BMEN 5413 -- New Course

Mon Sep 26 09:37:35 2011

Approvals Received:
Department
on 09-21-11
by Rachel Jorgenson
(boehm040@umn.edu)
Approvals Pending: College/Dean  > Catalog > CCE Catalog
Effective Status: Active
Effective Term: 1119 - Fall 2011
Course: BMEN 5413
Institution:
Campus:		 UMNTC - Twin Cities
UMNTC - Twin Cities
Career: UGRD
College: TIOT - College of Science and Engineering
Department: 11143 - Biomedical Engineerng, Dept of
General
Course Title Short: Neural Interfacing
Course Title Long: Neural Decoding and Interfacing
Max-Min Credits
for Course:		 3.0 to 3.0 credit(s)
Catalog
Description:		 This course will cover different types of neural interface technologies currently in use in patients as well as the biophysical, neural coding, and hardware features relating to their implementation in humans.  Practical and ethical considerations for implanting these devices into humans will also be presented.
Print in Catalog?: Yes
CCE Catalog
Description:		 To provide an overview of the different types of neural interface technologies currently in use in patients as well as coverage of the biophysics, neural coding, and hardware features relating to their implementation in humans.  The course will primarily focus on invasive neural implants that electrically interface with the peripheral or central nervous system.  Neurophysiological principles and computational modeling of neurons, current flow through tissue, and the tissue-electrode interface will be covered to understand how electrical signals and information are transmitted between the device and neurons.  Practical and ethical considerations for implanting these devices into humans are also presented.  A final group project will be required for simulating a neural implant system.
Grading Basis: A-F or Aud
Topics Course: No
Honors Course: No
Delivery Mode(s): Classroom
Instructor
Contact Hours:		 3.0 hours per week
Years most
frequently offered:		 Every academic year
Term(s) most
frequently offered:		 Spring
Component 1: LEC (with final exam)
Auto-Enroll
Course:		 No
Graded
Component:		 LEC
Academic
Progress Units:		 Not allowed to bypass limits.
3.0 credit(s)
Financial Aid
Progress Units:		 Not allowed to bypass limits.
3.0 credit(s)
Repetition of
Course:		 Repetition not allowed.
Course
Prerequisites
for Catalog:		 5411, [3201, 3401, or equiv, recommended]
Course
Equivalency:		 No course equivalencies
Consent
Requirement:		 No required consent
Enforced
Prerequisites:
(course-based or
non-course-based)		 003326 - BMEn 5411
Editor Comments: <no text provided>
Proposal Changes: <no text provided>
History Information: <no text provided>
Faculty
Sponsor Name:
Faculty
Sponsor E-mail Address:
Student Learning Outcomes
Student Learning Outcomes: * Student in the course:

- Can identify, define, and solve problems

Please explain briefly how this outcome will be addressed in the course. Give brief examples of class work related to the outcome.

Students will have to do a final project in which they have to take what they learned in class and perform in-depth research and thinking to expand their knowledge about their project. Then they have to simulate and implement their projects in Matlab. Finally they have to be able to present and explain it to the class.

How will you assess the students' learning related to this outcome? Give brief examples of how class work related to the outcome will be evaluated.

There will be exams and homeworks that will test their in-depth knowledge on the different topics covered in class. However, the key aspects of evaluating their learning outcomes above will be based on interactive class-room discussions/participation (they will receive points for this) and responses to questions for their final projects.

- Can locate and critically evaluate information

Please explain briefly how this outcome will be addressed in the course. Give brief examples of class work related to the outcome.

Students will have to do a final project in which they have to take what they learned in class and perform in-depth research and thinking to expand their knowledge about their project. Then they have to simulate and implement their projects in Matlab. Finally they have to be able to present and explain it to the class.

How will you assess the students' learning related to this outcome? Give brief examples of how class work related to the outcome will be evaluated.

There will be exams and homeworks that will test their in-depth knowledge on the different topics covered in class. However, the key aspects of evaluating their learning outcomes above will be based on interactive class-room discussions/participation (they will receive points for this) and responses to questions for their final projects.

