EEG-based analysis for pilots’ at-risk cognitive competency identification using RF-CNN algorithm - Jiang, Shaoqi; Chen, Weijiong; Ren, Zhenzhen; Zhu, He (Frontiers in Neuroscience, 2023)
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. Take the poor visibility scene as an example, a total of twelve correlation features in α/β、θ/(α+θ), and (α+θ)/β frequency bands were then extracted from frontal and central regions, and a RF algorithm was developed using principal component analysis (PCA) after errors correction. This RF algorithm was used to further optimize the salient feature combinations. Using these combinations, a CNN algorithm with optimal parameters was trained for identification. The comparative results of the proposed RF-CNN against individual RF and CNN methods demonstrate that the RF-CNN with feature optimization provides the best identification of at-risk cognitive competency (accuracy increases from 78.1% to 84.8%). 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.