muri project proposal-evmc

MURI Mentor’s Project Proposal Form, Updated: 9_10_12

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MURI Project Proposal Form

Section I: Proposal Cover Page

Date of submission: 23 August 2013 Proposed project title: Training the Extended Voltage Manifold Computer

Principle Mentor

Name: Ken Yoshida Title: Associate Professor

Phone number: 274 9714 Email: [email protected]

Department: Biomedical Engineering School: Engineering and Technology

Co-mentor

Name: Paul Salama Title: Professor

Phone number: 278 1682 Email: [email protected]

Department: Electrical Engineering School: Engineering and Technology

Co-mentor

Name: Title:

Phone number: Email:

Department: School:

Please note that preference will be given to projects that include mentors from multiple disciplines.


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Section II: Student Request Page Total number of students requested: 4 (Note: The total number of students must exceed by two the number of mentors) Total Number of freshmen and/or sophomores to be recruited: 1 (Note: Preference will be given to projects that include at least one freshman and/or sophomore) Disciplines or majors of students (preference will be given to projects that include at least two disciplines or majors): Biomedical Engineering (bioinstrumentaiton), Computer Engineering, Electrical Engineering, Applied Mathematics, Applied Statistics, Applied Physics Skills expected from students: Signals and Systems, Matlab, Stochastic Systems, Physics – Electricity, Calculus – Multivariate, Linear Algebra Names of students you request to work on this project. (Mentors are invited to recommend students that they would prefer to work on the proposed project. Please provide an email address and a rationale; for example, a student may have an essential skill, may already be working on a similar project, or may be intending to apply to graduate school to pursue the same area of research.) The Center for Research and Learning will consider the students requested below, but cannot guarantee placement of specific students on teams. Name of Student: Student’s Email: Rationale: 1)_________________ ______________ ________________________ 2)_________________ ______________ ________________________ 3)_________________ ______________ ________________________ 4)_________________ ______________ ________________________

5)_________________ ______________ ________________________ 6)_________________ ______________ ________________________


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Section III: Body of Proposal (A maximum of 5 pages is allowed for answers to questions 1-11.)

1) What are the research objectives for the proposed project?

2) What specific research question(s) will your proposed project address?

3) What is the significance of this research?

4) Why does this proposal offer a good opportunity for undergraduate researchers to gain substantive research skills?

5) What research methodology and specific tasks will students and mentors undertake?

6) What plan has been designed to ensure effective communication with all co-mentors and undergraduate researchers on the MURI team?

7) What measureable outcomes and benefits will this research provide to the students, you

and your co-mentor(s), your department, and your school?

8) What is the timeline for the major tasks associated with this proposal?

9) Please provide a rationale for your budget request. (NOTE: The maximum budget allowance is $2,000 for equipment and/or supplies needed for the research team. Generally speaking, expenditures for computers and/or travel are not approved by the review committee at this time due to financial constraints.)

10) Please describe your plan for sustaining your research beyond the funding that MURI is

able to provide. (For example, please list other external grants that have been or will be submitted as a follow-up to your MURI funding.)

11) Please identify any areas relevant to risk management.

All university policies with respect to research must be followed. The usual risk management assurances must be provided where appropriate (animal use, radiation safety, DNA, human subjects protocols) in accordance with the university policies. No funds may be released without risk-management assurances, where needed. Project proposals without required compliance approvals will be reviewed but the funds will not be released until approval is given by the university. Further information on risk management is available from http://researchadmin.iu.edu/cs.html

http://researchadmin.iu.edu/cs.html

Section III: Body of Proposal

Overview and Objectives

Electronic analog computers were the primary computing platform in the first half of the 20th century. They use the intrinsic values of electronic components to continuously implement differential equations. Their prime advantage lies in their relative simplicity, speed and low power consumption. In effect, the preamplifier and analog filters used in the head stage and signal conditioning modules of bioelectric amplifiers are simple electronic analog computers. In the 2nd half of the 20th century, they have largely been supplanted by digital electronic computers due to their numerical accuracy, reconfigurability through programming, that overcame some of the limitations of the early electronic analog computer. These early computers were sensitive to noise and temperature. The precision of the result relied on the stability of the value of the electronic components, which changed with use. Moreover, programming of the computer required physical hardware additions and structural changes. Thus, they were difficult to program and maintain, and could not be implemented as a general purpose configurable computing platform. Finally, given that physical components and circuitry needed to be altered to program the computer, they were difficult to scale down in size.

The present project aims to address this technological gap by advancing a hybrid technology that leverages the nearly instantaneous computational power of potential manifolds with digitally programmable, sampled time system: the extended analog computer (EAC). In preliminary collaborative work, Drs Yoshida and Mills elucidated the fundamental equations governing the EAC and developed a simplified EAC framework which we call the Extended Voltage Manifold Computer (EVMC). With EVMC modules, the means to implement arbitrary finite impulse response and infinite impulse response filters was further developed. The present project aims to further develop the EVMC, focusing upon methods to train EVMC modules using automatic machine learning techniques. To bring focus to the project, development of the EVMC will be using signals originating from neuroprosthetic electrode arrays. Although limited to a specific application and class of problem the application isidentical to and generalizes to processing of other bioelectrically active tissues that rely on interpretation of single unit activity; such as intracortical [1]–[5],dorsal root ganglion [6], [7], and skeletal muscles [8] signals.

