Deep Model for Improved Operator Function State Assessment

Document Type

Conference Paper

Publication Date

2014

Publication Title

Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference

Pages

58-59

Conference Name

8th Annual Modeling, Simulation & Visualization Student Capstone Conference, April 17, 2014, Suffolk, Virginia

Abstract

A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation.

Comments

Link to proceedings as a whole: https://digitalcommons.odu.edu/msvcapstone/proceedings/2014/1

Original Publication Citation

Li, F., Wen, J., Li, J., Zhang, G., Xu, R., & Schnell, T. (2014). Deep model for improved operator function state assessment. In Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference (pp. 58-59), Old Dominion University. https://digitalcommons.odu.edu/cgi/viewcontent.cgi?article=1021&context=msvcapstone#page=59

ORCID

0000-0003-0091-6986 (Li)

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