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Analysis of Functional Brain Connectivity Using EEG Signals for Classification of Brain States

Ghodsi, Saeed | 2018

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 51020 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Karbalai Aghajan, Hamid; Mohamadzadeh, Hoda
  7. Abstract:
  8. Different perceptual, cognitive, and emotional situations results in a kind of information flow in the brain by means of coordinated neuronal oscillations. Analysing these oscillations, especially synchronizations of different brain regions, can illustrate the brain response to the aforementioned situations. In the literature, connectivity between brain regions is divided into the three groups of structural, effective, and functional, s.t. the first one referes to the connectivity between nearby regions, while the second and third ones focus on the synchronization of oscillations of arbitrary located regions. Although EEG is not the best choice for analyzing functional connectivity between brain regions due to its relatively poor spatial resolution, extracting its statistical features may be helpful in the analysis of synchronization of brain oscillations. In this project, different measures for the synchronization of EEG signals are analyzed and a novel framework is proposed for the classification of brain states using some tools from statistics and machine learning fields. More specifically, in this project we first aim to predict the occurrence of seizures in epileptic patients using the proposed method and then try to apply this method on the problem of human emotion recognition
  9. Keywords:
  10. Machine Learning ; Functional Connectivity ; Seizure Detection ; Electroencephalography ; Computational Neuro Science ; Seizure Prediction ; Human Emotion Recognition

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