The EVMC addresses a key missing but necessary component for the translation of advanced multi-channel neuro / muscular techniques outside of the laboratory to mobile neuroprosthetic applications; a downscale-able means to decipher and process the raw neural signal. Current conventional methods involving DSP lack sufficient processing power and are not amenable to the massively parallel and relatively time-critical task this problem presents. This project aims to address this problem by applying the EVMC to this task.

Specific Aim: Explore automatic machine learning methods to develop a robust automatic means to train EVMCmodules and networks to perform neural unit spike detection and classification.

The Extended Analog ComputerIn 1993 after Lee A. Rubel analyzed thedecision-making processes in the brain [9],he created a new theoretical model ofmulti-dimensional sensory inputs in uncertaindecision processes that he called theExtended Analog Computer (EAC) [10]. Thejustification that Rubel gave, of a baseballplayer who sees, runs toward and catches along fly ball on a windy day, is outside thescope of Shannon’s General Purpose AnalogComputer (GPAC). In 1995 Mills began tobuild restricted versions of the EAC. It wasfound to represent a new computationalparadigm that required new techniques toapply. Our experience also indicates thatsilicon VLSI implementations of the EAChave the potential to deliver not teraFLOPs,but teraPDEs, the solution to trillions of partial differential equations per second [11].

The EAC prototype developed by Mills and co-workers is deceptively simple. It is a small seven-inch square board. The top of the EAC contains a matrix of 25 current / voltage sources and sinks, the input/output points of

2013 IUPUI MURI proposal – Yoshida & Salama 1

Figure 1: An implementation of the Extended Analog Computer (Left).The device is configured through the USB port of the notebook. The

current density distribution and manifold potentials alter as sources orsinks are added or repositioned. Each of the three cases shown here

represent different solutions to a problem posed to the EAC.

the device arranged on a conductive sheet or thin-film (Fig 1). Each point is digitally programmable to source or sink current, or measure the sheet potential or current. These are driven by an on-board microprocessor that receives configuration commands through a USB port. The conductive sheet of the EAC can be connected to a variety of conductive materials; in our current prototypes plastic foam and conductive polymers are used. VLSI sheets and three-dimensional colloids have also been used [12]. The operation of the EAC is inherent in the mathematical principles of natural materials, which are selectively enhanced or inhibited using the continuous logic of Lukasiewicz [13].

Theoretically, functions including arithmetic, inversion, integration, interpolation, ordinary and partial differentiation, limits, and minor error correction are possible. This computational power is so complex that it requires an automated search of the EAC function space to locate. Paradoxically, the EAC is so well adapted to evolved solutions that they may be found using particle swarm optimization (PSO) [14], [15] in a few iterations.

The EAC was developed to solve multivariate spatial problems. Solutions to the problems are solved by adjusting the location and numbers, positions and amplitudes of current sources injecting or sinking current onto a conductive space using machine learning optimization techniques such as PSO. The changes in location of sources and sinks adjust how the current distributes itself in the space, which come as solutions to Poisson's equation. Once configured, the solutions to the problems posed to the EAC form nearly instantaneously, as soonas the current density distribution forms on the conductive sheet. Non-linearities can be further added by recursion of outputs back onto the manifold sheet through logic functions. The EAC was developed from arrays of analog logic circuits (Lukasiewicz Logic Arrays, LLAs), which, in turn, are extensions of resistive meshes. For example, LLAs can act as retinas that detect edges moving horizontally, vertically and diagonally, something thata resistive mesh cannot do inherently. They, however, were not designed to directly take inputs that are a function of time, such as raw nerve recordings.

The Extended Voltage Manifold Computer (EVMC)The voltage manifold and spatial calculations

There are four possible modes in which Rubel and Mills' EAC can operate: voltage input to voltage manifold, current input to current density manifold, voltage input to current density manifold and current input to voltage manifold. We restricted our research to the current input to voltage manifold case, and refer to EACs using this restricted case as the Extended Voltage Manifold Computer (EVMC). Enforcing this limitation bounds the EAC in two important ways. 1) The inputs and the outputs of the EVMC do not alter the total conductivity of the space. This restricts the space to a constant passive conductivity defined by the electrical properties of the media. 2) The addition of input or output points does not alter the current density distribution or potential manifold generated by any other input or output. Thus, each source and the voltage manifold it creates in the media becomes independent of the manifold of any other source.

The EVMC can be described as a finite number of discrete, time varying current sources and a finite array of discrete, distributed measurement points distributed in a conductive space. At any given moment in time, the potential measured by the any given measurement point is the sum of the potentials resulting from each of the active current sources. Each measurement site can furthermore be described as a point in an infinitely volume conductor, and the potential it sees as simply the sum of the by (eq.1), where v(t) is the measured potential at time t; in(t) is the current at the n-th source; σ is the conductivity of the volume conductor; and zn is the distance of the n-th current source at a specific point in the manifold space. For a uniform volume conductor, this expression becomes strictly a function of the current at each node and a constant inversely proportional to the

distance between the source and the point in space, or v (t )=∑n=0

N−1

bni n(t) (eq.2) where bn is a constant related

to distance and the conductivity of the volume. This equation also describes the relationship of a set of current sources and the potential measured on the manifold at a particular location in the conductive space. The potential is the sum of the input current functions multiplied by a constant for each current source that is defined by the distance between the current source and measurement point as shown in the left panel of Fig 2. Positive and negative weights are represented by defining two measurement points and taking their difference. In the right panel of Fig 2 the current source inputs above the dotted line have coefficients defined positive definite, while those below the dotted line have negative coefficients. Those points on the dotted line, the locus of points of equal distance between the measurement points V1 and V2 have zero valued coefficients.


