A task-independent workload classifier for neuroadaptive technology: Preliminary data

Krol, LR; Freytag, SC; Fleck, M; Gramann, K; Zander, TO

A task-independent workload classifier for neuroadaptive technology: Preliminary data

Krol, LR Freytag, SC Fleck, M Gramann, K

Zander, TO

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, 2017, pp. 3171 - 3174
Issue Date:: 2017-02-06

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Field	Value	Language
dc.contributor.author	Krol, LR	en_US
dc.contributor.author	Freytag, SC	en_US
dc.contributor.author	Fleck, M	en_US
dc.contributor.author	Gramann, K https://orcid.org/0000-0003-2673-1832	en_US
dc.contributor.author	Zander, TO	en_US
dc.date.issued	2017-02-06	en_US
dc.identifier.citation	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, 2017, pp. 3171 - 3174	en_US
dc.identifier.isbn	9781509018970	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130167
dc.description.abstract	© 2016 IEEE. Passive brain-computer interfacing allows computer systems direct access to aspects of their user's cognition. In essence, a computer system can gain information about its user without this user needing to explicitly communicate it. Based on this information, human-computer interaction can be made more symmetrical, solving an age-old but still fundamental problem of present-day interaction techniques. For practical real-world application of this technology, it is important that cognitive states can be identified accurately and efficiently. Here we present preliminary data demonstrating it is possible to calibrate a task-independent classifier to identify when a user is under heavy workload across different activities. We used different types of mental arithmetic and even a semantic task. Task-independent classification is an important step towards real-world practical application of this technology.	en_US
dc.relation.ispartof	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings	en_US
dc.relation.isbasedon	10.1109/SMC.2016.7844722	en_US
dc.title	A task-independent workload classifier for neuroadaptive technology: Preliminary data	en_US
dc.type	Conference Proceeding
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. Passive brain-computer interfacing allows computer systems direct access to aspects of their user's cognition. In essence, a computer system can gain information about its user without this user needing to explicitly communicate it. Based on this information, human-computer interaction can be made more symmetrical, solving an age-old but still fundamental problem of present-day interaction techniques. For practical real-world application of this technology, it is important that cognitive states can be identified accurately and efficiently. Here we present preliminary data demonstrating it is possible to calibrate a task-independent classifier to identify when a user is under heavy workload across different activities. We used different types of mental arithmetic and even a semantic task. Task-independent classification is an important step towards real-world practical application of this technology.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/130167