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UID:calendar.361931.field_event_date.0@www.wright.edu
DTSTAMP:20260220T000957Z
CREATED:20190730T134720Z
DESCRIPTION:Ph.D. Committee:  Drs. Nikolaos Bourbakis\, Advisor\, Soon Chun
 g\, Bin Wang\, and Kostas Michalopoulos (Convergent Engineering\, Inc.)ABS
 TRACTHuman brain analysis and understanding pose several challenges due to
  the great complexity of the structural organization and the functional co
 nnectivity that characterizes the human brain. The ability of the brain to
  adapt in dynamic changes over time such as normal aging\, neurodegenerati
 ve diseases or congenital brain malformations renders the brain’s explorat
 ion a particularly demanding and difficult task.In recent years\, advances
  in brain imaging modalities and lately the multimodal fusion\, combined w
 ith improvements in related technologies have greatly assisted the develop
 ment of brain maps by providing insights regarding the overall brain struc
 ture and functionality. Even though the existence of sensory and motor map
 s for the human brain is known to some degree\, the formation process is s
 till subject to research.Electroencephalography (EEG) and functional magne
 tic resonance imaging (fMRI) are the two mostly used non-invasive brain im
 aging modalities that can track the changes of brain activity. Due to thei
 r complimentary nature\, high temporal resolution from EEG and high spatia
 l resolution from fMRI\, the fusion of simultaneous acquired EEG and fMRI 
 recordings aims to provide complementary information about the brain funct
 ionality. In an effort to extend the current research in the field of brai
 n understanding\, a novel Brain Mapping Model (BMM) based on EEG and fMRI 
 is proposed within this Ph.D. dissertation. The proposed BMM is based on t
 he synergy of state-of-the-art computational techniques to associate the b
 rain regional activities provided by the EEG-fMRI fusion.In more details\,
  first\, a novel formal model for the EEG signals representation is propos
 ed. The proposed formal model enables the analysis and extraction of struc
 tural EEG features. The proposed method is based on the Syntactic Aggregat
 e approXimation (SAX) algorithm\, that in this work is improved by the Loc
 al-Global (LG) graph technique\, to compose a Context Free-Grammar (CFG). 
 Moreover\, by modeling the EEG recordings with Stochastic Petri nets (SPNs
 ) we are able to combine the EEG channels’ spatiotemporal dependencies. Se
 cond\, two different EEG-fMRI fusion approaches are assessed in order to r
 eveal the enhanced brain spatiotemporal resolution offered by the combinat
 ion of the two modalities.The overarching goal of this BMM is to contribut
 e to the further exploration and better understanding of the brain activit
 ies formulation with a future goal to be used in navigation applications f
 or the visually impaired population.
DTSTART;TZID=America/New_York:20190801T140000
DTEND;TZID=America/New_York:20190801T160000
LAST-MODIFIED:20190730T144736Z
LOCATION:467 Joshi
SUMMARY:Ph.D. Dissertation Defense “A Novel Methodology for Timely Brain Fo
 rmations of 3D Spatial Information with Application to Visually Impaired N
 avigation” By Spyridon Manganas
URL;TYPE=URI:/events/phd-dissertation-defense-%E2%80%
 9C-novel-methodology-timely-brain-formations-3d-spatial-information
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