{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/62754"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/62754","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Neural correlates of extended dynamic face processing in neurotypicals","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_html":"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_has_math":false,"creators":["Urban, Luke (Luke S.)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Pawan Sinha."],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010","date_published":"2010","updated_at":"2026-07-22T22:21:30Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"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. 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