Extension to time domain signals

The original concept of the EAC took the form of a general purpose spatial processor. Processing of real- time functions in the time domain was not defined. A second modification of the EAC was defined and implemented [16] to take one of the spatial dimensions of the EAC and use it to capture time varying data using a tapped delay line. The input data is sampled and presented through a transconductance amplifier and a clocked array oftransmission gates. These gates propagate the sampled data at each clock cycle, stored as an analog charge at each sample and hold gate. Sequencing the tapped delay line's gating sequence propagates the data of the sampled time instances down an analog first in first out (FIFO) bank at a propagation velocity equal to the

sampling time delay. This is described by v (t)=∑n=0

N−1

bn i(t−n τ) (eq.3); where τ is the sampling interval and

constraining the input function to that of a single traveling wavelet. Each input current, in this case, is related to the input current at a previous point in time. By making each current source related to each other by integer multiples of a time delay, we have effectively traded a spatial dimension and assigned it to a time point. Next, by regularly sampling time at a fixed rate, the continuous time is converted to discrete time and the equation

becomes v (m)=∑n=0

N−1

bn i(m−n) (eq.4). This governing equation is identical to that of a Finite Impulse

Response (FIR) filter. More importantly, it has the implication that any arbitrary FIR filter, limited only by the filter order and density of sources, can be defined on the EVMC. Architecturally, eq.4 can be implemented by time


Figure 2: Illustration of twosource sensor layout strategies

for the EVMC. Both panels showsquare uniform conductive

sheets populated with multiplesources and one or two sensing

points. The left panel is anillustration of eq.2. The

coefficient for each inputcontributes to the potential seenat the detection point by a factorof bi which is inversely related to

the distance ri. Weights in theleft panel can only be defined

positive definite. Negativeweights can be assigned by

using a differential measuringmethod shown in the right panel.

V 1,n−V 2,n=14 πσ

(1r1,n

−1r2,n

)i n

Figure 3: Schematic architectural representation of the EVMC implementing a Finite Impulse Response filter (Left) orthe more general Direct form I FIR-IIR filter (Right). Tapped delay lines hold the present and past input and outputfunction values, which are transformed to current and injected into the EVMC conductive sheet (shown in blue).

sampling the input waveform and passing the sampled waveform down a tapped delay line. Each delayed input is converted to a current through a transconductance buffer and injected into the EVMC conductive sheet at points to implement the desired coefficient. The sheet sums the potential manifold from each input current and the resulting potential is measured by the sensing point or differential measurement points. This architecture is shown in the left panel of Fig 3.

By placing the tapped delay line on the output instead of at the input, and feeding back the values of the tapped delay line through transconductance amplifiers back into the voltage manifold space it is possible to implement an auto-regressive or Infinite Impulse Response (IIR) filter. In combination with the FIR circuit, a combined FIR-IIR filter in the Direct Form I can be constructed, as shown in the right panel of Fig 3. Thus, given these three implementations of the EVMC, any arbitrary digital filter or transfer function can be implemented and deterministically configured.

Approach

The preliminary work has developed methods to understand the voltage manifold and develop an architecture to implement linear filters with the EVMC platform. The concept of EVMC was validated using finite element method (FEM) simulations and using a non-real-time hardware implementation of the EAC. They demonstrated that the theory that was developed accurately and precisely predicted and actual behavior of the physical device.When applied to a specific neural signal processing task, they demonstrated signal processing steps for improving the signal to noise ratio and linear shape classification techniques can be implemented on the EVMC. However, these did not demonstrate the potential for computation speed or take advantage of the families of solutions that exist on the EVMC manifold. The project will leverage Dr Salama's expertise in advanced signal processing and recursive optimization methods to define the error space and explore automatic optimization methods to locate the number and positions of measurement points needed.

In general, the procedures taken will be the development of the theory of operation followed by computerized finite element model simulations of the EVMC modules and algorithms. Neural recordings taken from the trainingdata sets from the animal experiments will be used to develop algorithms. Three sets of data will be defined. Theraw multichannel recordings will comprise one set. Two other sets will be formed by using current methods to isolate the waveforms of single nerve units. Noise free templates of these units will be captured by spike triggered averaging clearly identifiable units to create sets of noise free unit templates. The total set of unit templates will then be divided randomly into two sets. The first set will be used to synthesize simulated multi-channel multi-unit nerve activity where the firing time instances of all units in the recording are known. Noise will be added by adding recordings of ambient noise made during the animal experiments to simulate realistic nerve recordings. A second set of synthesized recordings will be used to define the performance test set. Once an algorithm is developed, they will be tested against a test data set. Two specific research problems were identified to develop networks of EVMC modules: 1) the development of methods to train the EVMC modules and 2) the development of strategies to realize input and output locations on the manifold sheets. These two problems are developed as Aims 1 and 2.

Aim 1) Explore automatic machine learning methods to train EVMC modules and networks of EVMC modules to perform neural unit spike detection and classification.

