EEG workload prediction in a closed-loop learning environment

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dc.contributor.advisor Bogdan, Martin (Prof. Dr.)
dc.contributor.author Walter, Carina Benigna
dc.date.accessioned 2015-11-12T14:39:03Z
dc.date.available 2015-11-12T14:39:03Z
dc.date.issued 2015-11-09
dc.identifier.other 45168236X de_DE
dc.identifier.uri http://hdl.handle.net/10900/66395
dc.identifier.uri http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-663950 de_DE
dc.identifier.uri http://dx.doi.org/10.15496/publikation-7815
dc.description.abstract The issues of developing an online EEG-based adaptive learning environment are examined in this thesis. The aim is to adapt instructional learning material in real-time, to support learners in their individual learning process and keep them in their optimal workload capacity range during learning. First, suitable learning material is designed, which does not cause artifacts and induces confounds in the EEG data. Second, the most suitable features for an online workload detection in EEG data are determined, by using a variety of pre-processing and feature selection methods, as connectivity and independent component analysis. Third, generalizable classification methods like cross-task classification and cross-subject regression are developed, to enable a workload prediction across a variety of tasks, independently from subjects. In an offline analysis, the cross-subject regression leads to a higher workload prediction accuracy as the cross-task classification. Since the workload prediction across subjects is more precise, this method is used for the subsequent online study. Therefore, the achieved findings and developed classification methods will finally be applied in an online study. The difficulty level of the presented learning material is adapted in real-time, dependent on the predicted workload of each subject. Furthermore, the applicability and efficiency of an online EEG-based adaptive learning environment is investigated and assessed. Comparing the EEG-based learning environment with an error-adaptive learning system, which is state of the art, the induced learning effects are similar. Thus, the learners can successfully be supported in their individual learning process using an EEG-based adaptation of the learning material, by keeping them in their optimal workload range for learning. en
dc.language.iso en de_DE
dc.publisher Universität Tübingen de_DE
dc.rights ubt-podno de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_ohne_pod.php?la=de de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_ohne_pod.php?la=en en
dc.subject.classification Elektroencephalogramm , Gehirn-Computer-Schnittstelle , Maschinelles Lernen de_DE
dc.subject.ddc 004 de_DE
dc.subject.other passive brain-computer interface en
dc.subject.other cognitive workload en
dc.subject.other EEG de_DE
dc.subject.other cognitive load theory en
dc.subject.other cross-task classification en
dc.subject.other cross-subject regression en
dc.subject.other adaptive learning environments en
dc.title EEG workload prediction in a closed-loop learning environment en
dc.type Dissertation de_DE
dcterms.dateAccepted 2015-10-16
utue.publikation.fachbereich Informatik de_DE
utue.publikation.fakultaet 7 Mathematisch-Naturwissenschaftliche Fakultät de_DE
utue.publikation.fakultaet 7 Mathematisch-Naturwissenschaftliche Fakultät de_DE

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