An Automatic System for Characterization and Detection of Ocular Noise
dc.contributor.author | Melville, Alexander | |
dc.contributor.advisor | Dehzangi, Omid | |
dc.date.accessioned | 2017-04-26T19:18:45Z | |
dc.date.available | NO_RESTRICTION | en_US |
dc.date.available | 2017-04-26T19:18:45Z | |
dc.date.issued | 2017-04-30 | |
dc.date.submitted | 2017-04-07 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/136624 | |
dc.description.abstract | Eye blinks cause high amplitude noise in electroencephalograms (EEGs), the noise from these blinks causes interference in several very important frequency bands. The method detailed in this paper uses independent component analysis and a diversified feature space to identify and filter out eye blink noise during wearable electroencephalographic tests. Prior work used autoregressive modeling in the time domain to identify blink segments in the recorded data. While the previous autoregressive method showed high accuracy in short trials, the goal of this work is to create a more advanced system capable of filtering blink noise in long, continuous trials. One of the major applications for this system is improving the quality of data collected during workload assessment tasks. Trials that consider the subject’s workload over time involve sensitive calculations done over the long term, and blinking resides in frequency bands that are known to be useful in determining the subject’s csurrent workload. A blink in one of these bands could give a false positive result for workload, or it could confuse an algorithm during training. In smaller studies subjects have been told not to blink, or were told to keep their eyes closed, but for workload assessment tasks it’s usually not practical to tell the subject to not blink during a strenuous trial. Other methods have been introduced that involve electrooculogram (EOG) data; the proposed system only uses electrooculogram data for training purposes, after this channels can be removed, so that wearable system scan reduce the amount of data recorded per second. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Data Science | en_US |
dc.subject | Signal Processing | en_US |
dc.subject | Wearables | en_US |
dc.subject | Data Mining | en_US |
dc.subject | Biomedical | en_US |
dc.subject | EEG | en_US |
dc.subject.other | Computer Science | en_US |
dc.title | An Automatic System for Characterization and Detection of Ocular Noise | en_US |
dc.type | Thesis | en_US |
dc.description.thesisdegreename | Master of Science in Engineering (MSE) | en_US |
dc.description.thesisdegreediscipline | Computer Science, College of Engineering and Computer Science | en_US |
dc.description.thesisdegreegrantor | University of Michigan-Dearborn | en_US |
dc.contributor.committeemember | Malik, Hafiz | |
dc.contributor.committeemember | Maxim, Bruce | |
dc.contributor.committeemember | Medjahed, Brahim | |
dc.contributor.committeemember | Ma, Di | |
dc.identifier.uniqname | 8143640 | en_US |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/136624/1/Thesis_04242017_1001.pdf | |
dc.identifier.orcid | 0000-0002-5000-3352 | en_US |
dc.description.filedescription | Description of Thesis_04242017_1001.pdf : Thesis | |
dc.identifier.name-orcid | Melville, Alexander; 0000-0002-5000-3352 | en_US |
dc.owningcollname | Dissertations and Theses (Ph.D. and Master's) |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.