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Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/13118
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dc.contributor.authorTantisatirapong S.
dc.contributor.authorDechwechprasit P.
dc.contributor.authorSenavongse W.
dc.contributor.authorPhothisonothai M.
dc.date.accessioned2021-04-05T03:22:22Z-
dc.date.available2021-04-05T03:22:22Z-
dc.date.issued2017
dc.identifier.other2-s2.0-85017509472
dc.identifier.urihttps://ir.swu.ac.th/jspui/handle/123456789/13118-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85017509472&doi=10.1109%2fKST.2017.7886130&partnerID=40&md5=a15201981184cb1f169cb1c0026de966
dc.description.abstractThe stimulus flickering at specific frequencies, or as known as steady-state visually evoked potential (SSVEP), can be recorded on an occipital area of the brain. SSVEP is used to interpret the EEG signal to detect the desired goal of the experiment. In this paper, we aim to investigate SSVE signal by means of magnitude-squared coherence (MSC) analysis between the red and green visual stimuli. In the experimental paradigm, we considered two parameters that are chromatic color and flickering frequency where an epoch interval was 10 seconds. The obtained results showed the statistically significant frequency-domain response and its maximum MSC coefficient in the theta and alpha bands for green and red flickers, respectively. © 2017 IEEE.
dc.subjectBioelectric potentials
dc.subjectBrain computer interface
dc.subjectFlickering
dc.subjectFrequency domain analysis
dc.subjectInterface states
dc.subjectChromatic colors
dc.subjectCoherence analysis
dc.subjectElectro-encephalogram (EEG)
dc.subjectFrequency domain response
dc.subjectMagnitude squared coherences
dc.subjectSpecific frequencies
dc.subjectSteady state visually evoked potentials
dc.subjectVisual stimulus
dc.subjectElectroencephalography
dc.titleTime-frequency based coherence analysis of red and green flickering visual stimuli for EEG-controlled applications
dc.typeConference Paper
dc.rights.holderScopus
dc.identifier.bibliograpycitation2017 9th International Conference on Knowledge and Smart Technology: Crunching Information of Everything, KST 2017. (2017), p.279-283
dc.identifier.doi10.1109/KST.2017.7886130
Appears in Collections:Scopus 1983-2021

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