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<p class="MsoNormal"><span style="font-family:"Arial",sans-serif"><img width="600" height="171" style="width:6.25in;height:1.7812in" id="Picture_x0020_2" src="cid:image001.png@01D9BBC8.D63F3460" alt="Dissertation Defense Announcement at the Cullen College of Engineering"><o:p></o:p></span></p>
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<b><span style="font-size:18.0pt;font-family:"Times New Roman",serif;color:#C8102E">Non-invasive Electrospinography for a Novel Spinal Cord Computer Interface</span></b><span style="font-size:18.0pt;color:#C8102E"><o:p></o:p></span></p>
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<b><span style="font-size:13.5pt;font-family:"Times New Roman",serif;color:black">Alexander Gabriel Steele</span></b><span style="font-size:13.5pt"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black">July 28, 2023; 8:00 AM - 9:00 AM (CST)</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif"><br>
Location: E413 Eng Bld 2<br>
Zoom: <a href="https://urldefense.com/v3/__https:/uh-edu-cougarnet.zoom.us/j/97360074777?pwd=b3M4cFJxUHFnZnpuU3kyWW8vNzg0QT09__;!!LkSTlj0I!ANZzifO4iWxQSUuqJIMkEdbo-MdjGQ45AtMuL6r5vVuchKwV4GDDksnF3zxxa0hHS1J2EhuRm1osOk1lkk8HMUARjt-J$" target="_blank">https://uh-edu-cougarnet.zoom.us/j/97360074777?pwd=b3M4cFJxUHFnZnpuU3kyWW8vNzg0QT09</a><o:p></o:p></span></p>
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<strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif">Committee Chair:</span></strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif"><br>
Jose L. Contreras-Vidal, Ph.D.<o:p></o:p></span></p>
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<strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif">Committee Members:</span></strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif"><br>
<span style="color:black">Amir Faraji, MD, Ph.D. | David Mayerich, Ph.D. | Luca Pollonini, Ph.D. | Saurabh Prasad, Ph.D. | Adam Thrasher, Ph.D.</span><o:p></o:p></span></p>
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<strong><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#C8102E">Abstract</span></strong><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#C8102E"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black">Spinal cord injury (SCI) is a debilitating and life-long condition that affects approximately 302,000 people living in the United States alone, with an average of 18,000 new cases of
SCI each year. This can be costly and can lead to secondary complications such as neuropathic pain, or osteoporosis. While medical science has improved over the last 40 years, the average years of life after an SCI have not improved since the 1980s. This may
be due in part to researchers treating the spinal cord as a black box and relying on indirect methods for studies.</span><span style="font-size:11.0pt"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black">First, we employ electrospinography (ESG) to ascertain if the signals can be related to left or right plantarflexion or knee flexion in neurologically intact participants (Height: 165
± 15 cm, Weight: 72 ± 8 kg, Age: 30 ± 12 years, Sex: 4 women). Using a directed functional connectivity measure, we then demonstrate that ESG contains information about efferent and afferent signaling. Next, these data are compared with ESG data from a participant
who has a SCI (AIS C: > 5 years since injury, age: 47, sex: woman, height: 158.5 cm, weight: 63.5 kg) recorded during two different sessions 10 days apart to establish if (1) ESG can detect attempted left or right knee flexion and (2) to identify differences
that may be related to the functional state of the individual. We determined that the ESG signal was observed and exhibited changes during movement attempts. We then characterize the differences in power spectral density during rest and movement attempts between
the groups. Lastly, by utilizing an unscented Kalman filtering approach, we found that ESG signals contain predictive information for estimating in real-time lower limb kinematics with an individual who has a SCI.</span><o:p></o:p></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black">The work in this dissertation provides evidence for the feasibility of ESG for non-invasive research on the spinal cord. Importantly, this study presents a strategy for real-time cardiac
artifact filtering, which could otherwise have limited the usefulness of ESG. We also demonstrate a method for predicting lower limb kinematics from ESG in a real-time setting – the first step towards a spinal cord computer interface.</span><o:p></o:p></p>
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