- Have mastered a body of knowledge and a mode of inquiry

Please explain briefly how this outcome will be addressed in the course. Give brief examples of class work related to the outcome.

Students will have to do a final project in which they have to take what they learned in class and perform in-depth research and thinking to expand their knowledge about their project. Then they have to simulate and implement their projects in Matlab. Finally they have to be able to present and explain it to the class.

How will you assess the students' learning related to this outcome? Give brief examples of how class work related to the outcome will be evaluated.

There will be exams and homeworks that will test their in-depth knowledge on the different topics covered in class. However, the key aspects of evaluating their learning outcomes above will be based on interactive class-room discussions/participation (they will receive points for this) and responses to questions for their final projects.

- Can communicate effectively

Please explain briefly how this outcome will be addressed in the course. Give brief examples of class work related to the outcome.

In addition to the final project presentations, there will be continuous interactions and discussions during the class to encourage active thinking and communication among the students on the different topics taught in the class.

How will you assess the students' learning related to this outcome? Give brief examples of how class work related to the outcome will be evaluated.

There will be exams and homeworks that will test their in-depth knowledge on the different topics covered in class. However, the key aspects of evaluating their learning outcomes above will be based on interactive class-room discussions/participation (they will receive points for this) and responses to questions for their final projects.

- Understand the role of creativity, innovation, discovery, and expression across disciplines

Please explain briefly how this outcome will be addressed in the course. Give brief examples of class work related to the outcome.

Students will have to do a final project in which they have to take what they learned in class and perform in-depth research and thinking to expand their knowledge about their project. Then they have to simulate and implement their projects in Matlab. Finally they have to be able to present and explain it to the class.

How will you assess the students' learning related to this outcome? Give brief examples of how class work related to the outcome will be evaluated.

There will be exams and homeworks that will test their in-depth knowledge on the different topics covered in class. However, the key aspects of evaluating their learning outcomes above will be based on interactive class-room discussions/participation (they will receive points for this) and responses to questions for their final projects.
Liberal Education
Requirement
this course fulfills:		 None
Other requirement
this course fulfills:		 None
Criteria for
Core Courses:		 Describe how the course meets the specific bullet points for the proposed core requirement. Give concrete and detailed examples for the course syllabus, detailed outline, laboratory material, student projects, or other instructional materials or method.

Core courses must meet the following requirements:

  • They explicitly help students understand what liberal education is, how the content and the substance of this course enhance a liberal education, and what this means for them as students and as citizens.
  • They employ teaching and learning strategies that engage students with doing the work of the field, not just reading about it.
  • They include small group experiences (such as discussion sections or labs) and use writing as appropriate to the discipline to help students learn and reflect on their learning.
  • They do not (except in rare and clearly justified cases) have prerequisites beyond the University's entrance requirements.
  • They are offered on a regular schedule.
  • They are taught by regular faculty or under exceptional circumstances by instructors on continuing appointments. Departments proposing instructors other than regular faculty must provide documentation of how such instructors will be trained and supervised to ensure consistency and continuity in courses.

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Criteria for
Theme Courses:		 Describe how the course meets the specific bullet points for the proposed theme requirement. Give concrete and detailed examples for the course syllabus, detailed outline, laboratory material, student projects, or other instructional materials or methods.

Theme courses have the common goal of cultivating in students a number of habits of mind:
  • thinking ethically about important challenges facing our society and world;
  • reflecting on the shared sense of responsibility required to build and maintain community;
  • connecting knowledge and practice;
  • fostering a stronger sense of our roles as historical agents.


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Writing Intensive
Propose this course
as Writing Intensive
curriculum:		 No
Question 1 (see CWB Requirement 1): How do writing assignments and writing instruction further the learning objectives of this course and how is writing integrated into the course? Note that the syllabus must reflect the critical role that writing plays in the course.

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Question 2 (see CWB Requirement 2): What types of writing (e.g., research papers, problem sets, presentations, technical documents, lab reports, essays, journaling etc.) will be assigned? Explain how these assignments meet the requirement that writing be a significant part of the course work, including details about multi-authored assignments, if any. Include the required length for each writing assignment and demonstrate how the minimum word count (or its equivalent) for finished writing will be met.

