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Pattern detection in Brain Signals from Simple Visual Stimuli
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

With the advent of deep learning algorithms, the possibilities to analyse great amounts of data has been unlocked. As EEG-readers become both more affordable and of higher quality it enables interesting applications. In this thesis a convolutional neural network (CNN) is developed and implemented to evaluate the possibility to detect patterns in brain signals gathered from perceiving simple visual stimuli. Historically, brain signals are filtered into brain waves. These are cruder representations of brain signals than the raw data collected from the electrodes in the EEG-device. The software developed in this thesis enables the collection and structuring of raw EEG data such as it can be analysed by CNNs that classify images. The results show some emerging patterns, in contrast, to a completely noisy set of data and points towards the possibility for higher performance through further experimentation with data collection and modifications of the CNN. The deep learning pattern detection was implemented using the keras python library with tensorflow backend and open source software from openBCI. Data was collected from 14 participants using the 16-channel device (Cyton board + Daisy) in order to provide a proof of concept.

Place, publisher, year, edition, pages
2019. , p. 36
Series
IT ; 19031
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-396823OAI: oai:DiVA.org:uu-396823DiVA, id: diva2:1369195
Educational program
Master Programme in Human-Computer Interaction
Supervisors
Examiners
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-11Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
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  • de-DE
  • en-GB
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Output format
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