Real-time inference of word relevance from electroencephalogram and eye gaze

dc.contributor.authorWenzel, Markus A.
dc.contributor.authorBogojeski, Mihail
dc.contributor.authorBlankertz, Benjamin
dc.date.accessioned2019-12-11T16:30:29Z
dc.date.available2019-12-11T16:30:29Z
dc.date.issued2017-08-16
dc.description.abstractObjective. Brain-computer interfaces can potentially map the subjective relevance of the visual surroundings, based on neural activity and eye movements, in order to infer the interest of a person in real-time. Approach. Readers looked for words belonging to one out of five semantic categories, while a stream of words passed at different locations on the screen. It was estimated in real-time which words and thus which semantic category interested each reader based on the electroencephalogram (EEG) and the eye gaze. Main results. Words that were subjectively relevant could be decoded online from the signals. The estimation resulted in an average rank of 1.62 for the category of interest among the five categories after a hundred words had been read. Significance. It was demonstrated that the interest of a reader can be inferred online from EEG and eye tracking signals, which can potentially be used in novel types of adaptive software, which enrich the interaction by adding implicit information about the interest of the user to the explicit interaction. The study is characterised by the following novelties. Interpretation with respect to the word meaning was necessary in contrast to the usual practice in brain-computer interfacing where stimulus recognition is sufficient. The typical counting task was avoided because it would not be sensible for implicit relevance detection. Several words were displayed at the same time, in contrast to the typical sequences of single stimuli. Neural activity was related with eye tracking to the words, which were scanned without restrictions on the eye movements.en
dc.description.sponsorshipEC/FP7/611570/EU/Symbiotic Mind Computer Interaction for Information Seeking/MindSeeen
dc.identifier.eissn1741-2552
dc.identifier.issn1741-2560
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/10482
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9434
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/en
dc.subject.ddc150 Psychologiede
dc.subject.ddc610 Medizin und Gesundheitde
dc.subject.otherbrain-computer interfacingen
dc.subject.otherelectroencephalographyen
dc.subject.othereye movementsen
dc.subject.otherreadingen
dc.subject.otherrelevance detectionen
dc.subject.othersemanticsen
dc.subject.otherunrestricted viewingen
dc.titleReal-time inference of word relevance from electroencephalogram and eye gazeen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber056007en
dcterms.bibliographicCitation.doi10.1088/1741-2552/aa7590en
dcterms.bibliographicCitation.issue5en
dcterms.bibliographicCitation.journaltitleJournal of Neural Engineeringen
dcterms.bibliographicCitation.originalpublishernameIOP Publishingen
dcterms.bibliographicCitation.originalpublisherplaceBristolen
dcterms.bibliographicCitation.volume14en
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik>Inst. Softwaretechnik und Theoretische Informatik>FG Neurotechnologiede
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Neurotechnologiede
tub.affiliation.instituteInst. Softwaretechnik und Theoretische Informatikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
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