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Question 3 (see CWB Requirement 3): How will students' final course grade depend on their writing performance? What percentage of the course grade will depend on the quality and level of the student's writing compared to the percentage of the grade that depends on the course content? Note that this information must also be on the syllabus.

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Question 4 (see CWB Requirement 4): Indicate which assignment(s) students will be required to revise and resubmit after feedback from the instructor. Indicate who will be providing the feedback. Include an example of the assignment instructions you are likely to use for this assignment or assignments.

 <no text provided>
Question 5 (see CWB Requirement 5): What types of writing instruction will be experienced by students? How much class time will be devoted to explicit writing instruction and at what points in the semester? What types of writing support and resources will be provided to students?

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Question 6 (see CWB Requirement 6): If teaching assistants will participate in writing assessment and writing instruction, explain how will they be trained (e.g. in how to review, grade and respond to student writing) and how will they be supervised. If the course is taught in multiple sections with multiple faculty (e.g. a capstone directed studies course), explain how every faculty mentor will ensure that their students will receive a writing intensive experience.

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Course Syllabus
Course Syllabus: For new courses and courses in which changes in content and/or description and/or credits are proposed, please provide a syllabus that includes the following information: course goals and description; format;structure of the course (proposed number of instructor contact hours per week, student workload effort per week, etc.); topics to be covered; scope and nature of assigned readings (text, authors, frequency, amount per week); required course assignments; nature of any student projects; and how students will be evaluated. The University "Syllabi Policy" can be found here

The University policy on credits is found under Section 4A of "Standards for Semester Conversion" found here. Course syllabus information will be retained in this system until new syllabus information is entered with the next major course modification. This course syllabus information may not correspond to the course as offered in a particular semester.

(Please limit text to about 12 pages. Text copied and pasted from other sources will not retain formatting and special characters might not copy properly.)


 This course was originally taught as a Special Topics course, BMEn 5920. A formatted syllabus is available upon request from Rachel Jorgenson at bmengp@umn.edu. See below for an unformatted version.