Hypotheses: a) EVMC modules configured as sheets or networks of sheets can detect and classify single fiber nerve activity. b) The performance of automatically configured EVMC modules can generalize to meet or exceedthe classification performance of deterministically configured linear methods.

The EVMC module comprises of a set of input current sources and the set of output voltage measurement

points. Each output voltage is governed by eq.1, or restated v (x )= 14 πσ

∑j=1

N1

∥r j∥i j (x ) . The output voltage is

strictly a function of the sum of a constant weighting function defined by the Euclidean distance between each input and the output and the magnitude of each input current, and is described by a class of functions called radial basis functions (RBF). In the case of the EVMC, the kernel function is a simple hyperbolic function. More importantly, a class of neural network can be constructed using RBF modules, the radial basis function network (RBFN). Thus multi-layer perceptron networks can be constructed by linking multiple EVMC modules where each manifold sheet of the EVMC becomes a single layer in the RBFN. Training of the RBFN can be implemented using back propagation using a linear descent error operator [17], [18] but adaptation is needed to implement the algorithm for our kernel function. Training the EVMC-RBFN will involve recursively running finite element method (FEM) simulations of EVMC modules as the algorithm modifies the locations of the input and


output points of a randomly initialized the manifold sheet. Repetition of the process is necessary to ensure that a global minimum of the error function is reached. Once a training algorithm is formalized, particle swarm optimization (PSO) can be introduced to the training algorithm to increase the speed of training.

Initially, single layer EVMC-RBFNs will be explored. Their performance compared against the deterministic linearmethods (matched filter and PCA) and standard single layer linear perceptrons implemented in Matlab. These results will be used to hypothesis 1a. We expect equal performance between the linear deterministic method, thelinear perceptron and the single layer EVMC-RBFN. Next, the number number of layers and input sources will beincreased to test the performance between two layer EVMC-RBFNs will be defined and trained. We expect greater generalizability and thus higher detection/classification performance with the network based methods.

The MURI team will develop machine learning techniques to automatically configure the EVMC. The Comsol FEM simulation tools will be used to explore and adapt Swarm and radial basis function network training techniques to automatically configure the EVMC. The in-silico solutions will be tested against a real-time implementation of the EVMC. The outcomes of this work will then be used to support a grant proposal to the NSF.

MURI Project Management

To facilitate communication between the co-mentors and the undergraduate researchers, an Oncourse project site will be requested and used as the project information repository and primary communication tool. The Forumtool will be used as the official communication pathway and communication archive in the project. All participantswill be required to enable forum notifications and watch for all threads and postings. The undergraduate researchers will be requested to assign 3 officers amongst themselves: a general project leader to coordinate management of the research activities, a project accounts manager to administer the research budget and purchases, and communications officer to act as the single point of contact for external communications. Biweekly meetings with the co-mentors will take place on Mondays and Fridays during the project period. Monday meetings will be used to plan activities for the week, while Friday meetings will be used to report upon progress during the week.

The project is designed to develop and reinforce team group work in research. The participation in both parts of research, management and technical, will be imposed upon the group. The technical skills of computer programming, signals and systems, and biophysics will be reinforced and integrated within the context of neural signal processing. These activities will aim to produce as deliverables a project report consisting of the technical and management activities in the project, and participation in the Undergraduate Research Day with a poster presentation of the project. We will aim to publish the results of the research activities, initially as a conference paper at either the IEEE-EMBS or BMES meeting. As with the previous MURI project, it is anticipated that the preliminary work developed by the MURI team will become the seed of a Masters or PhD thesis project. We alsohope to identify potential future candidates to take graduate research positions for future projects within the lab.

Budget

The project involves programming and signal processing using Matlab and LabView. Research licenses for thesetwo software titles are requested to complete this project.

Risk management

This project will involve development of swarm and gradient descent training techniques for the Extended AnalogManifold Computer. All development will take place in-silico or with a realized benchtop prototype EAMC device. The work will not involve any animal work, human subjects, pathogens, recombinant techniques, radiation or other hazards.


Software Title Cost Source$100 Stat Math Center

Signal processing toolbox $30 Stat Math CenterWavelet toolbox $30 Stat Math Center

System Identification toolbox $30 Stat Math CenterControl toolbox $30 Stat Math Center

Optimization toolbox $30 Stat Math Center

$1,400PCB Fabrication $400 SOIC

Total $2,050

Matlab (base)

Comsol Subscription Comsol

Section IV: References

[1] P. K. Campbell, K. E. Jones, R. J. Huber, K. W. Horch, and R. A. Normann, “A silicon-based, three-dimensional neural interface: manufacturing processes for an intracortical electrode array.,” IEEE Trans. Biomed. Eng., vol. 38, no. 8, pp. 758–768, Aug. 1991.

[2] A. Branner, R. B. Stein, E. Fernandez, Y. Aoyagi, and R. A. Normann, “Long-term stimulation and recordingwith a penetrating microelectrode array in cat sciatic nerve,” IEEE Trans BiomedEng, vol. 51, no. 1, pp. 146–157, Jan. 2004.

[3] A. Branner and R. A. Normann, “A multielectrode array for intrafascicular recording and stimulation in sciatic nerve of cats,” Brain ResBull, vol. 51, no. 4, pp. 293–306, Mar. 2000.

[4] K. J. Otto, P. J. Rousche, and D. R. Kipke, “Microstimulation in auditory cortex provides a substrate for detailed behaviors,” Hear.Res., vol. 210, no. 1–2, pp. 112–117, Dec. 2005.

