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https://elibrary.tucl.edu.np/handle/123456789/7294Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Bista, Saroj | - |
| dc.date.accessioned | 2022-01-12T06:23:45Z | - |
| dc.date.available | 2022-01-12T06:23:45Z | - |
| dc.date.issued | 2017-11 | - |
| dc.identifier.uri | https://elibrary.tucl.edu.np/handle/123456789/7294 | - |
| dc.description | This thesis work presents a cost effective method to record brain wave signals using three channel active electrode EEG device and classify brain waves related to motor imagery(MI) left and right hand movement, based on electroencephalography (EEG) measured from the central lobe ,that could be used for the Brain Computer Interface (BCI) systems. | en_US |
| dc.description.abstract | Thisthesisworkpresentsacosteffectivemethodtorecordbrainwavesignalsusing threechannelactiveelectrodeEEGdeviceandclassifybrainwavesrelatedtomotor imagery(MI)leftandrighthandmovement,basedonelectroencephalography(EEG) measuredfromthecentrallobe,thatcouldbeusedfortheBrainComputerInterface (BCI)systems.ThegoalofthisthesisistouseIndependentComponentAnalysis (ICA)fortheremovalofEEGartifacts,andthenextractthebrainwavesfeaturesforMI lefthandandMIrighthandmovementusingWaveletDecomposition(WD).The‘Mor- let’motherwaveletisusedforwaveletdecompositionasitshowsbetterperformance foranalysisofnon-stationarybiomedicalsignalslikeEEG.Thebrainwavefeatureslike MaximumPoweramongalldecompositionlevel(MMP),Frequencycorrespondingto MMP(MAF),andMaximumAmplitudeofthesignalwithMAF(MMA)ischosen astheclassificationfeaturesfortheclassificationofMIbrainwaves.Theclassifica-tionofMIbrainwavesignalsisdoneusingLinearDiscriminantAnalysis(LDA)which showedtheaccuracyof81.6%.Thus,thedesignedthreechannelactiveelectrodeEEG deviceusedshowedgoodperformanceforrecordingEEGsignals.Furthermore,signal preprocessingalgorithmICA,featureextractionmethodWaveletDecomposition,and classificationmethodLDAshowedgoodperformancefortheclassificationofMIleft handandMIrighthandactivities. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Pulchowk Campus | en_US |
| dc.subject | LDA | en_US |
| dc.subject | Morlet | en_US |
| dc.subject | Wavelet Decomposition | en_US |
| dc.subject | ICA | en_US |
| dc.title | A 3-channel Active Electrode EEG Device for the Classification of MotorImagery Brain waves for Brain Computer Interface | en_US |
| dc.type | Thesis | en_US |
| local.institute.title | Institute of Engineering | en_US |
| local.academic.level | Masters | en_US |
| local.affiliatedinstitute.title | Pulchowk Campus | en_US |
| Appears in Collections: | Electronics and Computer Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Saroj Bista.pdf | 2.78 MB | Adobe PDF | View/Open |
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