Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-11335
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Main Title: Towards task-independent workload classification: Shifting from binary to continuous classification
Author(s): Zhang, Xixie
Krol, Laurens R.
Zander, Thorsten O.
Type: Conference Object
URI: https://depositonce.tu-berlin.de/handle/11303/12516
http://dx.doi.org/10.14279/depositonce-11335
License: http://rightsstatements.org/vocab/InC/1.0/
Abstract: Passive Brain-Computer-Interfaces provide a promising approach to the continuous measurement of mental workload in realistic scenarios. Typically, a BCI is calibrated to discriminate between different levels of workload induced by a specific task. However, workload in realistic scenarios is typically a result of a mixture of different tasks. Here, we present a study on investigating the possibility of a task-independent classifier, which can be applied to classify mental workload induced by various tasks (including n-back, backward span, addition, word recovery and mental rotation). Furthermore, our approach is not limited to binary classification of workload but can discriminate it on a continuous metric.
Subject(s): passive Brain-Computer-Interfaces
neuroadaptive classifier
task-independent technology
mental workload
continuous metric
Issue Date: 17-Jan-2019
Date Available: 25-Jan-2021
Language Code: en
DDC Class: 150 Psychologie
Proceedings Title: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Publisher: IEEE
Publisher DOI: 10.1109/SMC.2018.00104
Page Start: 556
Page End: 561
EISSN: 2577-1655
ISBN: 978-1-5386-6650-0
978-1-5386-6651-7
ISSN: 1062-922X
TU Affiliation(s): Fak. 5 Verkehrs- und Maschinensysteme » Inst. Psychologie und Arbeitswissenschaft » FG Biopsychologie und Neuroergonomie
Appears in Collections:Technische Universit├Ąt Berlin » Publications

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