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Synchronization Analysis of EEG-Based Brain Functional Network

Alamfard, Vahid | 2017

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 49501 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Manzuri, Mohammad Taghi
  7. Abstract:
  8. It is believed that the synchronized activity of different brain areas, is the main cause of information binding inside the brain. Tis is definitely one of the most exciting challenges in modelling modern complex systems. Brain disorders such as schizophrenia,Alzheimer’s disease, epilepsy, autism and Parkinson’s disease are associated with abnormal synchronization abilities of neural networks. Functional connections can be assessed indirectly by measuring the electrophysiological criteria of ynchronization.Traditionally, in the study of neurophysiological, synchronizations are assessed by analyzing the coherence of frequency-domain characteristics of time series in standard methods for recording neural activities such as EEG and MEG techniques. Recently,some other time domain methods for the analysis of synchronization among bivariate time series are also developed. Tese bivariate methods are unable to capture all the information carried within a multivariate signals such as high density EEG. Synchronization of collaborative neural communities is studied here as a dynamic complex network of dynamic high dimensional subsystems. In analysis of functional networks, number of different criteria are introduced for estimating the degree of synchronization in multivariate time series. Also a method for inferring causal connection, between two multivariate time series is introduced. Obviously, the results are not identical for introduced methods. It was expected so, as these methods measure different procedures related to synchronization behavior. However, the aim of synchronization study is performing comparisons in different applications. Te results of applying these methods on EEG data for 34 people (including 17 healthy and 17 Alzheimer’s patient) indicated that the degrees of synchronization assessed from both methods for constructing whole-head topography of local synchronization are correlated to a large extent
  9. Keywords:
  10. Brain Functional Network ; Electroencphalogram Signal ; Alzheimer ; Synchronisation ; Synchronization Analysis

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