2026-09-19T05:39:25Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1059392026-06-06T00:49:30Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Samuel Madden.
Blum, Joshua (Joshua M.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-12-22T15:15:40Z
2016-12-22T15:15:40Z
2016
2016
http://hdl.handle.net/1721.1/105939
965197110
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 75-77).
In this thesis, I describe a system I designed and implemented for interactively analyzing large electroencephalogram (EEG) datasets. Trained experts, known as encephalographers, analyze EEG data to determine if a patient has experienced an epileptic seizure. Since EEG analysis is time intensive for large datasets, there is a growing corpus of unanalyzed EEG data. Fast analysis is essential for building a set of example data of EEG results, allowing doctors to quickly classify the behavior of future EEG scans. My system aims to reduce the cost of analysis by providing near real-time interaction with the datasets. The system has three optimized layers handling the storage, computation, and visualization of the data. I evaluate the design choices for each layer and compare three dierent implementations across dierent workloads.
by Joshua Blum.
M.Eng.
77 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Pinky : interactively analyzing large EEG datasets
Thesis
application/pdf
Publication
unknown
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