Understanding Human Implicit Intention based on Electroencephalography (EEG) = EEG에 기반한 묵시적 의도 파악 연구

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There are many approaches that understand human intention. Although previous study has mainly used behavioral data which are explicitly expressed such as speech, gesture, and touch strokes, but human intention cannot be revealed explicitly in real life. Whether it is intended or by accident, there are many situations that we do not disclose our minds. In this study, EEG was examined to investigate the human implicit intention. Subjects showed their intention by their voices whether they agree or disagree toward given obvious and non-obvious sentences. Experiment focuses on some situation that human may not want to express their real intention to others and may answer differently. It is assumed that brain activation may not be the same when they show differently with their real intention, so it can be a measurement of the implicit intention. ICA is applied to extract independent components of the recorded EEG data. And only few components were selected based on Fisher Linear Discriminant (FLD) which can discriminate between agreement and disagreement state. Using selected components, support vector machine trained with obvious condition identified the validation sample from the classifier output. The results showed that SVM output of selected independent components can discriminate implicit intention states, and recognize non-obvious condition. It may be used to understand implicit intention.
Advisors
Lee, Soo-Youngresearcher이수영
Description
한국과학기술원 : 전기 및 전자공학과,
Publisher
한국과학기술원
Issue Date
2011
Identifier
482822/325007  / 020103223
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기 및 전자공학과, 2011.8, [ v, 50 p. ]

Keywords

묵시적 의도; 뇌파; 독립 요소 분석; Electroencephalography; support vector machine; Independent component analysis; fisher linear discriminant; Implicit intention

URI
http://hdl.handle.net/10203/180924
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=482822&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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