[5] D. J. Anderson, K. Najafi, S. J. Tanghe, D. A. Evans, K. L. Levy, J. F. Hetke, X. L. Xue, J. J. Zappia, and K. D. Wise, “Batch-fabricated thin-film electrodes for stimulation of the central auditory system,” IEEE Trans BiomedEng, vol. 36, no. 7, pp. 693–704, Jul. 1989.

[6] Y. Aoyagi, R. B. Stein, A. Branner, K. G. Pearson, and R. A. Normann, “Capabilities of a penetrating microelectrode array for recording single units in dorsal root ganglia of the cat,” J.Neurosci.Methods, vol. 128, no. 1–2, pp. 9–20, Sep. 2003.

[7] D. J. Weber, R. B. Stein, D. G. Everaert, and A. Prochazka, “Limb-state feedback from ensembles of simultaneously recorded dorsal root ganglion neurons,” JNeural Eng, vol. 4, no. 3, pp. S168–S180, Sep. 2007.

[8] D. Farina, K. Yoshida, T. Stieglitz, and K. P. Koch, “Multichannel thin-film electrode for intramuscular electromyographic recordings,” J. Appl. Physiol. Bethesda Md 1985, vol. 104, no. 3, pp. 821–827, Mar. 2008.

[9] L. A. Rubel, “The brain as an analog computer,” J. Theor. Neurobiol., vol. 4, no. 2, pp. 73–81, 1985.[10] L. A. Rubel, “The Extended Analog Computer,” Adv. Appl. Math., vol. 14, no. 1, pp. 39–50, Mar. 1993.[11] J. W. Mills, M. Parker, B. Himebaugh, C. Shue, B. Kopecky, and C. Weilemann, “‘Empty space’ computes,”

2006, pp. 115–126.[12] J. W. Mills, “Polymer processors,” Technical Report TR580, Department of Computer Science, University of

Indiana, 1995.[13] J. W. Mills, “The nature of the Extended Analog Computer,” Phys. Nonlinear Phenom., vol. 237, no. 9, pp.

1235–1256, Jul. 2008.[14] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” pp. 39–43.[15] J. F. Kennedy, R. C. Eberhart, and Y. Shi, Swarm intelligence. San Francisco: Morgan Kaufmann

Publishers, 2001.[16] M. Soliman, “Developing a neural signal processor using the extended analog computer,” M.S., Indiana

University - Purdue University Indianapolis, Indianapolis, IN, 2012.[17] S. Chen, C. F. N. Cowan, and P. M. Grant, “Orthogonal least squares learning algorithm for radial basis

function networks,” IEEE Trans. Neural Networks, vol. 2, no. 2, pp. 302–309, Mar. 1991.[18] P. V. Yee and S. S. Haykin, Regularized radial basis function networks : theory and applications. New York:

John Wiley, 2001.


Section V: Cvs/Resumes

Curriculum vitae of Ken Yoshida and Paul Salama are attached.

Section VII: Appendix

Past MURI Mentoring Activities

Title of Past MURI Project:

Towards estimating the position of the leg using sensory information intercepted by neuroprosthetic electrodes

Date Awarded: Summer 2012

Date Completed: Summer 2012

Description:

The project aims to merge two lines of research taking place within the Bioellab (Dr Yoshida's lab):

1. Development of a robotic end point effector

2. Development of neural signal processing algorithms.

The first phase of the research will concentrate on work with the robotic end-effector. The second phase of the research will concentrate on the analysis of the neural signal processing algorithm. To accomplish this, a multi-disciplinary team with interest in mechanics, signal processing, computer programming, neural electrophysiology, and rehabilitation is required.

Student Learning Outcomes:

Group management and learning. Mechanics of the leg, and measurement of all 6 mechanical DOFs. Programming Matlab GUIs, digital signal processing using SWT.

Poster presentations:

Title: Measurement of the Limb Segment Mechanics of the Leg Using a Robotic Endpoint EffectorDate: July 2012Students Involved: Jimmy G. Corcoran, Daniel L. French, Eric R. Wolf

Title: Graphical Neural Signal Processing And Analysis Framework For Electroneurograms Detected By Intrafascicular ElectrodesDate: July 2012Students Involved: Thawngzapum Lian, Shaoyu Qiao

Title: Measuring the kinematics and biomechanics of the leg during endpoint manipulationDate: Nov 2012Students Involved: Alec Willard

Title of Past MURI Project:

Development of a biofeedback testing platform for evaluating sensory feedback and volition through an advanced neuroprosthetic device

Date Awarded: Oct 2007

Date Completed: Dec 2008

Description:

The project aims to design, develop and implement a psychophysical testing protocol and instrumentation to efficiently quantify and map the volitional intentions and sensory feedback perceived by a subject following multi-channel surface stimulation and recording. The system will be the first step towards developing a method to be used to evaluate an amputee subject interacting with a neuroprosthetic electrode implanted in the amputee’s peripheral nerve. It further aims to introduce tools and concepts across disciplines to the MURI


scholars through cross pollination and group work on a multidisciplinary project involving psychophysics, bioinstrumentation, and object oriented software design

Outcomes:

The protocol, instrumentation and software developed by the MURI team served as the basis of a successful application to the European Commission (TIME) 2008-2012. It formed the initial starting point for a PhD student thesis project. Bo Geng, the PhD student from Aalborg University is co-mentored by Dr Yoshida and Dr Jensen (Aalborg University), and has published 2 full papers, and 5 conference papers (listed below).

