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

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/M...

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Bibliographic Details
Other Authors: Jiang, Jiewei (Author)
Format: Dissertation
Language:English
Published: 2014
Subjects:
Electrical engineering
Artifacts Suppression
Feature extraction
Multiple target tracking
Neural activity
Probability hypothesis density filter
Online Access:http://hdl.handle.net/2286/R.I.25909

Internet

http://hdl.handle.net/2286/R.I.25909

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