Decoding pilot behavior consciousness of EEG, ECG, eye movements via an SVM machine learning model

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 theθwaves 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.

Use of multimodal physiological signals to explore pilots’cognitive behaviour during flight strike task performance

This study explored the use of multi-physiological signals and simultaneously recorded high-density electroencephalography(EEG),electrocardiogram(ECG),and eye movements to better understand pilots’cognitive behaviour during flight simulator manoeuvres.Multimodal physiological signals were collected from 12 experienced pilots with international aviation qualifications under the wide-angle and impressive vision simulation.The data collection spanned two flight strike missions,each with three mission intensities,resulting in a data set of EEG,ECG,and eye movement signals from six subtasks.The multimodal data were analysed using signal processing methods.The results indicated that,when the flight missions were performed,the pilots’physiological characteristics exhibited rhythmic changes in the power spectrum ofθwaves in the EEG,r-MSSD in the ECG,and average gaze duration.Furthermore,the pilots’physiological signals were more sensitive during the target mission than during the empty target mission.The results also showed correlations between different physiological characteristics.We showed that specific multimodal features are useful for advancing neuroscience research into pilots’cognitive behaviour and processes related to brain activity,psychological rhythms,and eye movement.