Publications:

Title: Development of a biofeedback testing platform for evaluating sensory feedback and volition through an advanced neuroprosthetic deviceDate: 12/18/2008Students Involved: David Sempsrott, Brandon Brungard, Sriharsha Muttineni, Magali Carret

GENG, B. , YOSHIDA, K., PETRINI., L., JENSEN, W., “Evaluation of sensation evoked by electrocutaneous stimulation on forearm in nondisabled subjects ”, JRRD, 49(2), pp. 297- 308, 2012DOI: 10.1682/JRRD.2010.09.0187

GENG, B. , YOSHIDA, K., JENSEN, W., “Impacts of selected stimulation patterns on the perception threshold in electrocutaneous stimulation”, Journal Neural Engineering Rehabilitation, 8(9), pp. 1-10, 2011.

DOI: 10.1186/1743-0003-8-9GENG, B. , YOSHIDA, K., JENSEN, W., (2011) “A case study on phantom sensation and sensory discrimination

induced by electrocutaneous stimulation”, Neuroscience 2011, Washington DC, 897.18/GG32.GENG, B. , HARREBY, K.R., KUNDU, A., YOSHIDA, K., BORETIUS, T., STIEGLITZ, T., PASSAMA, R., GUIRAUD, D.,

DIVOUX J.L., BENVENUTO, A., DIPINO, G., ROSSINI, P.M., JENSEN, W., (2011) “Development of a Psychophysical Testing Platform – a computerized tool to control, deliver and evaluate electrical stimulation to relieve phantom limb pain”, Nordic-Baltic Conf on BME and Medical Phys, Aalborg, Denmark.

GENG, B. , YOSHIDA, K., JENSEN, W., (2010) “Effects of the number of pulses on evoked sensations in pairwise electrocutaneous stimulation”, IFESS2010, Vienna Austria, paper 67.

GENG, B. , YOSHIDA, K., JENSEN, W., (2010) “Psychophysical evaluation of the effect of electrode location on sensations during electrocutaneous stimulation”, ISEK2010, Aalborg, Denmark. (submitted 1.10)

GENG, B. , YOSHIDA, K., JENSEN, W., (2009) “Effects of stimulus patterns on sensory thresholds in dual-channel electrocutaneous stimulation”, Soc. Neuroscience Abstr., Chicago, IL


http://dx.doi.org/10.1186/1743-0003-8-9

http://dx.doi.org/10.1682/JRRD.2010.09.0187

BIOGRAPHICAL SKETCHProvide the following information for the Senior/key personnel and other significant contributors in the order listed on Form Page 2.

Follow this format for each person. DO NOT EXCEED FOUR PAGES.

NAME

Yoshida, KenPOSITION TITLE

Associate Professor of Biomedical EngineeringeRA COMMONS USER NAME (credential, e.g., agency login)

kyoshidaEDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training and residency training if applicable.)

INSTITUTION AND LOCATIONDEGREE

(if applicable)MM/YY FIELD OF STUDY

University of California, San Diego 06/86 Pre-AMESUniversity of California, Los Angeles B.S. 06/89 Biomedical EngineeringUniversity of Utah, Salt Lake City, UT Ph.D. 12/94 Biomedical EngineeringUniversity of Alberta, Edmonton, AB, Canada Postdoctoral 04/98 Neuroscience

A. Personal StatementThe nervous system can be viewed as the substrate upon which consciousness and our ability to manipulateand interact with our environment resides. I view the capturing and understanding of the electrical activity ofthe nervous system as the gateway to understanding the systems, states and processes that enable us to behuman. From that standpoint, advanced neuroprosthetic devices are the means to that world within us. Appliedto those who suffered loss of function of that substrate through injury or disease, they hold the key to replacingthe lost function by acting as an artificial bridge, given that we can understand and interpret how and what thenervous system signals. A critical technological component in that chain is the bioamplifier used to amplify andenable capturing of the bioelectrical activity of the body. However, a major challenge that stands in the waybetween translation of advanced neuroprosthetics and practical clinical use is the high performance yetlow-power consuming, portable signal processing computer needed to extract and interpret the neural trafficpicked up by the nervous system. This need led me to explore unconventional computing techniques, to DrMills' Extended Analog Computer and the development of the Extended Voltage Manifold Computer.In 15 years of independent research in the field, I have carried out development of electrode structures,bioelectric interfaces, computer modeling, signal processing and interpretation of the neural data stream inacute and chronic animal work. The work was carried out through a series of successful completed researchgrants at the national (Canadian & Danish) and European levels. I have 20 years of experience with electricalstimulation and recording through implanted and surface neural and muscle based interfaces in animal modelsranging from worms to pigs, and 10 years of experience with electrical stimulation and biomechanical /bioelectrical characterization of muscle activity in humans.

