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OAP: An efficient online principal component analysis algorithm for streaming EEG data
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Data processing on streaming data poses computational as well as statistical challenges. Streaming data requires that data processing algorithms are able to process a new data point within micro-seconds. This is especially challenging on dimension reduction, where traditional methods as Principal Component Analysis (PCA) require eigenvectors decomposition of a matrix based on the complete dataset. So a proper online version of PCA should avoid this computational involved step in favor for a more efficient update rule. This is implemented by an algorithm named Online Angle Preservation (OAP), which is able to handle large dimensions in the required time limitations. This project presents an application of OAP in the case of Electroencephalography (EEG). For this, an interface was coded from an openBCI EEG device, through a Java API to a streaming environment called Stream Analyzer (sa.engine). The performance of this solution was compared to a standard Windowised PCA solution, indicating its competitive performance. This report details this setup and details the results.

Place, publisher, year, edition, pages
2018. , p. 43
Series
IT ; 18061
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-392403OAI: oai:DiVA.org:uu-392403DiVA, id: diva2:1348141
Educational program
Master Programme in Computer Science
Supervisors
Examiners
Available from: 2019-09-03 Created: 2019-09-03 Last updated: 2019-09-03Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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