BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Canada/Mountain BEGIN:DAYLIGHT DTSTART:20220313T030000 TZOFFSETFROM:-0700 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:MDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211107T010000 TZOFFSETFROM:-0600 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:MST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20220118T023135Z UID:A8E48C0A-55D0-494C-81F8-D5905036771D DTSTART;TZID=Canada/Mountain:20220117T180000 DTEND;TZID=Canada/Mountain:20220117T193000 DESCRIPTION:Abstract: Brain machine interfaces or neural prosthetics have t he potential to restore movement to people with paralysis or amputation\, bridging gaps in the nervous system with an artificial device. Microelectr ode arrays can record from hundreds of individual neurons in motor cortex\ , and machine learning can be used to generate useful control signals from this neural activity. Performance can already surpass the current state o f the art in assistive technology in terms of controlling the endpoint of computer cursors or prosthetic hands. The natural next step in this progre ssion is to control more complex movements at the level of individual fing ers. Our lab has approached this problem in three different ways. For peop le with upper limb amputation\, we acquire signals from individual periphe ral nerve branches using small muscle grafts to amplify the signal. Human study participants have recently been able to control individual fingers o nline using indwelling EMG electrodes within these grafts. For spinal cord injury\, where no peripheral signals are available\, we implant Utah arra ys into finger areas of motor cortex\, and have successfully decoded flexi on and extension in multiple fingers simultaneously. Decoding “spiking b and” activity at much lower sampling rates\, we recently showed that pow er consumption of an implantable device could be reduced by an order of ma gnitude compared to existing broadband approaches\, and fit within the spe cification of existing systems for upper limb functional electrical stimul ation. Finally\, finger control is ultimately limited by the number of ind ependent electrodes that can be placed within cortex or the nerves\, and t his is in turn limited by the extent of glial scarring surrounding an elec trode. Therefore\, we developed an electrode array based on 8 um carbon fi bers\, no bigger than the neurons themselves to enable chronic recording o f single units with minimal scarring. The long-term goal of this work is t o make neural interfaces for the restoration of hand movement a clinical r eality for everyone who has lost the use of their hands.\n\nSpeaker(s): Cy nthia Chestek\, PhD\, \n\nCalgary\, Alberta\, Canada\, Virtual: https://ev ents.vtools.ieee.org/m/294157 LOCATION:Calgary\, Alberta\, Canada\, Virtual: https://events.vtools.ieee.o rg/m/294157 ORGANIZER:lwhitby@ieee.org SEQUENCE:4 SUMMARY:Neural Interfaces for Controlling Finger Movements URL;VALUE=URI:https://events.vtools.ieee.org/m/294157 X-ALT-DESC:Description: <br /><p>Abstract: Brain machine interfaces or neur al prosthetics have the potential to restore movement to people with paral ysis or amputation\, bridging gaps in the nervous system with an artificia l device. Microelectrode arrays can record from hundreds of individual neu rons in motor cortex\, and machine learning can be used to generate useful control signals from this neural activity. Performance can already surpas s the current state of the art in assistive technology in terms of control ling the endpoint of computer cursors or prosthetic hands. The natural nex t step in this progression is to control more complex movements at the lev el of individual fingers. Our lab has approached this problem in three dif ferent ways. For people with upper limb amputation\, we acquire signals fr om individual peripheral nerve branches using small muscle grafts to ampli fy the signal. Human study participants have recently been able to control individual fingers online using indwelling EMG electrodes within these gr afts. For spinal cord injury\, where no peripheral signals are available\, we implant Utah arrays into finger areas of motor cortex\, and have succe ssfully decoded flexion and extension in multiple fingers simultaneously. Decoding &ldquo\;spiking band&rdquo\; activity at much lower sampling rate s\, we recently showed that power consumption of an implantable device cou ld be reduced by an order of magnitude compared to existing broadband appr oaches\, and fit within the specification of existing systems for upper li mb functional electrical stimulation. Finally\, finger control is ultimate ly limited by the number of independent electrodes that can be placed with in cortex or the nerves\, and this is in turn limited by the extent of gli al scarring surrounding an electrode. Therefore\, we developed an electrod e array based on 8 um carbon fibers\, no bigger than the neurons themselve s to enable chronic recording of single units with minimal scarring. The l ong-term goal of this work is to make neural interfaces for the restoratio n of hand movement a clinical reality for everyone who has lost the use of their hands.</p> END:VEVENT END:VCALENDAR