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Evaluation Auditory Attention Using Eeg Signals when Performing Motion and Visual Tasks

Bagheri, Sara | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53947 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Hajipour, Sepideh
  7. Abstract:
  8. Attention is one of the important aspects of brain cognitive activities, which has been widely discussed in psychology and neuroscience and is one of the main fields of research in the education field. The human sense of hearing is very complex, impactful and crucial in many processes such as learning. Human body always does several tasks and uses different senses simultaneously. For example, a student who listens to his/her teacher in the class, at the same time pays attention to the teacher, looks at a text or image, and sometimes writes a note.Using the electroencephalogram (EEG) signal for attention assessment and other cognitive activities is considered because of its facile recording, non-invasiveness, and high temporal resolution. Analysis of connectivity and interaction between different areas of the brain can provide useful information about the brain's response to different states of a person. Connectivity between different areas of the brain is divided into three categories: structural connectivity, functional connectivity, and effective connectivity.In this research, we evaluate auditory attention when performing motor and visual tasks, by EEG signal, eye tracker, and motion capture data glove, and assess the effect of different linguistic inaccuracies and anomalies on a person's performance and attention. In each case, we calculate the energy of EEG signals in different areas of the brain while the writings and the audio version of the writings would not match completely and compared it to when they would. We also used functional connectivity to find interactions between different areas of the brain, and then, used graph theory to calculate features of the brain network topology in each case. Finally, we analyzed eye tracker and glove data to assess how the eyes and the writings would function in case of linguistic anomalies
  9. Keywords:
  10. Electroencphalogram Signal ; Eye Tracking ; Graph Theory ; Functional Connectivity ; Motion Capture ; Auditory Attention ; Motion Capture Data Glove

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