Towards task-independent workload classification: Shifting from binary to continuous classification
| dc.contributor.author | Zhang, Xixie | |
| dc.contributor.author | Krol, Laurens R. | |
| dc.contributor.author | Zander, Thorsten O. | |
| dc.date.accessioned | 2021-01-25T08:51:33Z | |
| dc.date.available | 2021-01-25T08:51:33Z | |
| dc.date.issued | 2019-01-17 | |
| dc.description.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. | en |
| dc.identifier.eissn | 2577-1655 | |
| dc.identifier.isbn | 978-1-5386-6650-0 | |
| dc.identifier.isbn | 978-1-5386-6651-7 | |
| dc.identifier.issn | 1062-922X | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/12516 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-11335 | |
| dc.language.iso | en | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject.ddc | 150 Psychologie | de |
| dc.subject.other | passive Brain-Computer-Interfaces | en |
| dc.subject.other | neuroadaptive classifier | en |
| dc.subject.other | task-independent technology | en |
| dc.subject.other | mental workload | en |
| dc.subject.other | continuous metric | en |
| dc.title | Towards task-independent workload classification: Shifting from binary to continuous classification | en |
| dc.type | Conference Object | en |
| dc.type.version | acceptedVersion | en |
| dcterms.bibliographicCitation.doi | 10.1109/SMC.2018.00104 | en |
| dcterms.bibliographicCitation.originalpublishername | IEEE | en |
| dcterms.bibliographicCitation.originalpublisherplace | New York, NY | en |
| dcterms.bibliographicCitation.pageend | 561 | en |
| dcterms.bibliographicCitation.pagestart | 556 | en |
| dcterms.bibliographicCitation.proceedingstitle | 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 5 Verkehrs- und Maschinensysteme::Inst. Psychologie und Arbeitswissenschaft::FG Biopsychologie und Neuroergonomie | de |
| tub.affiliation.faculty | Fak. 5 Verkehrs- und Maschinensysteme | de |
| tub.affiliation.group | FG Biopsychologie und Neuroergonomie | de |
| tub.affiliation.institute | Inst. Psychologie und Arbeitswissenschaft | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |