Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146008
Title: EEG-based cadets training and performance assessment system in maritime virtual simulator
Authors: Liu, Yisi
Lan, Zirui
Sourina, Olga
Liew, Hui Ping
Krishnan, Gopala
Konovessis, Dimitrios
Ang, Hock Eng
Keywords: Engineering
Issue Date: 2018
Source: Liu, Y., Lan, Z., Sourina, O., Liew, H. P., Krishnan, G., Konovessis, D., & Ang, H. E. (2018). EEG-based cadets training and performance assessment system in maritime virtual simulator. Proceedings of the International Conference on Cyberworlds, 214-220. doi:10.1109/CW.2018.00047
Abstract: Deep investment in the maritime industries has led to many cutting edge technological advances in shipping navigation and operational safety to ensure safe and efficient logistical transportations. However, even with the best technology equipped onboard, maritime accidents are still occurring with at least three quarters of them attributed to human errors. Due to the rising need to address the human factors in shipping operations, various human factors studies are conducted in maritime domain. In this paper, an Electroencephalogram (EEG)-based cadets training and performance assessment system is proposed and implemented that could be used in the maritime virtual simulator. The system includes an EEG processing and analyses part and an evaluation part. It could recognize the brain states such as mental workload, emotions, and stress from raw EEG signal recorded during the exercises in the simulator and then give an indicative recommendation on "pass", "retrain", or "fail" of the cadet based on the EEG recognition results and input of the level of the task difficulty performed.
URI: https://hdl.handle.net/10356/146008
ISBN: 9781538673157
DOI: 10.1109/CW.2018.00047
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CW.2018.00047
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:Fraunhofer Singapore Conference Papers

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