that should be focused on. Situation awareness (SA), as the cognitive foundation of unsafe behaviors, is susceptible to influencing piloting performance. To address this issue, this paper develops an identification model based on random forest- convolutional neural network (RF-CNN) method for detecting at-risk cognitive competency (i.e., low SA level) using wearable EEG signal acquisition technology. In the poor visibility scene, the pilots’ SA levels were correlated with EEG frequency metrics in frontal (F) and central (C) regions, including α/β (p = 0.071 < 0.1 in F and p = 0.042 < 0.05 in C), θ/(α + θ) (p = 0.048 < 0.05 in F and p = 0.026 < 0.05 in C) and (α + θ)/β (p = 0.046 < 0.05 in F and p = 0.012 < 0.05 in C), and then a total of 12 correlation features were obtained based on a 5 s sliding time window. Using the RF algorithm developed by principal component analysis (PCA) for further feature combination, these salient combinations are used as input sets to obtain the CNN algorithm with optimal parameters for identification. The comparative results of the proposed RF-CNN (accuracy is 84.8%) against individual RF (accuracy is 78.1%) and CNN (accuracy is 81.6%) methods demonstrate that the RF-CNN with feature optimization provides the best identification of at-risk cognitive competency (accuracy increases 6.7%). Overall, the results of this paper provide key technical support for the development of an adaptive evaluation system of pilots’ cognitive competency based on intelligent technology, and lay the foundation and framework for monitoring the cognitive process and competency of ship piloting operation in China.

The Use of Quantitative Electroencephalography (QEEG) to Assess Post-COVID-19 Concentration Disorders in Professional Pilots: An Initial Concept

“…The only study that demonstrates the impact of COVID-19 on this professional group relates to the risk of SARS-CoV-2 virus infection in small aircraft, which can occur during flights between pilots [ 37 ]. Therefore, in our opinion, the use of quantitative electroencephalography as a method to diagnose the impact of the SARS-CoV-2 virus on the central nervous system of professional pilots represents an innovative approach, as it is the first study conducted on this professional group in which cognitive function disorders have appeared after recovering from the coronavirus [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ].…”

Advancing Aviation Safety Through Machine Learning and Psychophysiological Data: A Systematic Review

“…ML, on the other hand, serves as the analytical framework capable of extracting granular insights from this data. This synergistic relationship has given rise to groundbreaking studies that have significantly extended our understanding of human performance and decision-making within aviation contexts [23]- [28].…”

Recognizing the situation awareness of forklift operators based on EEG techniques in a field experiment

“… Feng et al (2022) used SA-sensitive EEG features fed into principal component analysis (PCA) and the Bayes method to discriminate different SA groups, and the accuracies were 83.3% for the original validation and 70.8% for the cross-validation. Jiang et al (2023) developed an RF algorithm by PCA on EEG features with significant correlation with SA for further feature combination, which was then fed into CNN classification algorithm to obtain a classification recognition accuracy of 84.8%.…”