Publicacions CVC -- Edit Record

	Publicacions CVC Home \| Show All \| Simple Search \| Advanced Search \| Add Record \| Import	You must login to submit this form! Login Quick Search: Field: contains: ...
	Edit the following record:

Author	...				is Editor
Title	...			Type
Year	...	Publication	...	Abbreviated Journal	...
Volume	...	Issue	...	Pages	...
Keywords	...
Abstract	High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models.
Address	...
Corporate Author	...			Thesis
Publisher	...	Place of Publication	...	Editor	...
Language	...	Summary Language	...	Original Title	...
Series Editor	...	Series Title	...	Abbreviated Series Title	...
Series Volume	...	Series Issue	...	Edition	...
ISSN	...	ISBN	...	Medium	...
Area	...	Expedition	...	Conference	...
Notes	...			Approved	yes no
Location
Call Number	...			Serial
Marked	yes no	Copy		Selected	yes no
User Keys	...
User Notes	...			User File	...
User Groups	...			Cite Key	...
Related	...
File
URL	...			DOI	...
	Online publication. Cite with this text: ...

Location Field:	my name & email address

Home

SQL Search | Library Search | Show Record | Extract Citations

Help