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

Shaoqi Jiang

doi:10.3389/fnins.2023.1172103

2023-04-21

DOI：10.3389/fnins.2023.1172103

原文链接

查看原文

摘要

En 中文

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 alpha/beta (p = 0.071 < 0.1 in F and p = 0.042 < 0.05 in C), theta/(alpha + theta) (p = 0.048 < 0.05 in F and p = 0.026 < 0.05 in C) and (alpha + theta)/beta (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.

Keyword:

ship pilotage

cognitive competency

situation awareness

correlation evaluation

feature identification