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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. <\/jats:p>","DOI":"10.1142\/s1793962320500282","type":"journal-article","created":{"date-parts":[[2020,7,2]],"date-time":"2020-07-02T15:26:02Z","timestamp":1593703562000},"page":"2050028","source":"Crossref","is-referenced-by-count":14,"title":["Decoding pilot behavior consciousness of EEG, ECG, eye movements via an SVM machine learning model"],"prefix":"10.1142","volume":"11","author":[{"given":"Xiashuang","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100083, P. R. China"},{"name":"Automation Science and Electrical Engineering, Beihang University, Beijing 100083, P. R. 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