Paper Info
Title
Decoding Pilot Behavior Consciousness Of Eeg, Ecg, Eye Movements Via An Svm Machine Learning Model
Abstract
To decode the pilot's behavioral awareness, an experiment is designed to use an aircraft simulator obtaining the pilot's physiological behavior data. Existing pilot behavior studies such as behavior modeling methods based on domain experts and behavior modeling methods based on knowledge discovery do not proceed from the characteristics of the pilots themselves. The experiment starts directly from the multimodal physiological characteristics to explore pilots' behavior. Electroencephalography, electrocardiogram, and eye movement were recorded simultaneously. Extracted multimodal features of ground missions, air missions, and cruise mission were trained to generate support vector machine behavior model based on supervised learning. The results showed that different behaviors affects different multiple rhythm features, which are power spectra of the. waves of EEG, standard deviation of normal to normal, root mean square of standard deviation and average gaze duration. The different physiological characteristics of the pilots could also be distinguished using an SVM model. Therefore, the multimodal physiological data can contribute to future research on the behavior activities of pilots. The result can be used to design and improve pilot training programs and automation interfaces.
Year
DOI
Venue
2020
10.1142/S1793962320500282
INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING
Keywords
DocType
Volume
Pilots' behavior, decision making, aircraft simulator, multimodal physiological features, SVM model
Journal
11
Issue
ISSN
Citations 
4
1793-9623
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Xiashuang Wang100.34
Guanghong Gong28221.16
Ni Li3387.07
Li Ding442.47
Yaofei Ma512.43