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DESCRIPTION:Abstract\n\nBrain-Computer Interfaces (BCI) attempt to measure 
 neuronal activity in the brain of a computer user and use those measuremen
 ts to infer the user's cognitive state. This is a highly interdisciplinary
  area of study\, which overlaps with neuroscience\, psychology\, and compu
 ter science. Applications of BCI include many areas such as interactive me
 dia\, adaptive user interfaces\, accessible user interfaces\, usability te
 sting\, and human-machine teaming. In this talk\, the author will present 
 his work on improving BCI using machine learning. He used a brain activity
  sensor called functional Near InfraRed Spectroscopy (fNIRS)\, which uses 
 near-infrared light to measure blood flow in the brain's cerebral cortex. 
 He developed novel preprocessing and machine learning techniques to analyz
 e fNIRS data and infer user emotion and cognitive workload. He will presen
 t the methods and machine learning techniques used in his research. He wil
 l also talk about the implications of his research and future directions.\
 n\nBio\n\nDanushka Bandara received his Ph.D. in Electrical and Computer E
 ngineering and M.S. in Computer Engineering from Syracuse University in 20
 18 and 2013\, respectively\, and a B.S. in Electrical Engineering with hon
 ors from the University of Moratuwa in 2009. Before joining Fairfield Univ
 ersity\, he worked as a Data Scientist at Corning Incorporated. The focus 
 of his Ph.D. research was on the application of machine learning to brain 
 activity data. His research interests include machine learning\, human-com
 puter interaction\, computer vision\, pattern recognition\, and signal pro
 cessing.\n\nFairfield\, Connecticut\, United States\, Virtual: https://eve
 nts.vtools.ieee.org/m/289362
LOCATION:Fairfield\, Connecticut\, United States\, Virtual: https://events.
 vtools.ieee.org/m/289362
ORGANIZER:arusu@fairfield.edu
SEQUENCE:2
SUMMARY:Brain-Computer Interfaces (BCI) 
URL;VALUE=URI:https://events.vtools.ieee.org/m/289362
X-ALT-DESC:Description: <br /><p class="yiv0121184854msonormal">Abstract</p
 >\n<p class="yiv0121184854msonormal">Brain-Computer Interfaces (BCI) attem
 pt to measure neuronal activity in the brain of a computer user and use th
 ose measurements to infer the user's cognitive state. This is a highly int
 erdisciplinary area of study\, which overlaps with neuroscience\, psycholo
 gy\, and computer science. Applications of BCI include many areas such as 
 interactive media\, adaptive user interfaces\, accessible user interfaces\
 , usability testing\, and human-machine teaming. In this talk\, the author
  will present his work on improving BCI using machine learning. He used a 
 brain activity sensor called functional Near InfraRed Spectroscopy (fNIRS)
 \, which uses near-infrared light to measure blood flow in the brain's cer
 ebral cortex. He developed novel preprocessing and machine learning techni
 ques to analyze fNIRS data and infer user emotion and cognitive workload. 
 He will present the methods and machine learning techniques used in his re
 search. He will also talk about the implications of his research and futur
 e directions.</p>\n<p class="yiv0121184854msonormal">&nbsp\;</p>\n<p class
 ="yiv0121184854msonormal">Bio</p>\n<p class="yiv0121184854msonormal">Danus
 hka Bandara received his Ph.D. in Electrical and Computer Engineering and 
 M.S. in Computer Engineering from Syracuse University in 2018 and 2013\, r
 espectively\, and a B.S. in Electrical Engineering with honors from the Un
 iversity of Moratuwa in 2009. Before joining Fairfield University\, he wor
 ked as a Data Scientist at Corning Incorporated. The focus of his Ph.D. re
 search was on the application of machine learning to brain activity data. 
 His research interests include machine learning\, human-computer interacti
 on\, computer vision\, pattern recognition\, and signal processing.</p>
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