PHYS 576 A: Selected Topics in Experimental Physics

Welcome to PHYS 427A / 576A , my name is Samu Taulu, and I will be your instructor. This is a special topic course titled "Electromagnetism in brain imaging". We will explore the physics-based methodology in non-invasive measurements of the electromagnetic fields produced by the human brain. The main emphasis is on magnetoencephalography (MEG), but the basics of electroencephalography (EEG) will also be covered. The course covers the formation of action potentials and post-synaptic potentials, derivation of the associated electromagnetic fields outside the head, instrumentation to detect these fields, inverse models to reconstruct the underlying electric currents in the brain, vector spherical harmonic expansion as a novel analysis approach, and relevant signal processing methods to correct for signal distortions in MEG/EEG data for more reliable analysis. Many of the mathematical methods covered in this course are based on linear algebra. At the end of the course we will take a look at future trends and emerging technologies. We will practice problem solving skills in MEG/EEG with the aim of gaining insight more generally into the interpretation of realistic signals recorded with multi-sensor instruments that detect electromagnetic fields. Part of the tutorial component is dedicated to interpreting actually recorded MEG data.

By attending this course the student will learn:

• The basic, high-level, operating principle of the human brain and the practical aspects of the MEG instrument for non-invasive study of the brain functions
• To apply Maxwell’s equations to data recorded with real MEG/EEG instruments
• Principles of shielding and signal detection of very weak electromagnetic signals
• To apply mathematical methods with the aim of solving the electromagnetic inverse problem using tools of linear algebra
• To recognize the importance of harmonic basis function expansions (for scalar and vector fields) as an approach to analyze spatiotemporally discretized multichannel signals in neuroimaging
• To recognize signal distortion mechanisms in realistic measurements and methods to compensate for them
• To interpret features in real multichannel MEG data and to find optimal solutions for efficient and robust signal processing and analysis
• Future trends in the development of MEG instrumentation

Note that due to the recent decision to have all spring quarter teaching on-line in order to mitigate the spread of the Coronavirus, I will potentially have to adjust the overall plan to better fit the current exceptional circumstances. My priority is to ensure that the course will be an enjoyable experience and the basic learning objectives are met. 

Getting started

The required material for this course is the textbook by Ilmoniemi & Sarvas:

Risto J. Ilmoniemi and Jukka Sarvas, "Brain Signals: Physics and Mathematics of MEG and EEG", ISBN: 978-0262039826.

In addition, we will study parts of the following articles (in alphabetical order) :  

• • Hämäläinen M., Hari R., Ilmoniemi R.J., Knuutila J., and Lounasmaa O.V., “Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain”, Rev. Mod. Phys. 65(2), 413-497 (1993).
  • Jin K., Alexopoulos A.V., Mosher J.C., Burgess R.C., "Implanted medical devices or other strong sources of interference are not barriers to magnetoencephalographic recordings in epilepsy patients", Clin. Neurophys. 124:1283-1289 (2013).
  • Taulu S. and Kajola M., "Presentation of electromagnetic multichannel data: The signal space separation method", J. Appl. Phys. 97:124905 1-10 (2005). 
  • Taulu S., Simola J., and Kajola M. "Applications of the Signal Space Separation Method", IEEE Trans. Sign. Proc. 53:3359-3372 (2005). 
  • Taulu S., Nenonen J., Simola J., and Parkkonen L., "Novel noise reduction methods" (this is a book chapter)

If you are interested in neuroscience, I recommend the following book 

Riitta Hari and Aina Puce, "MEG-EEG Primer", ISBN: 978-0190497774

Additionally, some further readings may be assigned as needed to support the tutorials

Class components

This class consists of the following components:

• • Lectures: We will meet each Tuesday and Thursday at 1:00 PM - 2:20 PM, and all sessions will be given remotely by Zoom. During the first lecture, we will test the technology and make sure that everyone will be able to attend the course without major problems. On Tuesdays, I will give a traditional lecture on topics indicated in the table below. I will post sufficient materials before the lecture so that students can follow the lecture even if the video connection does not work optimally. Note: During the first week, we will have a lecture also on Thursday, and the tutorials will start on the second week.
  • Tutorials: On Thursdays (from 1:00 PM - 2:20 PM), we will have a tutorial session where we go over the homework problems (to be solved and submitted before the session) as a group. If needed, part of the Thursday slot may be lecture-style. There may also be questions at the tutorial session on, e.g., real acquired MEG data where the students are asked to interpret features in the MEG data and potentially find solutions to certain problematic conditions regarding the data. The original idea was to have students discuss the solutions in small groups, followed by a selected group or student presenting the solution that is then open for group discussion. However, we will have to be flexible about this and see what kind of a format best fits the new circumstances requiring everything to be on-line. If you know that you cannot attend a certain Thursday session, please let me know and we can find an alternative way to account for your absence. Part of the tutorial may actually consist, e.g., of a review of a research article assigned to a student or a small group of students for presentation. Each student will submit their HW solutions electronically before the tutorial session. It is sufficient to work out the problems with pen and paper and scan the paper or simply take a picture of it with a smart phone. Supported formats are at least pdf, png, and jpg. You can send the solutions to me, e.g., by email or Google Drive.