Instructor: Hubert H. Lim, Ph.D.
Assistant Professor
Department of Biomedical Engineering, University of Minnesota
E-mail: hlim@umn.edu
Phone: (612) 626-4565
Office: 6-132 Nils Hasselmo Hall
Office Hours: Wednesdays, 10-11AM
Meeting Time & Place: 10:10 AM ⿿ 12:05 PM, Friday, AmundH 162
Teaching Assistant: Hitesh Mehta, M.D. (mehta067@umn.edu)
Objectives: To provide an overview of the different types of neural interface technologies currently in use
in patients as well as coverage of the biophysics, neural coding, and hardware features relating to their
implementation in humans. The course will primarily focus on invasive neural implants that electrically
interface with the peripheral or central nervous system. Neurophysiological principles and computational
modeling of neurons, current flow through tissue, and the tissue-electrode interface will be covered to
understand how electrical signals and information are transmitted between the device and neurons.
Practical and ethical considerations for implanting these devices into humans are also presented. A final
group project will be required for simulating a neural implant system.
Text Books:
Book A (chapters will be scanned and posted on Moodle)
Horch KW, Dhillon GS. Neuroprosthetics: Theory and Practice. Series on Bioengineering &
Biomedical Engineering (Vol. 2). World Scientific Publishing Co., 2004.
Book B (downloadable for free from UMN libraries catalog or use link below from a UMN network)
Greenbaum E, Zhou D. Implantable Neural Prostheses 1: Devices and Applications. Biological and
Medical Physics, Biomedical Engineering Series. Springer, 2009.
http://www.springerlink.com/content/978-0-387-77260-8#section=61808&page=1
Book C (downloadable for free from UMN libraries catalog or use link below from a UMN network)
Zhou D, Greenbaum. Implantable Neural Prostheses 2: Techniques and Engineering Approaches.
Biological and Medical Physics, Biomedical Engineering Series. Springer, 2009.
http://www.springerlink.com/content/978-0-387-98119-2#section=730338&page=1
Website: A Moodle site is created for this course. It will be used for announcements, assignments,
solutions and posting other supplementary files. All scores will be posted on the Moodle
site so please check that they are accurate throughout the Spring term and if there
are any errors, please email the TA.
NOTE: "In this class, our use of technology will sometimes make students' names and U of M
Internet IDs visible within the course website, but only to other students in the same class.
Since we are using a secure, password-protected course website, this will not increase the risk
of identity theft or spamming for anyone in the class. If you have concerns about the visibility
of your Internet ID, please contact me for further information. You can also change your name
and profiles to limit the available information."
2
Course Format: This is a lecture-based course consisting of one 115-minute lecture per week. Students
will be assigned reading materials for each week and will be tested on that material with a short
quiz at the start of each lecture. Students are encouraged to participate in discussions during
each lecture and will receive in-class participation points for asking and/or answering questions.
The lecture will be organized as follows:
10:10-10:20 ⿿ Short quiz (2 questions; full credit for at least 1 correct answer)
10:20-11:10 ⿿ Lecture on topic listed in Table below
11:10-11:15 ⿿ Short break
11:15-12:05 ⿿ Interactive discussion on assigned reading
Grading Policy:
Weekly Quizzes: 25% Homeworks: 25% Final Project: 40% In-Class Participation: 10%
Grading will be based on 100-90%=A, 89-80%=B, 79-70%=C, 69-60%=D, <60%=Fail. The threshold
for grades may be lowered depending on overall class performance.
Students will be asked questions during lecture that are based on the assigned reading to receive In-Class
Participation points. Students can also receive points for asking questions or providing comments during
discussions and then will not be asked a question during a given lecture.
Reading materials/papers can be used for the quizzes. However, there will not be sufficient time to find
the answers if the materials were not read ahead of time.
All quizzes, homeworks, and final project must be completed and turned in on the designated dates. Only
under severe circumstances (e.g., death in family, severe health condition, etc.) will a make-up
opportunity be provided. Homeworks and the final project may be turned in at a later agreed upon date.
Make-up quizzes will be administered orally by the professor.
The Final Project will require a 40-minute presentation followed by 10 minutes of questions by the
instructor and fellow students. The Final Project is a major part of the final grade and it is recommended
that the students work on this project throughout the semester. The project topic and groups will be
determined during Week 1 Lecture. The students will research on the assigned topic, which will focus on
one neural prosthesis (NP) system (see end of syllabus for topics). They will need to research, in detail,
this NP to understand and present on the following components:
1) Background/Rationale (10% of points)
2) Neurophysiology underlying NP implementation (20%)
3) Implementation including practical, functional, safety considerations/justifications (30%)
4) Demonstrate simulation of NP with Matlab (30%)
5) Current trends and future directions (10%)
The Instructor will base the final project score on the group presentation and responses to the in-class
