<div class="csl-bib-body">
<div class="csl-entry">Adelberger, P. (2019). <i>An EEG- and ERP-based image ranking application</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2019.63126</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2019.63126
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/2033
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dc.description.abstract
An electroencephalogram (EEG) is a measurement to record the electrical potentials in the brain, also referred to as brain activity. For some time now, researchers use this EEG signal to create a brain-computer interfaces (BCI), which allows users to manipulate the computer just with their thoughts. This application ranks images of a picture set via their elicit responses in the EEG signal. The goal of this thesis is to check the functionality of the image ranking application with 2 different BCI devices and optimize the application run for image ranking. The first part includes criteria about signal processing, e.g. sample rate. Here, the timing in which the EEG signal is sampled and sent to the application is analysed. After, the reliability of the recorded EEG signal the interstimulus interval (ISI) can be optimized. The ISI consist of three parameters: the time an image is displayed on the screen, the time between two images and the number of times each image (flashes) has to be shown. This three parameters have to be tuned in a way that the accuracy is increased and the time for one application run is decreased. Additionally, a ranking with different subjects should be created to depict if certain images are always ranked in the first few positions and are independent of the subject. The results show that the interaction between application and the two BCI devices work as expected, short of some minor issues. Thus, the applicability of the application was given and the tuning of the ISI parameters could be started. For the ISI the best accuracy was achieved when the images were displayed on the screen for 100 ms and 75 ms was used between two images. Furthermore, each image was flashed 20 or 5 times for the classifier or ranking, respectively. For the group rankings, the results indicated, that images of faces rank in average higher than the rest. Finally, the median of all rankings for one image should be used as the ranking parameter.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
EEG
en
dc.subject
BCI
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dc.subject
Event-Related Potential
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dc.subject
P300
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dc.subject
Interstimulus Interval
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dc.title
An EEG- and ERP-based image ranking application
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dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2019.63126
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Patrick Adelberger
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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tuw.publication.orgunit
E193 - Institut für Visual Computing and Human-Centered Technology