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Title: Construction of air traffic controller’s decision network using error-related potential
Authors: Goh, Sim Kuan
Tran, Ngoc Phu
Pham, Duc-Thinh
Alam, Sameer
Izzetoglu, Kurtulus
Duong, Vu
Keywords: Engineering::Aeronautical engineering::Aviation
Issue Date: 2019
Source: Goh, S. K., Tran, N. P., Pham, D.-T., Alam, S., Izzetoglu, K., & Duong, V. (2019). Construction of air traffic controller’s decision network using error-related potential. Proceedings of the HCI International (HCII) The International Conference on Human-Computer Interaction, 384-393. doi:10.1007/978-3-030-22419-6_27
Abstract: Electroencephalography based brain computer interface has enabled communication of human’s intention to a computer directly from the brain by decoding signatures that relay the intention information. Error-related potential has been adopted as a signature for natural communication and performance monitoring, among others. In this work, we investigate the use of error-related potential as an input channel to transfer human preference of a strategical advisory to a computer. Air traffic control task was used as a case study to make an empirical inquiry of error-related potential for higher level cognitive tasks (i.e. situation awareness in air traffic control tasks). The experimental task requires the subjects to monitor and assess air traffic scenarios presented on a simulated environment that provides conflict resolution advisories. The task is known to be highly mental demanding as it requires continuous situation awareness of the traffic. An interface and experimental protocol were developed for this experiment to validate that error-related potential can be used a new channel for preference. The implementation of the complete design was described together with the experimental evidence of error-related potential. According to the results, we found error-related potential that is in line with existing literature. We also discussed how the preliminary findings of this work can be used as an integral part of an intelligent conflict resolution advisory system that can learn from human preference and duplicate the decision making.
ISBN: 9783030224189
DOI: 10.1007/978-3-030-22419-6_27
Rights: © 2019 Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in HCI International (HCII) The International Conference on Human-Computer Interaction. The final authenticated version is available online at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers
MAE Conference Papers

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