A machine learning approach to taking EEG-based brain-computer interfaces out of the lab

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dc.contributor.advisor Grosse-Wentrup, Moritz (Prof. Dr.)
dc.contributor.author Jayaram, Vinay
dc.date.accessioned 2018-12-04T08:45:12Z
dc.date.available 2018-12-04T08:45:12Z
dc.date.issued 2018-12-04
dc.identifier.other 514795301 de_DE
dc.identifier.uri http://hdl.handle.net/10900/85130
dc.identifier.uri http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-851307 de_DE
dc.identifier.uri http://dx.doi.org/10.15496/publikation-26520
dc.description.abstract Despite being a subject of study for almost three decades, non-invasive brain- computer interfaces (BCIs) are still trapped in the laboratory. In order to move into more common use, it is necessary to have systems that can be reliably used over time with a minimum of retraining. My research focuses on machine learning methods to minimize necessary retraining, as well as a data science approach to validate processing pipelines more robustly. Via a probabilistic transfer learning method that scales well to large amounts of data in high dimensions it is possible to reduce the amount of calibration data needed for optimal performance. However, a good model still requires reliable features that are resistant to recording artifacts. To this end we have also investigated a novel feature of the electroencephalogram which is predictive of multiple types of brain-related activity. As cognitive neuroscience literature suggests, shifts in the peak frequency of a neural oscillation – hereafter referred to as frequency modulation – can be predictive of activity in standard BCI tasks, which we validate for the first time in multiple paradigms. Finally, in order to test the robustness of our techniques, we have built a codebase for reliable comparison of pipelines across over fifteen open access EEG datasets. en
dc.language.iso en de_DE
dc.publisher Universität Tübingen de_DE
dc.rights ubt-podok de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_mit_pod.php?la=de de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_mit_pod.php?la=en en
dc.subject.classification Elektroencephalogramm , Elektroencephalographie , Gehirn-Computer-Schnittstelle , Signalverarbeitung de_DE
dc.subject.ddc 500 de_DE
dc.title A machine learning approach to taking EEG-based brain-computer interfaces out of the lab en
dc.type PhDThesis de_DE
dcterms.dateAccepted 2018-11-15
utue.publikation.fachbereich Biologie de_DE
utue.publikation.fakultaet 7 Mathematisch-Naturwissenschaftliche Fakultät de_DE


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