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<DOC>
  <docHead>
    <!--required header includes metadata about the assignment (title, author, version)-->
    <title>Project 1 - Annotated Bibliography</title>
    <version n="3" date="2016-07-13"/>
    <!--note that the date must be YYYY-MM-DD for the document to be valid-->
  </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.</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).
                <!-- can include <q> element(s) for quoted material --></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.<!-- can include <q> element(s) for quoted material --></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 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 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. 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>
</DOC>

  

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