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<p style="margin: 0in; font-family: Aptos, sans-serif; font-size: 12pt;"><span style="font-family: Arial, sans-serif; font-size: 13.5pt; color: black;"><img alt="Dissertation Defense Announcement at the Cullen College of Engineering" id="x_Picture_x0020_2" width="598" height="170" style="width: 6.2395in; height: 1.7812in; margin-top: 0px; margin-bottom: 0px;" data-outlook-trace="F:1|T:1" src="cid:image001.png@01DA8B3E.77DD5C80"></span></p>
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<span style="font-family: "Times New Roman", serif; font-size: 18pt; color: rgb(200, 16, 46);"><b>State of the Art of Implantable Brain-Computer Interfaces (BCI), and Emergent Developments Toward Pediatric BCI Systems</b></span></p>
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<span style="font-family: "Times New Roman", serif;"><b> </b></span></p>
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<span style="font-family: "Times New Roman", serif; font-size: 13.5pt; color: black;"><b>K. Michelle Patrick Krueger</b></span></p>
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<span style="font-family: Arial, sans-serif; font-size: 10.5pt; color: black;">April 17, 2024; 1:00 PM - 3:00 PM (CDT)<br>
Location: E413 Engineering Building 2<br>
Zoom: <a href="https://urldefense.com/v3/__https:/us06web.zoom.us/j/85138678142?pwd=ZDaZp3HnLZ7bPyZr1sVw95GT1K6KFi.1__;!!LkSTlj0I!HEqetiRAGCWqxil-UEFzJFR-28JZZpeUWRkNG-KolLnUYFsy0fC0lCPRT_Kft0q6mazWmDXFsrXd0jRqbPWYknYKSVeknw$" data-auth="NotApplicable" style="margin-top: 0px; margin-bottom: 0px;" id="OWA53bc9c02-5552-fe01-efba-d645410f6d66" class="OWAAutoLink" data-loopstyle="linkonly">
https://us06web.zoom.us/j/85138678142?pwd=ZDaZp3HnLZ7bPyZr1sVw95GT1K6KFi.1</a></span></p>
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<span style="font-family: Arial, sans-serif; font-size: 10.5pt; color: black;"><b>Committee Chair:</b><br>
Jose L. Contreras-Vidal, Ph.D.</span></p>
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<span style="font-family: Arial, sans-serif; font-size: 10.5pt; color: black;"><b>Committee Members:</b><br>
Ioannis Pavlidis, Ph.D. | Saurabh Prasad, PhD | David Mayerich, Ph.D. | Stuart Fraser, MD, Ph.D. | Nathan Wiggins, Ph.D.</span></p>
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<span style="font-family: Arial, sans-serif; color: rgb(200, 16, 46);"><b>Abstract</b></span></p>
<p style="margin: 0in; font-family: Aptos, sans-serif; font-size: 12pt;"><span style="font-family: Arial, sans-serif; color: black;">Implanted brain-computer interface (iBCI) systems are providing volitional gait control to paraplegics, control of assistive
robotic arms, and giving voice to people with dysarthria. This dissertation has undertaken two efforts to advance the translation of iBCI systems and to promote pediatric inclusion. First, an enhanced, critical integrated knowledge review of iBCI systems has
been conducted to elucidate the status of the field and identify challenges and opportunities for accelerating translation to the end users. This yielded a registry of the 28 identified clinical trials conducted by 21 research groups investigating iBCI for
communication and motor control (iBCI-CMC), as well as demographic and implantation information on the 67 trial participants who were implanted with four types of electrodes. Sociotechnical considerations were assessed and addressed, including the sparsity
of female participants, ethical considerations, medical outcomes, and workforce development, and a roadmap provided. An analysis of publications indicates a high level of convergence and collaboration throughout the iBCI-CMC field with the highest impact coming
from the active research groups of BrainGate, University of Pittsburgh, and Caltech. Currently, iBCI participants are primarily adult males, though recent literature has begun discussing the special considerations needed for use in pediatric populations. The
second effort of this dissertation has been conducting a feasibility study toward developing a pediatric non-invasive BCI system for gait assistance and rehabilitation using deep learning algorithms to decode lower-limb joint kinematics from scalp electroencephalography
(EEG) in typically-developing children. Multiple methodological approaches were compared varying 18 EEG denoising pipelines and two decoding algorithms: Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). Results showed the LSTM algorithm operating
on delta-band (0.1-4 Hz) EEG data denoised using only H-infinity and bandpass filtering with common average referencing provided the highest decoding accuracy. The IC cluster centroids for the participants under one standard deviation of accuracy are in the
prefrontal cortex, premotor cortex, somatosensory cortex, angular gyrus, and visual cortex with no clear age/gender differentiation potentially due to additional complexities inherent in pediatric data collections. Overall, this dissertation resulted in a
dataset and actionable recommendations to accelerate the translation of BCI systems to patients, including children.</span></p>
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<p style="margin: 0in; font-family: Aptos, sans-serif; font-size: 12pt;"><span style="font-family: Arial, sans-serif; font-size: 13.5pt; color: black;"><img alt="Engineered For What's Next" id="x_Picture_x0020_1" width="624" height="81" style="width: 6.5104in; height: 0.8541in; margin-top: 0px; margin-bottom: 0px;" data-outlook-trace="F:1|T:1" src="cid:image002.png@01DA8B3E.77DD5C80"></span></p>
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