B. Positions and HonorsPositions and Employment04/98-12/00 Research Assistant Professor, Center for Sensory-Motor Interaction, Aalborg University, Denmark01/01-09/01 Assistant Professor, Center for Sensory-Motor Interaction, Aalborg University, Denmark10/01-10/06 Associate Professor, Dept of Health Science and Technology, Aalborg University, Denmark10/06-present Associate Professor, Dept of Biomedical Engineering, Indiana Univ.-Purdue Univ. Indianapolis

Other Professional Experience and Membership1989- IEEE Engineering in Medicine and Biology Society (Senior Member)1989- Biomedical Engineering Society (Member)1995- International Functional Electrical Stimulation Society (Charter Member)1998- Society for Neuroscience (Member)2010- Veterans Administration RR&D RRD7 grant panel, Ad-Hoc Reviewer.

Honors1994-1997 Canadian Network of Centres of Excellence, NeuroScience Network Fellow.1995-1998 Alberta Heritage Foundation for Medical Research Fellow.2002 Hede-Nielsens Family Foundation Award for Research in Bioelectronics2003 Sygekassernes Helsefond Young Investigator Award

C.Selected peer-reviewed publications (Selected from 52 peer-reviewed journal publications)Most relevant to the current applications1. Qiao, S., Torkamani-Azar, M., Salama, P., Yoshida, K., “Stationary Wavelet Transform and Higher Order

Statistical Analyses of Intrafascicular Nerve Recordings”, JNE, 9(5)2. Qiao S., Odoemene, O., Yoshida, K., “Determination of electrode to nerve fiber distance and nerve

conduction velocity through spectral analysis of the extracellular action potentials recorded from earthwormgiant fibers”, MBEC,50(8), pp.867-875, 2012.

3. Qiao, S., Yoshida, K., "Influence of unit distance and conduction velocity on the spectra of extracellularaction potentials recorded with intrafascicular electrodes", MEP, 2012.

4. Kamavuako, N., Jensen, W., Yoshida, K., Kurstjens, M., Farina, D., “A criterion for signal-based selectionof wavelets for denoising intrafascicular nerve recordings”, J Neurosci Met, 186(2), pp. 274 – 280, 2010.

5. Micera, S.M., Rossini, P.M., Rigosa, J., Citi, L., Carpaneto, J., Raspopovic, S., Tombini, M., Cibriani, C.,Assenza, G., Carrozza, M.C., Hoffmann, K.P., Yoshida, K., Navarro, X., Dario, P., “Decoding of graspinginformation from neural signals recorded using peripheral intrafascicular interfaces”, JNER, 8(53), 2011.PMCID: PMC3177892

6. Djilas, M., Azevedo-Coste, C., Guiraud, D., Yoshida, K., “Spike sorting of muscle spindle afferent nerveactivity recorded with thin-film intrafascicular electrodes”, Comp Intel Neurosci., 2010 PMCID:PMC2847763

7. Yoshida, K, Farina, D, Akay M, Jensen W, “Multi-channel intraneural and intramuscular techniques formulti-unit recording and use in active prostheses”, ProcIEEE, 98(3), pp. 432 – 449, 2010.

8. Micera, S., Citi, L., Rigosa, J., Carpaneto, J., Raspopovic, S., DiPino, G., Rossini, L., Yoshida, K., Dario, P.,Rossini, P.M., “Decoding sensory and motor information from neural signals recorded using intraneuralelectrodes: towards the development of a neurocontrolled hand prosthesis”, ProcIEEE, 98(3), pp. 407 -417, 2010.

9. Djilas, M., Azevedo-Coste, C., Guiraud, D., Yoshida, K., “Interpretation of muscle spindle afferent nerveresponse to passive muscle stretch recorded with thin-film longitudinal intarafascicular electrodes”,IEEEtNSRE, 17(5), pp. 445-453, 2009.

10. Citi, L., Carpaneto, J., Yoshida, K., Hoffmann, K.P., Koch, K.P., Dario, P., Micera, S., “On the use of waveletdenoising and spike sorting techniques to process ENG signals recorded using intra-neural electrodes”, J.Neu.Sci. Meth, 172, pp.294-302, 2008.

11. Farina, D., Yoshida, K., Stieglitz, T., Koch, K.P., “Multi-Channel Thin-Film Electrode for IntramuscularElectromyographic Recordings”, J App. Physiol., 104(3), pp.821-827, 2008.

Additional recent publications to the field (in chronological order)Yoshida, K., Qiao S., Himebaugh, B., Soliman, M., “Extended Analog Computer Apparatus”, US Provisional

Patent Application No. 61/680,077, Filed 6 Aug 2012.Yoshida, K., Qiao S., Himebaugh, B., “Extended Analog Computer Apparatus”, US Provisional Patent

Application No. 61/606,741, Filed 5 Mar 2012.

Principal Investigator/Program Director (Last, first, middle): Salama, Paul

BIOGRAPHICAL SKETCH

NAME Paul Salama, Ph.D. eRA COMMONS USER NAME psalama

POSITION TITLE Professor

EDUCATION/TRAINING

INSTITUTION AND LOCATION DEGREE (if applicable)

YEAR(s) FIELD OF STUDY

Purdue University, West Lafayette, IN, USA MSEE. 1992-1993 Electrical Engineering Purdue University, West Lafayette, IN, USA Ph.D. 1994-1999 Electrical Engineering

A. Positions and Honors.

Professional positions 1999-2005: Assistant Professor of Electrical and Computer Engineering, Department of Electrical and Computer Engineering, Indiana University – Purdue University, Indianapolis, IN 2005 – 2013: Associate Professor of Electrical and Computer Engineering, Department of Electrical and Computer Engineering, Indiana University – Purdue University, Indianapolis, IN, Indianapolis, IN 2013 – present: Professor of Electrical and Computer Engineering, Department of Electrical and Computer Engineering, Indiana University – Purdue University, Indianapolis, IN, Indianapolis, IN

Awards and other professional activities

Award: Senior Member of the IEEE, 2005 Associate Editor: IEEE Transactions on Circuits and Systems for Video Technology Ad hoc reviewer: The IEEE Transactions on Pattern Analysis and Machine Intelligence; The IEEE Transactions on Circuits and Systems for Video Technology; The IEEE Transactions on Image Processing; The IEE Proceedings on Vision, Image and Signal Processing; Real Time Imaging Journal; The IEEE Communications Letters; The IEEE International Conference on Multimedia and Expo; The IEEE International Multi-Conference on Systems, Cybernetics and Informatics; The IEEE International Conference on Acoustics, Speech, and Signal Processing; The IEEE International Conference on Image Processing Ad hoc reviewer: NSF

B. Selected peer-reviewed publications (in chronological order).

S. Qiao, M. Torkamani-Azar, P. Salama, and K. Yoshida, “Stationary Wavelet Transform and Higher Order Statistical Analyses of Intrafascicular Nerve Recordings”, Journal of Neural Engineering, vol. 9, 2012. K. S. Lorenz, P. Salama, K.W. Dunn, E.J. Delp, “A Multi-Resolution Approach to Non-Rigid Registration of Microscopy Images,” Proceedings of IEEE International Symposium on Biomedical Imaging, Barcelona, Spain, 2-5 May 2012.

K. S. Lorenz, P. Salama, K.W. Dunn, E.J. Delp, "Digital Correction of Motion Artifacts in Microscopy Image Sequences Collected from Living Animals Using Rigid and Non-Rigid Registration," Journal of Microscopy, Volume 245, no. 2, pages 148–160, February 2012

Robert G. Presson, Jr., Mary Beth Brown, Amanda J. Fisher, Ruben M. Sandoval, Kenneth W. Dunn, Kevin S. Lorenz, Edward J. Delp, Paul Salama, Bruce A. Molitoris, and Irina Petrache, “Two-Photon Imaging within the Murine Thorax without Respiratory and Cardiac Motion Artifact” The American Journal of Pathology, vol. 179, no. 1, pp. 75-82, July 2011. K. Lorenz, P. Salama, K. W. Dunn, E. Delp, “Non-Rigid Registration of Multiphoton Microscopy Images Using B-Splines,” Proceedings of the SPIE Conference on Medical Imaging, 12-17 February, 2011, Orlando, Florida, USA. R. Mack, M. Rizkalla, P. Salama, M. El-Sharkawy, “VLSI Implementation for Low Noise Power Efficiency Cellular Communication Systems,” Wireless Sensor Network, vol. 2010, no. 2, pp. 18-30. S. Assegie, P. Salama, and B. King, “An Attack on Wavelet Tree Shuffling Encryption Schemes,” Security-Enriched Urban Computing and Smart Grid, Communications in Computer and Information Science, pp. 139-148, vol. 78, 2010. L. Siruvuri, P. Salama, and D. Kim, “Adaptive Error Resilience for Video Streaming,” International Journal of Digital Multimedia Broadcasting, Special Issue on Advances in Video Coding for Broadcast Applications, vol. 2009, no. 681078, pp. 29-38. L. Liang, P. Salama and E. J. Delp, “Unequal Error Protection Techniques Based on Wyner--Ziv Coding,” EURASIP Journal on Image and Video Processing, Special Issue on Distributed Video Coding, vol. 2009, no. 474689, pp. 112-124. K. Lorenz, F. Serrano, P. Salama, E. J. Delp, “Segmentation and Registration Based Analysis of Microscopy Images,” Proceedings of the IEEE International Conference on Image Processing, 7-9 November, 2009, Cairo Egypt. K. Lorenz, F. Serrano, P. Salama, E. J. Delp, “Analysis of Multiphoton Renal and Liver Microscopy Images: Preliminary Approaches to Segmentation and Registration,” Proceedings of the Workshop on Microscopic Image Analysis with Applications in Biology, 3-4 September, 2009, Bethesda, MD, USA. L. Liang, P. Salama, and E. J. Delp, “Feedback Aided Content Adaptive Unequal Error Protection Based on Wyner-Ziv Coding,” Proceedings of the 27th Picture Coding Symposium, 6-9 May 2009, Chicago, Illinois, USA. M. Torkamani-Azar, E. N. Kamavuako, P. Salama, and K. Yoshida, “Multi-scale and higher order statistical analyses of intrafascicular nerve recordings,” Proceedings of the Annual Conference of the International Functional Electrical Stimulation Society (IFESS), September 21-25, 2008, Freiburg, Germany

P. Salama, “A Least Squares Approach to Estimating the Probability Distribution of Unobserved Data in Multi-photon Microscopy,” SPIE International Conference on Computational Imaging VI, January 28 – 31, 2008, San Jose, CA.


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Section IV: References/Bibliography (insert 1-2 pages as needed)

Section V: CVs/Resumes (insert 2 pages per mentor for a maximum of 6 pages)

Section VI: Support Letters (insert 1- 2 pages as needed)

Section VII: Appendix

Section VIII: Signature

Name and Signature of the Principal Mentor:

Ken Yoshida 23 August 2013

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