questions as well as a detailed discussion/simulation during a separate 30-minute meeting set up outside
of class with each group. Each group will provide reading materials for the class to read before their
presentation and will provide 1 quiz question from that reading. Two NP examples (one encoding and one
decoding) will be presented in lectures by the Instructor covering all 5 components above. Students
should research and present their projects following a similar organization and content as those lectures.
Academic integrity is essential to a positive teaching and learning environment. All students enrolled in
University courses are expected to complete coursework responsibilities with fairness and honesty. Failure
to do so by seeking unfair advantage over others or misrepresenting someone else⿿s work as your own,
can result in disciplinary action. The University Student Conduct Code defines scholastic dishonesty as
follows:
Scholastic Dishonesty: Scholastic dishonesty means plagiarizing; cheating on assignments or
examinations; engaging in unauthorized collaboration on academic work; taking, acquiring, or using test
materials without faculty permission; submitting false or incomplete records of academic achievement;
acting alone or in cooperation with another to falsify records or to obtain dishonestly grades, honors,
awards, or professional endorsement; altering forging, or misusing a University academic record; or
fabricating or falsifying data, research procedures, or data analysis.
Within this course, a student responsible for scholastic dishonesty can be assigned a penalty up to and
including an "F" or "N" for the course. If you have any questions regarding the expectations for a specific
assignment or quiz, please ask.
Students with disabilities
The instructor will make all reasonable accommodations necessary for students with disabilities.
LECTURES:
WEEK TOPICS READING HWs
1
Part I: Introduction
Overview of neural prostheses and considerations;
Go over syllabus
A_1.2-1.4
(if needed)
2
Part II: Basics
PNS and CNS electrode/interface technologies and
safety/functionality considerations for translation;
Case study on Auditory Midbrain Implant
Navarro review
Konrad review
B_117-154
3
Part II: Basics
PNS and CNS stimulation:
Electrode-tissue interface, neural tissue damage,
safety stimulation limits, charge injection capacity
4
Part II: Basics
PNS and CNS stimulation:
Current spread and activation effects
5
Part II: Basics
PNS and CNS recording:
Electrode-tissue interface, neural tissue damage,
recording properties (local field potentials/spikes)
Set 1
due
6
Part II: Basics
PNS and CNS recording:
Local field potential and spike analyses
7
Part III: Ethical,
Safety, Translational
Device development/safety; FDA; Patient
concerns/rehabilitation; Intellectual property;
Collaborations/Commercialization
Set 2
due
8
Part IV: Encoding
Neural Prosthesis Ex.
Cochlear implant:
Rationale, Neurophysiology
4
9
Part IV: Encoding
Neural Prosthesis Ex.
Cochlear implant:
Implementation, Matlab simulation
10
Part IV: Encoding
Neural Prosthesis Ex.
Cochlear implant:
Current trends, Future directions
Set 3
due
11
Part V: Decoding
Neural Prosthesis Ex.
Cortical control of robotic arm:
Rationale, Neurophysiology
12
Part V: Decoding
Neural Prosthesis Ex.
Cortical control of robotic arm:
Implementation, Matlab simulation
13
Part V: Decoding
Neural Prosthesis Ex.
Cortical control of robotic arm:
Current trends, Future directions
Set 4
due
14
Part VI: Presenting a
Neural Prosthesis
Encoding prosthesis group projects with Matlab
simulation results
15
Part VI: Presenting a
Neural Prosthesis
Encoding prosthesis group projects with Matlab
simulation results
Final Exam Day
Part VI: Presenting a
Neural Prosthesis
Decoding prosthesis group projects with Matlab
simulation results
Project Topics:
Only encoding or decoding topics are mainly covered in this class since modulation topics are covered in a
separate course taught by Dr. Matt Johnson
During Week 1, the Instructor will assign the groups and project topics by randomly drawing names
written on a piece of paper. 6 groups of 2-3 people each.
1. Retinal or Optic Nerve NP (encoding/stimulation)
2. Visual Cortical NP (encoding/stimulation)
3. Vestibular Nerve NP (encoding/stimulation)
4. Incontinence/Bladder Control Spinal Cord NP (encoding/stimulation)
5. Robotic arm/hand control PNS NP (decoding/recording)
6. Speech/Text Cortical NP (decoding/recording; invasive/noninvasive)
Strategic Objectives & Consultation
Name of Department Chair
Approver:		 <no text provided>
Strategic Objectives -
Curricular Objectives:		 How does adding this course improve the overall curricular objectives ofthe unit?

 <no text provided>
Strategic Objectives - Core
Curriculum:		 Does the unit consider this course to be part of its core curriculum?

 <no text provided>
Strategic Objectives -
Consultation with Other
Units:		 In order to prevent course overlap and to inform other departments of new curriculum, circulate proposal to chairs in relevant units and follow-up with direct consultation. Please summarize response from units consulted and include correspondence. By consultation with other units, the information about a new course is more widely disseminated and can have a positive impact on enrollments. The consultation can be as simple as an email to the department chair informing them of the course and asking for any feedback from the faculty.

 <no text provided>