Open Access

ARTICLE

A Method for Classification and Evaluation of Pilot’s Mental States Based on CNN

by Qianlei Wang^1,2,3,*, Zaijun Wang³, Renhe Xiong⁴, Xingbin Liao^1,2, Xiaojun Tan⁵

1 Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, 610041, Sichuan, China
2 School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100080, China
3 Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University of China, Guanghan, 618307, Sichuan, China
4 School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, 618307, Sichuan, China
5 Neurology Department, Deyang Second People's Hospital, Guanghan, 618307, Sichuan, China

* Corresponding Author: Qianlei Wang. Email:

Computer Systems Science and Engineering 2023, 46(2), 1999-2020. https://doi.org/10.32604/csse.2023.034183

Received 08 July 2022; Accepted 08 December 2022; Issue published 09 February 2023

Abstract

How to accurately recognize the mental state of pilots is a focus in civil aviation safety. The mental state of pilots is closely related to their cognitive ability in piloting. Whether the cognitive ability meets the standard is related to flight safety. However, the pilot's working state is unique, which increases the difficulty of analyzing the pilot's mental state. In this work, we proposed a Convolutional Neural Network (CNN) that merges attention to classify the mental state of pilots through electroencephalography (EEG). Considering the individual differences in EEG, semi-supervised learning based on improved K-Means is used in the model training to improve the generalization ability of the model. We collected the EEG data of 12 pilot trainees during the simulated flight and compared the method in this paper with other methods on this data. The method in this paper achieved an accuracy of 86.29%, which is better than 4D-aNN and HCNN etc. Negative emotion will increase the probability of fatigue appearing, and emotion recognition is also meaningful during the flight. Then we conducted experiments on the public dataset SEED, and our method achieved an accuracy of 93.68%. In addition, we combine multiple parameters to evaluate the results of the classification network on a more detailed level and propose a corresponding scoring mechanism to display the mental state of the pilots directly.

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Keywords

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