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Massachusetts Institute of Technology

Neural correlates of extended dynamic face processing in neurotypicals

Abstract

dc:description.abstract

This thesis explores the unique brain patterns resulting from prolonged dynamic face stimuli. The brain waves from neurotypical subjects were recorded using the electroencephalography (EEG) while viewing a series of 10 second long video clips. These clips were one of two categories: face or non-face. Modern signal processing and machine learning techniques were applied to the resulting waveforms to determine the underlying neurological signature for extended face viewings. The occipitotemporal (left hemisphere), occipitotemporal (right hemisphere), and occipital proved to have the largest change in activity. Across the 12 recorded subjects a consistent decrease in the 10 Hz power range and increase in the 20 Hz power range was found. This biomarker will serve later works in the study of autism.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2010

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Urban, Luke (Luke S.)
Advisor dc:contributor.advisor
  • Pawan Sinha.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • 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.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/62754
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/62754

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Urban, Luke (Luke S.). Neural correlates of extended dynamic face processing in neurotypicals. Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62754