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Multiple Nueral Artifacts Suppression Using Gaussian Mixture Modeling and Probability Hypothesis Density Filtering

Abstract Neural activity tracking using electroencephalography (EEG) and magnetoencephalography (MEG) brain scanning methods has been widely used in the field of neuroscience to provide insight into the nervous system. However, the tracking accuracy depends on the presence of artifacts in the EEG/MEG recordings. Artifacts include any signals that do not originate from neural activity, including physiological artifacts such as eye movement and non-physiological activity caused by the environment.

This work proposes an integrated method for simultaneously tracking multiple neural sources using the probability hypothesis density particle filter (PPHDF) and reducing the effect of artifacts using feature extraction and stochastic modeling. Unique time-f... (more)
Created Date 2014
Contributor Jiang, Jiewei (Author) / Papandreou-Suppappola, Antonia (Advisor) / Bliss, Daniel (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Artifacts Suppression / Feature extraction / Multiple target tracking / Neural activity / Probability hypothesis density filter
Type Masters Thesis
Extent 72 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Electrical Engineering 2014
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis