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Temporal Analysis of Functional Brain Connectivity Using EEG Signals

Khazaei, Ensieh | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53286 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Mohammadzadeh, Narges Hoda
  7. Abstract:
  8. Human has different emotions such as happiness, sadness, anger, etc. Recognizing these emotions plays an important role in human-machine interface. Emotion recognition can be divided into approaches, physiological and non-physiological signals. Non-physiological signals include facial expressions, body gesture, and voice, and physiological signals include electroencephalograph (EEG), electrocardiograph (ECG), and functional magnetic resonance imaging (fMRI). EEG signal has been absorbed a lot of attention in emotion recognition because recording of EEG signal is easy and it is non-invasive. Analysis of connectivity and interaction between different areas of the brain can provide useful information about the brain's response to different emotional states. Connectivity between different areas of the brain is divided into three categories: structural connectivity, functional connectivity, and effective connectivity. Structural connectivity describe connectome between neighboring neurons in spatially distant brain regions, and functional and effective connectivities indicate temporal dependency between activities of different areas which are not necessarily adjacent. In this regard, the goal of this study is temporal analysis of functional connectivity in emotion recognition. So in the first, appropriate visual, auditory and visual-auditory stimuli are created for each person, and then their brain signals are recorded. After recording the brain signals, by processing the signals and extracting the appropriate features, the individual's different emotions are classified and temporal changes of functional connectivity is examined. The results of this study show that during the stimulation, some intervals perform much better than the whole signal in classification of people's emotions. Keywords: Electroencephalogram Signal, Functional connectivity, Emotion recognition, Machine learning
  9. Keywords:
  10. Emotion Recognition ; Functional Connectivity ; Electroencphalogram Signal ; Machine Learning ; Electroencephalogram Signals Classification ; Functional Magnetic Resonance Imaging (FMRI)

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