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dc.contributor.advisorManian, Vidya
dc.contributor.authorAceros-Moreno, Cesar A.
dc.date.accessioned2019-06-04T13:45:57Z
dc.date.available2019-06-04T13:45:57Z
dc.date.issued2019-05-15
dc.identifier.urihttps://hdl.handle.net/20.500.11801/2459
dc.description.abstractTime-frequency methods (TFM) increase the dimensionality of signal spaces. In this thesis, a computational signal processing framework using tensor products algebra is developed to map Electroencephalographic (EEG) signals to time-frequency space, extract robust features and classify them. EEG signal acquisition is a modality to record brain signals using electrodes placed on the scalp of a subject. The signals are recorded with 32 electrodes while the subject listens to an auditory stimulus such as repeated tones, vowels, or words. The TFM used are cyclic short time Fourier transform (CSTFT) and continuous wavelet transform (CWT). The framework takes as input the raw EEG signals. The time-frequency sparse representations using the STFT and CWT are computed on the noise free channels at di erent time steps. The source localization using event related potentials (ERP) can be compared with time-frequency based localization. Results of multichannel EEG nonnegative matrix factorization (NMF) and reconstruction is also presented. The algorithms are implemented as part of a multichannel signal analysis toolbox (MSAT) using the Python programming language. The algorithms presented in this thesis can be extended to more general algorithms for analyzing and classifying the sources of multichannel biosensory signals.en_US
dc.language.isoenen_US
dc.subjectTime-frequency Methodsen_US
dc.subjectElectroencephalographyen_US
dc.subjectCyclic Short Time Fourier Transformen_US
dc.subjectContinuous Wavelet Transformen_US
dc.subjectComputational Signal Processing Frameworken_US
dc.subject.lcshSignal processing -- Mathematicsen_US
dc.subject.lcshTensor productsen_US
dc.subject.lcshElectroencephalographyen_US
dc.subject.lcshFourier transformationen_US
dc.subject.lcshWavelets (Mathematics)en_US
dc.subject.lcshAlgorithmsen_US
dc.titleTensor products algebra applied to time frequency analysis of auditory encephalographic signalen_US
dc.typeDissertationen_US
dc.rights.licenseAll rights reserved
dc.rights.holder(c) 2019 Cesar A. Aceros Morenoen_US
dc.contributor.committeeRodriguez, Domingo
dc.contributor.committeeRodriguez, Nestor
dc.contributor.committeeVega-Riveros, Jose Fernando
dc.contributor.representativeBaiges-Valentin, Ivan
thesis.degree.levelPh.D.en_US
thesis.degree.disciplineElectrical Engineeringen_US
dc.contributor.collegeCollege of Engineeringen_US
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.description.graduationSemesterSpringen_US
dc.description.graduationYear2019en_US


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