Medical Design Briefs - April 2023 - 22

(3.48 mm2), giving it great potential for
scalability to more channels. The integration
of an energy-aware learning algorithm
- which penalizes features that
consume a lot of power - also makes
NeuralTree highly energy efficient.
In addition to these advantages, the
system can detect a broader range of
symptoms than other devices, which until
now have focused primarily on epileptic
seizure detection. The chip's machine
learning algorithm was trained
on datasets from both epilepsy and Parkinson's
disease patients, and accurately
classified pre-recorded neural signals
from both categories.
" To the best of our knowledge, this is the
first demonstration of Parkinsonian tremor
detection with an on-chip classifier, "
Shoaran says.
n Self-Updating Algorithms
Shoaran is passionate about making
neural interfaces more intelligent to
enable more effective disease control,
and she is already looking ahead to further
innovations.
" Eventually, we can use neural interfaces
for many different disorders, and we
need algorithmic ideas and advances in
chip design to make this happen. This
work is very interdisciplinary, and so it also
requires collaborating with labs like the
Laboratory for Soft Bioelectronic Interfaces,
which can develop state-of-the-art neural
electrodes, or labs with access to
high-quality patient data. "
As a next step, she is interested in enabling
on-chip algorithmic updates to keep
up with the evolution of neural signals.
" Neural signals change, and so over time
the performance of a neural interface will
decline. We are always trying to make algorithms
more accurate and reliable, and
one way to do that would be to enable onchip
updates, or algorithms that can update
themselves. "
Funding was provided by ERC Starting
Grant 2021, funded by the Swiss State
Secretariat for Education, Research, and
Innovation.
References
1. U. Shin, C. Ding, B. Zhu, Y. Vyza, A. Trouillet, E. C.
M. Revol, S. P. Lacour, M. Shoaran, " NeuralTree: A
256-Channel 0.227-μJ/Class Versatile Neural Activity
Classification and Closed-Loop Neuromodulation
SoC, " in IEEE Journal of Solid-State Circuits
(JSSC), vol. 57, no. 11, pp. 3243-3257, Nov. 2022,
doi: 10.1109/JSSC.2022.3204508.
2. U. Shin, L. Somappa, C. Ding, B. Zhu, Y. Vyza, A.
Trouillet, S. P. Lacour, M. Shoaran, " A 256- Channel
0.227μJ/class Versatile Brain Activity Classification
and Closed-Loop Neuromodulation SoC with
0.004mm2-1.51μW/channel Fast-Settling Highly
Multiplexed Mixed-Signal Front-End " in IEEE International
Solid-State Circuits Conference (ISSCC),
2022, doi: 10.1109/ISSCC42614.2022.9731776.
This article was written by Celia LuterResearchers
have combined low-power chip design, machine learning algorithms, and soft implantable
electrodes to produce a neural interface that can identify and suppress symptoms of
various neurological disorders. (Credit: EPFL)
bacher, EPFL. Contact: mahsa.shoaran@
epfl.ch. For more information, visit
https://actu.epfl.ch.
Powering Wearable Technology with MXene Textile
Supercapacitor 'Patch'
The textile-based
supercapacitor can
charge in minutes.
Drexel University
Philadelphia, PA
Researchers at Drexel University are
one step closer to making wearable textile
technology a reality. Recently published in
the Royal Society of Chemistry's Journal of
Material's Chemistry A, materials scientists
from Drexel's College of Engineering, in
partnership with a team at Accenture Labs,
have reported a new design of a flexible
wearable supercapacitor patch. It uses MXene,
a material discovered at Drexel Uni22
versity
in 2011, to create a textile-based supercapacitor
that can charge in minutes
and power an Arduino microcontroller
temperature sensor and radio communication
of data for almost two hours.
" This is a significant development for
wearable technology, " says Yury Gogotsi,
PhD, Distinguished University and Bach
professor in Drexel's College of Engineering,
who co-authored the study. " To
fully integrate technology into fabric, we
must also be able to seamlessly integrate
its power source - our invention shows
the path forward for textile energy
storage devices. "
Co-authored along with Gogotsi's undergraduate
and postdoctoral students;
www.medicaldesignbriefs.com
Genevieve Dion, professor and director
of the Center for Functional Fabrics and
researchers from Accenture Labs in California,
the study builds on previous research
that looked at durability, electric
conductivity and energy storage capacity
of MXene-functionalized textiles that did
not push to optimize the textile for powering
electronics beyond passive devices
such as LED lights. The latest work shows
that not only can it withstand the rigors of
being a textile, but it can also store and
deliver enough power to run programmable
electronics collecting and transmitting
environmental data for hours -
progress that could position it for use in
healthcare technology.
Medical Design Briefs, April 2023
https://actu.epfl.ch http://www.medicaldesignbriefs.com
Medical Design Briefs - April 2023

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