2026-09-19T03:47:00Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/627542022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Pawan Sinha.
Urban, Luke (Luke S.)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-05-09T15:30:30Z
2011-05-09T15:30:30Z
2010
2010
http://hdl.handle.net/1721.1/62754
717715558
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 105-107).
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.
by Luke Urban.
M.Eng.
107 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Neural correlates of extended dynamic face processing in neurotypicals
Thesis
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<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.</Abstract>
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