Modeling of Human Operator Behavior for Brain-Actuated Mobile Robots Steering
Human operator control of brain-actuated robot steering based on electroencephalograph (EEG)-signals is a complex behavior consisting of surroundings perceiving, decision making, and commands issuing and differs among individual operators. However, no existing models allow decoupling the user from the loop to improve the system design and testing process, which can capture such behavior of a brain-actuated robot. To address this problem, in this paper, we propose an operator brain-controlled steering model consisting of an operator decision model based on the queuing network (QN) cognitive architecture and a brain-machine interface (BMI) performance model. The QN-based operator decision model can mimic the human decision process with the individual operator differences considered. The new BMI performance model is built to represent the varied accuracy of BMI during brain-controlled direction operations. Furthermore, the model is simulated and validated against the results of human operator-in-the-loop experiments. The results show that the proposed model can reproduce the behavior of human operators thanks to its similar direction control performance.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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| Enthalten in: |
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society - 28(2020), 9 vom: 30. Sept., Seite 2063-2072 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Hongqi [VerfasserIn] |
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| Anmerkungen: |
Date Completed 24.06.2021 Date Revised 24.06.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1109/TNSRE.2020.3009376 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM313216444 |
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| 520 | |a Human operator control of brain-actuated robot steering based on electroencephalograph (EEG)-signals is a complex behavior consisting of surroundings perceiving, decision making, and commands issuing and differs among individual operators. However, no existing models allow decoupling the user from the loop to improve the system design and testing process, which can capture such behavior of a brain-actuated robot. To address this problem, in this paper, we propose an operator brain-controlled steering model consisting of an operator decision model based on the queuing network (QN) cognitive architecture and a brain-machine interface (BMI) performance model. The QN-based operator decision model can mimic the human decision process with the individual operator differences considered. The new BMI performance model is built to represent the varied accuracy of BMI during brain-controlled direction operations. Furthermore, the model is simulated and validated against the results of human operator-in-the-loop experiments. The results show that the proposed model can reproduce the behavior of human operators thanks to its similar direction control performance | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Shi, Haonan |e verfasserin |4 aut | |
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