dc.contributor.author	Miklody, Daniel
dc.contributor.author	Blankertz, Benjamin
dc.date.accessioned	2022-02-14T12:46:45Z
dc.date.available	2022-02-14T12:46:45Z
dc.date.issued	2022-01-27
dc.date.updated	2022-02-12T21:53:35Z
dc.description.abstract	Changing and often class-dependent non-stationarities of signals are a big challenge in the transfer of common findings in cognitive workload estimation using Electroencephalography (EEG) from laboratory experiments to realistic scenarios or other experiments. Additionally, it often remains an open question whether actual cognitive workload reflected by brain signals was the main contribution to the estimation or discriminative and class-dependent muscle and eye activity, which can be secondary effects of changing workload levels. Within this study, we investigated a novel approach to spatial filtering based on beamforming adapted to changing settings. We compare it to no spatial filtering and Common Spatial Patterns (CSP). We used a realistic maneuvering task, as well as an auditory n-back secondary task on a tugboat simulator as two different conditions to induce workload changes on professional tugboat captains. Apart from the typical within condition classification, we investigated the ability of the different classification methods to transfer between the n-back condition and the maneuvering task. The results show a clear advantage of the proposed approach over the others in the challenging transfer setting. While no filtering leads to lowest within-condition normalized classification loss on average in two scenarios (22 and 10%), our approach using adaptive beamforming (30 and 18%) performs comparably to CSP (33 and 15%). Importantly, in the transfer from one to another setting, no filtering and CSP lead to performance around chance level (45 to 53%), while our approach in contrast is the only one capable of classifying in all other scenarios (34 and 35%) with a significant difference from chance level. The changing signal composition over the scenarios leads to a need to adapt the spatial filtering in order to be transferable. With our approach, the transfer is successful due to filtering being optimized for the extraction of neural components and additional investigation of their scalp patterns revealed mainly neural origin. Interesting findings are that rather the patterns slightly change between conditions. We conclude that the approaches with low normalized loss depend on eye and muscle activity which is successful for classification within conditions, but fail in the classifier transfer since eye and muscle contributions are highly condition-specific.	en
dc.description.sponsorship	EC/FP7/611570/EU/Symbiotic Mind Computer Interaction for Information Seeking/MindSee	en
dc.description.sponsorship	BMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktion - Teilprojekte A1, A3, A4, B4, W3, Zentrum	en
dc.description.sponsorship	DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berlin	en
dc.identifier.eissn	1662-5161
dc.identifier.uri	https://depositonce.tu-berlin.de/handle/11303/16354
dc.identifier.uri	http://dx.doi.org/10.14279/depositonce-15130
dc.language.iso	en	en
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	en
dc.subject.ddc	004 Datenverarbeitung; Informatik	de
dc.subject.other	cognitive workload	en
dc.subject.other	EEG	en
dc.subject.other	spatial filtering	en
dc.subject.other	beamforming	en
dc.subject.other	non-stationarity	en
dc.subject.other	adaptation	en
dc.title	Cognitive Workload of Tugboat Captains in Realistic Scenarios: Adaptive Spatial Filtering for Transfer Between Conditions	en
dc.type	Article	en
dc.type.version	publishedVersion	en
dcterms.bibliographicCitation.articlenumber	818770	en
dcterms.bibliographicCitation.doi	10.3389/fnhum.2022.818770	en
dcterms.bibliographicCitation.journaltitle	Frontiers in Human Neuroscience	en
dcterms.bibliographicCitation.originalpublishername	Frontiers	en
dcterms.bibliographicCitation.originalpublisherplace	Lausanne	en
dcterms.bibliographicCitation.volume	16	en
tub.accessrights.dnb	free	en
tub.affiliation	Fak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Neurotechnologie	de
tub.affiliation.faculty	Fak. 4 Elektrotechnik und Informatik	de
tub.affiliation.group	FG Neurotechnologie	de
tub.affiliation.institute	Inst. Softwaretechnik und Theoretische Informatik	de
tub.publisher.universityorinstitution	Technische Universität Berlin	en

Cognitive Workload of Tugboat Captains in Realistic Scenarios: Adaptive Spatial Filtering for Transfer Between Conditions

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