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• W3217604234 abstract "In heavily automated environments, such as modern-day cockpits, operators perform prolonged monitoring tasks, during which critical events may occur only rarely. A user model can help inform the automated environment about the operator's current mental state, allowing it to adapt accordingly and provide support. By using a passive brain-computer interface (pBCI), information about the momentary mental state of operators can be decoded and fed into such a model. However, pBCI classifiers commonly require large amounts of training data, which cannot realistically be gathered for rare critical events. Here, we trained a classifier on an abstract 15-minute oddball task and investigated its ability to differentiate between auditory events of different criticality during simulated flight. Results indicate that classifiers trained in brief, standardized sessions before flight can distinguish between brain responses to events of different categories during simulated flight. We discuss how this can be used to inform neuroadaptive user models." @default.
• W3217604234 created "2021-12-06" @default.
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• W3217604234 date "2022-01-01" @default.
• W3217604234 modified "2023-10-05" @default.
• W3217604234 title "Towards neuroadaptive modeling: assessing the cognitive states of pilots through passive brain-computer interfacing" @default.
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• W3217604234 doi "https://doi.org/10.1016/b978-0-12-821413-8.00009-9" @default.
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