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 [Formula: see text] 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.