Contact info

Below is the contact information.  Contact me for any questions specifically related to this course.

• Lecture instructor	Prof. Samu Taulu <staulu@uw.edu>
  TA	Tomek Fraczek <tfraczek@uw.edu>
  Undergraduate adviser	Margot Nims <margot@phys.washington.edu>

Weekly assignment due times

• 	How to submit	Due
  Lecture homework	Send solutions to instructor by email	Thursdays at 12:00 PM

Schedule

As mentioned above, we will meet each Tuesday and Thursday from 1:00 PM - 2:20 PM on Zoom. The technical details will be sorted out during the first lecture, and I will send an invitation link to everyone before the lecture. I will post slides and potentially additional lecture notes under the "Files" menu on the left before each class. 

The final exam will be on June 12th from 2:30 PM to 4:20 PM and this will be an online exam. The exact technical details will be announced later.  

Make-up exams can be arranged in case technical problems occur at the time of the exam, preventing the student from participating, or in case of illness.

Here is the schedule for the weekly broad topics:

• WEEK OF	TOPIC	READING
  3/30	Practical information, structure and biophysics of the brain	Ilmoniemi & Sarvas, chs. 2.3-2.6 Hämäläinen et al., ch. II Hämäläinen et al., ch. V.B.4-5, V.C.
  4/6	Quasi-static approximation of Maxwell’s equations, derivation of the MEG/EEG forward model	Ilmoniemi & Sarvas, ch. 2.1 & 3.1-3.4 & 3.6-3.7 & 3.9 Hämäläinen et al., ch. III
  4/13	Magnetic shielding, signal detection with pick-up loops and SQUIDs, examples of signal distortion mechanisms	Book chapter Taulu et al., sections 1-2 & 3.1-3.2
  4/20	Sampling theory and Shannon’s information, lead field and linear algebra concepts in MEG	Ilmoniemi & Sarvas, ch. 4 Hämäläinen et al., chs. V.F.1-3
  4/27	Electromagnetic inverse problem and approaches to solve it with linear algebra	Ilmoniemi & Sarvas, ch. 5
  5/4	Specific inverse problem solutions in MEG	Ilmoniemi & Sarvas, chs. 6.1-6.8 & chs. 7.1-7.3
  5/11	More inverse solutions and signal decomposition methods	Ilmoniemi & Sarvas, chs. 7.1-7.5 & 8.1-8.4
  5/18	Harmonic expansions in MEG/EEG, the SSS model, novel interpretations on the sensitivity of MEG/EEG to specific neural sources	Taulu and Kajola, J. Appl. Phys.  Book chapter Taulu et al., sections 3.3 & 3.5.3-3.5.5.
  5/25	Interference signals in MEG, methods to suppress interference, movement compensation methodology	Taulu et al., IEEE Trans. Sign. Proc. Jin et al., Clin. Neurophys.
  6/1	Future sensor technologies in MEG, wrap-up of the course	Ilmoniemi & Sarvas, ch 1.7

Getting help

If you have a scientific question that could be interesting to the whole class, use the "Discussions" menu on the left. I will monitor this a few times per week and will respond unless somebody else has already provided an answer.

You are encouraged to visit me during my office hours on Zoom on:

• • Monday from 2:00 PM to 3:00 PM
  • Or by appointment (please send me an email at least 24 h in advance to request a meeting time)

Grades

Your grade will consist of the following components:

• • Final exam: 60% of the grade
  • Homework problems: 30% of the grade
  • Tutorial discussions/quizzes: 10% of the grade

Alternatively, the final exam can account for 100% of the grade in case this would give you a better grade than the above combination.

Access and accommodation

Your experience in this class is important to me, so if you have a temporary health condition or permanent disability that requires accommodations (conditions include but are not limited to: mental health, attention-related, learning, vision, hearing, physical), please see details here. 

Lecture recordings

This course is scheduled to run synchronously at your scheduled class time via Zoom. These Zoom class sessions will be recorded. The recording will capture the presenter’s audio, video and computer screen. Student audio and video will be recorded if they share their computer audio and video during the recorded session. The recordings will only be accessible to students enrolled in the course to review materials. These recordings will not be shared with or accessible to the public.

The University and Zoom have FERPA-compliant agreements in place to protect the security and privacy of UW Zoom accounts. Students who do not wish to be recorded should:

• Change their Zoom screen name to hide any personal identifying information such as their name or UW Net ID, and
• Not share their computer audio or video during their Zoom sessions.

Religious accommodations

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy (https://registrar.washington.edu/staffandfaculty/religious-accommodations-policy/). Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form (https://registrar.washington.edu/students/religious-accommodations-request/)