Project 1
Student id: g14
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<docHead>
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<title>Project 1 - Annotated Bibliography</title>
<author xml:id="g14">Ben Gincley</author>
<version n="3" date="2016-07-13"/>
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</docHead>
<annotated_bib>
<problem_stmt>In neural engineering, there currently exist many gaps between current
technology available to the public, and where public necessity lies. Demand comes
primarily from the clinical sector, wherein individuals suffering from neurodegenerative
diseases are left at a deficit in their ability to sense and interact with the world
around them. Challenges facing the technology mainly center around decoding the
biochemical signals within the central nervous system, encoding information technology
into biochemical signals, and creating devices that are able to function within or in
tandem with biological systems. Neural engineering as a discipline attempts to close
these gaps by taking neurobiological, materials science, biotechnological, and computer
science research and designing devices and techniques to integrate technology with
neurobiology. Much of the work takes place in pre-clinical and clinical research
contexts, and the applied aspect of the discipline is still by-and-large in its infancy,
as technology begins to catch up to what is demanded by the applications. Ultimately,
the goal of the discipline is to create devices that work seamlessly with biological
systems, are non-invasive or biologically integrated, and allow complete restoration of
function without loss of fidelity.<note type="instructor">So, as I understand it, your
'problem' is the gap between current neural engineering tech available to the public
and the demand for said tech? Or is it more broadly the further development of this
(relatively) nascent field of research? The question is whether your problem is
about development or implementation, I guess.</note>
</problem_stmt>
<!-- Source 1-->
<citation n="1" style="APA"><author n="1">Jarosiewicz, B.</author>, <author>Sarma, A. A.,
Bacher, D., Masse, N. Y., Simeral, J. D., Sorice, B., … Hochberg, L.R.</author>,
(2015).<title level="a">Virtual typing by people with tetraplegia using a
self-calibrating intracortical brain-computer interface</title>. <title level="j">
Science Translational Medicine</title>, 7(313), 313ra179.
<ref>http://doi.org/10.1126/scitranslmed.aac7328</ref>
</citation>
<annotation>
<background type="author">The primary author of this paper, Beata Jarosiewicz, is a
professor at Brown University's Department of Neuroscience, who has, in her career,
contributed to or produced 18 publications, garnering nearly 800 citations thus far,
and is renowned for her work using biotechnology and neuroscience to investigate
ways to use and optimize brain-computer interfaces (BCIs).
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</background>
<summary type="general">Jarosiewicz, et. al., sought to demonstrate that the
nonstationarity that is convolved with neural signaling, that normally forces
brain-computer interfaces to require frequent recalibration, can be mitigated using
software to automatically account for such drift, in a point-and-click model used by
tetrapalegic patients. <!-- can include <q> element(s) for quoted material -->
</summary>
<summary type="approach">The investigators used technological innovations, namely:
<q>tracking the statistics of neural activity</q> (neurobiological
observations), correction for velocity bias (computer engineering), and
recalibration of the decoder using data inferred from the user's past actions
(computer engineering) to develop software that extends the time that BCIs can
function properly before needing to be manually recalibrated.<note type="instructor"
>I appreciate the close attention to discipline-specific methods in the approach
section, it helps me a lot (as an outsider in all of these fields)</note>
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</summary>
<relevance type="application">The innovations made through this investigation can be
applied to a wider range of devices, not merely point-and-click oriented BCIs. This
will further enable the viable use of intracortical BCIs that patients needing
restoration of independent communication and other assistive devices depend on, and
lay the groundwork for more optimized methods of accounting for nonstationarities.
</relevance>
</annotation>
<!-- Source 2-->
<citation n="2" style="APA"><author n="1">Hwang, H.-J.</author>, <author>Ferreria, V. Y.,
Ulrich, D., Kilic, T., Chatziliadis, X., Blankertz, B., Treder, M.</author> (2015).
<title level="a"> A Gaze Independent Brain-Computer Interface Based on Visual
Stimulation through Closed Eyelids.</title>
<title level="j">Scientific Reports</title>, 5, 15890.
