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DTSTART:20190310T030000
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DTSTART:20181104T010000
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DTSTAMP:20190301T033657Z
UID:7B12BC70-6ED1-40F4-81BE-2BC4E16ADA75
DTSTART;TZID=America/Chicago:20190228T184500
DTEND;TZID=America/Chicago:20190228T200000
DESCRIPTION:The IEEE Computer Society Chicago Section is proud to present L
 oyola University’s Dr. Vincent Chen.\n\nClinical researchers have long c
 onsidered the task of localizing real-time changes in the cortical areas t
 hat handle sensory signals a difficult one\, primarily due to limitations 
 of biomedical instrumentation. The electroencephalogram (EEG)\, despite pr
 oviding excellent temporal resolution for monitoring real-time brain signa
 ls\, can only offer very limited spatial resolution of brain activities. T
 his makes it difficult to determine the origins of brain signals\, which i
 n turn makes it challenging to pinpoint the exact cortical areas that are 
 actively functioning. In the proof of concept for this study\, EEG signals
  acquired from a subject are segmented into appropriate epochs based on a 
 pseudorandom binary sequence. During EEG acquisition\, peripheral nerve st
 imulation synchronized with the same pseudorandom binary sequence is appli
 ed to the immediate surface of the subject&#39;s right median nerve\, enabling
  us to perform calculations of autocorrelation values derived from the pse
 udorandom binary sequence and the power spectral density of the EEG epochs
 . Higher autocorrelation values provide greater certainty as to the origin
  of the brain signals. By utilizing features of this stochastic method\, w
 e can provide close to real-time brain maps that can accurately associate 
 ever-changing cortical activities with indications of neuroplasticity. Thi
 s innovation can be utilized as a groundbreaking tool for neurorehabilitat
 ion applications.\n\nSpeaker(s): Dr. Vincent Chen\, \n\nRoom: Suite 700\, 
 Bldg: Lewis University at Oak Brook\, 1111 W. 22nd Street\, Oak Brook\, Il
 linois\, United States\, 60523
LOCATION:Room: Suite 700\, Bldg: Lewis University at Oak Brook\, 1111 W. 22
 nd Street\, Oak Brook\, Illinois\, United States\, 60523
ORGANIZER:gina.martinez.us@ieee.org
SEQUENCE:2
SUMMARY:MAPPING THE BRAIN BY APPLYING PSEUDORANDOMLY CODED PERIPHERAL NERVE
  STIMULATION
URL;VALUE=URI:https://events.vtools.ieee.org/m/188995
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;&lt;strong&gt;The IEEE Computer Society Chicago 
 Section is proud to present Loyola University&amp;rsquo\;s Dr. Vincent Chen.&lt;/
 strong&gt;&lt;/p&gt;\n&lt;p&gt;Clinical researchers have long considered the task of loca
 lizing real-time changes in the cortical areas that handle sensory signals
  a difficult one\, primarily due to limitations of biomedical instrumentat
 ion. The electroencephalogram (EEG)\, despite providing excellent temporal
  resolution for monitoring real-time brain signals\, can only offer very l
 imited spatial resolution of brain activities. This makes it difficult to 
 determine the origins of brain signals\, which in turn makes it challengin
 g to pinpoint the exact cortical areas that are actively functioning. In t
 he proof of concept for this study\, EEG signals acquired from a subject a
 re segmented into appropriate epochs based on a pseudorandom binary sequen
 ce. During EEG acquisition\, peripheral nerve stimulation synchronized wit
 h the same pseudorandom binary sequence is applied to the immediate surfac
 e of the subject&#39;s right median nerve\, enabling us to perform calculation
 s of autocorrelation values derived from the pseudorandom binary sequence 
 and the power spectral density of the EEG epochs. Higher autocorrelation v
 alues provide greater certainty as to the origin of the brain signals. By 
 utilizing features of this stochastic method\, we can provide close to rea
 l-time brain maps that can accurately associate ever-changing cortical act
 ivities with indications of neuroplasticity. This innovation can be utiliz
 ed as a groundbreaking tool for neurorehabilitation applications.&lt;/p&gt;\n&lt;p&gt;
 &amp;nbsp\;&lt;/p&gt;
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