Scheduling Neural Sensors to Estimate Brain Activity
| Abstract | Research on developing new algorithms to improve information on brain functionality and structure is ongoing. Studying neural activity through dipole source localization with electroencephalography (EEG) and magnetoencephalography (MEG) sensor measurements can lead to diagnosis and treatment of a brain disorder and can also identify the area of the brain from where the disorder has originated. Designing advanced localization algorithms that can adapt to environmental changes is considered a significant shift from manual diagnosis which is based on the knowledge and observation of the doctor, to an adaptive and improved brain disorder diagnosis as these algorithms can track activities that might not be noticed by the human eye. An important ... (more) |
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| Created Date | 2012 |
| Contributor | Michael, Stefanos (Author) / Papandreou-Suppappola, Antonia (Advisor) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher) |
| Subject | Biomedical engineering / Electrical engineering / biomedical signal processing / Dipole Source estimation / neural source estimation / particle filter / sensor scheduling / Wearable EEG |
| Type | Masters Thesis |
| Extent | 55 pages |
| Language | English |
| Copyright |
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| Reuse Permissions | All Rights Reserved |
| Note | M.S. Electrical Engineering 2012 |
| Collaborating Institutions | Graduate College / ASU Library |
| Additional Formats | MODS / OAI Dublin Core / RIS |
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