<ref>http://doi.org/10.1038/srep15890</ref></citation>
<annotation>
<background type="source">At the time of publication, Hwang was a postdoctoral
researcher at the Machine Learning Group of the Berlin Institute of Technology (TU
Berlin), and the supporting authors were members of the Neurotechnology Group of TU
Berlin. These groups are highly prolific, contributing a dozen or more journal
publications, conference papers, and book materials each year. </background>
<summary type="general">Most Brain Computer Interfaces (BCIs) function using the visual
system of patients, often requiring individuals to gaze at elements of a display to
provide a signal to the computer. This study seeks to introduce a BCI model that
does not require voluntary gaze, to help patients who are visually paralyzed or
suffer a similar injury or deficit. </summary>
<summary type="approach">The study takes the engineering process approach of finding a
gap in current technology, in this case a lack of BCIs that are usable by patients
with ocular dysfunction, and innovates a novel paradigm in which patients attend to
one of three stimuli presented to their closed eyelids. Understanding
neurobiological phenomenon associated with attention, and usnig electrical
engineering to modify existing technology, the researchers were able to come up with
a novel solution. </summary>
<relevance type="application">The investigation demonstrated significantly effective use
of the novel paradigm with patients for whom use of standard BCI devices are not
possible or effective, and was the first to do so using this method. As a result,
technology has been adapted to aid individuals who were previously unable to be
helped. </relevance>
</annotation>
<!-- Source 3-->
<citation n="3" style="APA"><author n="1">McClay, W. A.</author>, <author>Yadav, N., Ozbek,
Y., Haas, A., Attias, H. T., Nagarajan, S. S.</author> (2015). <title level="a">A
Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D
Visualization and the Hadoop Ecosystem.</title>
<title level="j">Brain Sciences</title>, 5(4), 419–440.
<ref>http://doi.org/10.3390/brainsci5040419</ref>
</citation>
<annotation>
<background type="author">The primary team contributing to this work is based out of
Northeastern University in conjunciton with the Lawrence Livermore National
Laboratory. Northeastern is a highly prolific private research university<note
type="instructor">recently reclassified as a Research-1 institution, in
fact</note> that encourages groundbreaking technological advancement research,
and the LLNL is widely recognized as being committed to seeking engineering
solutions to nationally-scoped problems. </background>
<summary type="general">Understanding that advances in technology have allowed for
better handling of big data, the investigators have developed a BCI that utilizes
Hadoop (a library of big data processing programs) to distinguish distinctive
thought actions using magnetoencepholography (MEG) in a demonstration of flight
visualization, where the user's thoughts of left or right are used to turn a virtual
aircraft in that direction. </summary>
<summary type="approach">By combining computer engineering and computer science methods
with principles of neurobiology, the investigators have developed a system that can
pick out specific thoughts using a non-invasive technique. </summary>
<relevance type="application">The basis of this BCI technology can be used to translate
user thought into specific actions like keyboard or mouse button presses, and the
history of the user's input can be stored and analyzed using Hadoop to both access
the information later for observation, and to ultimately perform comprehensive
post-hoc analyses or calculations. These advances pave the way for further
integration of big data computation and BCI technology.</relevance>
</annotation>
<!-- Source 4-->
<citation n="4" style="APA"><author n="1">Minev, I. R.</author>, <author>Musienko, P.,
Hirsch, A., Barraud, Q., Wenger, N., Moraud, E. M., … Lacour, S. P.</author> (2015).
<title level="a">Electronic dura mater for long-term multimodal neural
interfaces.</title>
<title level="j">Science</title>, 347(6218), 159–163.
