Extraction of User Preference for Video Stimuli Using EEG-Based User Responses

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Owing to the large number of video programs available, a method for accessing preferred videos efficiently through personalized video summaries and clips is needed. The automatic recognition of user states when viewing a video is essential for extracting meaningful video segments. Although there have been many studies on emotion recognition using various user responses, electroencephalogram (EEG)-based research on preference recognition of videos is at its very early stages. This paper proposes classification models based on linear and nonlinear classifiers using EEG features of band power (BP) values and asymmetry scores for four preference classes. As a result, the quadratic-discriminant-analysis-based model using BP features achieves a classification accuracy of 97.39% (+/- 0.73%), and the models based on the other nonlinear classifiers using the BP features achieve an accuracy of over 96%, which is superior to that of previous work only for binary preference classification. The result proves that the proposed approach is sufficient for employment in personalized video segmentation with high accuracy and classification power.
Publisher
ELECTRONICS TELECOMMUNICATIONS RESEARCH INST
Issue Date
2013-12
Language
English
Article Type
Article
Keywords

EMOTION RECOGNITION; INFORMATION; ASYMMETRY; PARADIGM; GAMMA

Citation

ETRI JOURNAL, v.35, no.6, pp.1105 - 1114

ISSN
1225-6463
DOI
10.4218/etrij.13.0113.0194
URI
http://hdl.handle.net/10203/188824
Appears in Collection
IE-Journal Papers(저널논문)
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