Please use this identifier to cite or link to this item: https://knowledgecommons.lakeheadu.ca/handle/2453/5255
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dc.contributor.advisorFadlullah, Zubair-
dc.contributor.advisorFouda, Mostafa-
dc.contributor.authorElshafei, Mohamed-
dc.date.accessioned2023-10-11T18:40:37Z-
dc.date.available2023-10-11T18:40:37Z-
dc.date.created2023-
dc.date.issued2023-
dc.identifier.urihttps://knowledgecommons.lakeheadu.ca/handle/2453/5255-
dc.description.abstractThe human brain, a marvel of nature, consists of intricate neural networks that have fascinated and perplexed the scientific community for generations. As scientists and researchers globally endeavour to unravel the mysteries of bioelectrical activities that form the basis of our cognitive functions and experiences, our research emerges at the nexus of biology and cutting-edge technology. Specifically, we spotlight the remarkable capabilities of magnetoencephalography (MEG) and electroencephalography (EEG). These potent neuroimaging tools, celebrated for their unparalleled spatial and temporal precision, are synergistically combined in our study. We aim to map MEG innovatively signals onto their EEG replicas, employing avant-garde spintronic devices, with a particular emphasis on Magnetic Tunnel Junctions (MTJ). [...]en_US
dc.language.isoen_USen_US
dc.subjectMagnetoencephalographyen_US
dc.subjectElectroencephalographyen_US
dc.subjectMagnetic tunnel junctionsen_US
dc.subjectBiLSTM modelen_US
dc.subjectNeuroimaging devicesen_US
dc.subjectArtificial intelligence (MEG/EEG mapping)en_US
dc.titleNeural synergy in compact biomedical IoT devices: spintronic pathways for MEG-EEG integration and portabilityen_US
dc.typeThesisen_US
etd.degree.nameMaster of Scienceen_US
etd.degree.levelMasteren_US
etd.degree.disciplineComputer Scienceen_US
etd.degree.grantorLakehead Universityen_US
Appears in Collections:Electronic Theses and Dissertations from 2009

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