Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/345672
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dc.coverage.spatialMachine learning algorithms for eye state prediction and meditation analysis in eeg signals
dc.date.accessioned2021-10-26T06:15:49Z-
dc.date.available2021-10-26T06:15:49Z-
dc.identifier.urihttp://hdl.handle.net/10603/345672-
dc.description.abstractElectroencephalography (EEG) is a measure of mind action by wave examination; it comprises number of nodes that are used for clinical purposes. There is a high need of deeper learning of the dynamics of brain that are mainly caught in EEG is the need of the medical society for an authentic and long term progression of psychophysiological and many cognitive studies. The available literatures are minimum on the studies of EEG based on their structure.It is well known that there are many differences in EEG signal between the eyes-open and eyes-closed states. Signal processing plays a vital role of any latest and advanced application involving data acquisition and assessment of EEG signal in eyes-open and eyes-closed states. newline
dc.format.extentxviii,148p
dc.languageEnglish
dc.relationp.136-147
dc.rightsuniversity
dc.titleMachine learning algorithms for eye state prediction and meditation analysis in eeg signals
dc.title.alternative
dc.creator.researcherDevipriya, A
dc.subject.keywordEngineering and Technology
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Information Systems
dc.subject.keywordalgorithms
dc.subject.keywordmeditation analysis
dc.description.note
dc.contributor.guideNagarajan, N
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registeredn.d.
dc.date.completed2019
dc.date.awarded2019
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificates.pdf422.33 kBAdobe PDFView/Open
03_vivaproceedings.pdf772.03 kBAdobe PDFView/Open
04_bonafidecertificate.pdf348.08 kBAdobe PDFView/Open
05_abstracts.pdf170.43 kBAdobe PDFView/Open
06_acknowledgements.pdf365.97 kBAdobe PDFView/Open
07_contents.pdf190.74 kBAdobe PDFView/Open
08_listoftables.pdf107.14 kBAdobe PDFView/Open
09_listoffigures.pdf116.44 kBAdobe PDFView/Open
10_listofabbreviations.pdf991.66 kBAdobe PDFView/Open
11_chapter1.pdf1.06 MBAdobe PDFView/Open
12_chapter2.pdf989.01 kBAdobe PDFView/Open
13_chapter3.pdf280.27 kBAdobe PDFView/Open
14_chapter4.pdf1.23 MBAdobe PDFView/Open
15_chapter5.pdf1.39 MBAdobe PDFView/Open
16_chapter6.pdf176.01 kBAdobe PDFView/Open
17_conclusion.pdf152.08 kBAdobe PDFView/Open
18_references.pdf980.34 kBAdobe PDFView/Open
19_listofpublications.pdf186.16 kBAdobe PDFView/Open
80_recommendation.pdf58.42 kBAdobe PDFView/Open


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