<ref>http://doi.org/10.1126/science.1260318</ref>
</citation>
<annotation>
<background type="source">The Center for Neuroprosthetics of the Ecole Polytechnique
Fédérale de Lausanne (EPFL) is a Switzerland-based research organization with
notable sponsors including the International Foundation for Research in Paraplegia
and Medtronic. It is involved in an array of cutting-edge research endeavors with
bases in neuroscience and biomedical engineering.</background>
<summary type="general">Working from the challenge that stiff neural-integrated devices
often don't interface well with soft biological systems<note type="instructor">what
is a 'soft biologica; system? maybe a quick example here</note> over extended
periods of time, the researchers developed a neural implant with properties similar
to dura mater, the membrane that covers brain and spinal cord tissue<note
type="instructor">I see, literally 'soft'. That makes sense</note>. The
resulting construct has been demonstrated to be durable with regards to stretch
cycles, chemical injections, and electrical stimulation, allowing it to be viable in
an array of different tissue areas and treatment/function scenarios. </summary>
<summary type="approach">The research team has integrated neurobiological scientific
knowledge with materials science and electrical engineering innovations to create
implants that better integrate with biological tissue, improving on previous devices
that were previously made of stiffer, less bio-friendly materials.</summary>
<relevance type="application">The advances made by this group establish technological
innovations that will allow others in the future to use similar techniques,
materials, and methodologies to continue to develop solutions to the problems
touched on by this work. The implants were demonstrated to function both to extract
neural activity in behaving animals, as well as restoring locomotion to animals
suffering from spinal cord paralysis with electrochemical inputs. Thus, continued
study of these materials will allow for devices that can extract neural activity and
provide supplemental inputs to paralyzed systems.</relevance>
</annotation>
<!-- Source 5-->
<citation n="5" style="APA"><author n="1">Gibson, R. M.</author>, <author n="2">Chennu,
S.</author>, <author n="3">Owen, A. M.</author>, <author n="4">Cruse, D.</author>
(2014). <title level="a">Complexity and familiarity enhance single-trial detectability
of imagined movements with electroencephalography.</title>
<title level="j">Clinical Neurophysiology</title>, 125(8), 1556–1567.
<ref>http://doi.org/10.1016/j.clinph.2013.11.034</ref>
</citation>
<annotation>
<background type="source">The Brain and Mind Institute of Western University is a
distinguished institution that focuses on an integrative approach to neuroscience,
incorporating an array of different disciplines' perspectives to address challenges
facing the field. </background>
<summary type="general">The work in this publication centers on testing whether
sensorimotor rhythms that result from motor imagery are more distinguishable via
electroencepholography if the actions are more complex and are familiar to the user,
such as requesting a pianist to imagine playing a complicated piece of music.
Through study with healthy volunteer subjects, the research has found that EEG
responses are indeed more reliably classified when motor imagery actions are complex
and familiar.</summary>
<summary type="approach">This research is based in a scientific method approach of
testing a hypothesis based off of other conclusions in the field, and integrates
cognitive neuroscience, EEG technology, and computational analytics to find ways to
optimize reliable signal classification in EEG scenarios. </summary>
<relevance type="application">As a result of this research, Brain-Computer Interfaces
can serve more effectively when decoding the intentions of the user when the motor
imagery used is more complex and/or are familiar to the user. This study will
therefore improve the ability for the intentions of individuals who are unable to
communicate normally to be interpreted by BCIs, and assist in the overall goal of
the discipline to utilize less invasive technologies such as EEG and its eventual
descendents.</relevance>
</annotation>
</annotated_bib>
<docReview>
<reviewer type="instructor" xml:id="kgs"/>
<report type="instructor" resp="#kgs">
<p>This is really interesting stuff and I enjoyed reading about this area of research.
On the whole, I think you did an excellent job on the annotations, particularly with
respect to how you outline the approach (very important with a field as complex as
this). I was (as you can see by my note) just a bit lost reading your problem
statement; perhaps a revision would include just a sentence or so that specifically
circumscribes the issue you are taking on in the annotated bib. After reading your
annotations, I understand your problem to be a problem of scope. And the research
articles you selected all appropriately seek to increase the scope of neural
engineering to a wider array of patients and/or clinical situations.</p>
</report>
</docReview>
</DOC>