Classification of EEG recordings without perfect time-locking
| dc.contributor.advisor | Huang, Yufei | |
| dc.contributor.author | Zhu, Manli | |
| dc.contributor.committeeMember | Zhang, Jianqiu | |
| dc.contributor.committeeMember | Jin, Yufang | |
| dc.date.accessioned | 2024-03-08T17:41:04Z | |
| dc.date.available | 2024-03-08T17:41:04Z | |
| dc.date.issued | 2012 | |
| dc.description | This item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID. | |
| dc.description.abstract | It has been established that neural response is time-locked to stimulus; however, the latencyin between may vary because of stimulus strength, subject fatigue, distraction, etc. Instead of assuming perfect time-locking between stimulus and its neural response, we proposed here a statistical model that admits latency variation. We tested the approach on an EEG data set from an image Rapid Serial Visual Presentation (RSVP) experiment. Results show that the proposed approach consistently outperforms those relying on perfect time-locking. In addition, our approach can predict the stimulus' onset time when this information is not available. | |
| dc.description.department | Electrical and Computer Engineering | |
| dc.format.extent | 29 pages | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.isbn | 9781267616296 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12588/6226 | |
| dc.language | en | |
| dc.subject | discriminate | |
| dc.subject | EEG | |
| dc.subject | Gaussian distribution | |
| dc.subject | neural response | |
| dc.subject | stimulus | |
| dc.subject | time-locking | |
| dc.subject.classification | Electrical engineering | |
| dc.subject.classification | Computer engineering | |
| dc.title | Classification of EEG recordings without perfect time-locking | |
| dc.type | Thesis | |
| dc.type.dcmi | Text | |
| dcterms.accessRights | pq_closed | |
| thesis.degree.department | Electrical and Computer Engineering | |
| thesis.degree.grantor | University of Texas at San Antonio | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science |
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