Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/61628
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dc.contributor.authorWu, Zuobin
dc.date.accessioned2014-06-30T04:34:42Z
dc.date.available2014-06-30T04:34:42Z
dc.date.copyright2013en_US
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/10356/61628
dc.description.abstractPriming is an implicit memory effect which has effects on a person’s attitude and evaluation towards an image. Previous study of priming effect involves a lot of self-evaluation questionnaires. In this project, effects of subliminal priming were studied from the perspective of ERP. EEG data was recorded from forty subjects of positive, negative and neutral priming. A series of pre-processing steps including epoch extraction, re-referencing, independent component analysis and artifacts rejection were applied. The study focus on an early response difference which is between 0-100ms and a late ERP component which is between 300-500ms. Quantitative analysis of ERPs was performed. Shift-invariant multi-linear decomposition analysis was used to align ERP data. Comparison between normal averaged ERP and shift CP ERP was made throughout the study. To differentiate the three priming conditions, statistical analysis, feature selection and discriminant analysis using SVM were carried out based on processed ERPs.en_US
dc.format.extent129 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleMachine learning methods to analyze subliminal priming ERPen_US
dc.typeThesis
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Signal Processing)en_US
dc.contributor.supervisor2Justin Dauwelsen_US
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