Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/61628
Title: Machine learning methods to analyze subliminal priming ERP
Authors: Wu, Zuobin
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2013
Abstract: Priming 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.
URI: http://hdl.handle.net/10356/61628
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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