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TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20290311T030000 RDATE:20291104T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20300310T030000 RDATE:20301103T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20310309T030000 RDATE:20311102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20320314T030000 RDATE:20321107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20330313T030000 RDATE:20331106T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20340312T030000 RDATE:20341105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:d30a4cdb434d1e98a2f68037695686b5 CATEGORIES:Qualifying Exam CREATED:20190823T084022 SUMMARY:Brain-informed learning models of movement primitives for neurorehabilitation robots LOCATION:CBIM 22 DESCRIPTION;ENCODING=QUOTED-PRINTABLE:
Abstract:
Neurorehabilitation robotics is the epi tome of a challenging interaction between robots and humans, where pathophy siological movements need to be first accurately and precisely identified a nd then steered towards normalcy. In this talk, I will present our first fr uits in exploring the fascinating possibility that a neural network, when d esigned and trained upon the brain's constraints, has enough discriminative power to predict brain states associated with movement primitives. We have initially focused on the intent of the user to move (2-classes), the react ion time (3-classes), and the direction of the movement (4-classes). Specif ically, I will propose a new 3-dimensional Convolutional Neural Network arc hitecture that accounts for the brain regions associated with the respectiv e task. I will demonstrate how, for the first time, a model can learn to pr edict movement primitives using electroencephalography (EEG), in an experim ental paradigm that we designed and ran in the Lab. In addition to establis hing the effectiveness of 3D convolutions in extracting task-discriminative spatiotemporal features from EEG, our model outperformed the state-of-the- art methods, demonstrating above 9% improvement in the 4-classes task. By d eveloping data-rich brain-constrained deep models, I seek to objectify and subsequently optimize therapy provided by robots as they interact with the intrinsic variability of each brain’s dysfunction and steer it towards norm alcy.
(This research is supported through Grant K12HD093427 from the National Center for Medical Rehabilitation Research, NIH/NICHD.)
DTSTAMP:20240329T003638Z DTSTART;TZID=America/New_York:20190507T150000 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR