Bioengineering 245

Electromagnetic Neuroimaging

Description

This course provides a rigorous introduction to human functional brain imaging, using electroencephalography (EEG) and magnetoencephalography (MEG). The aim is to establish the necessary foundation to understand and critique the biomedical engineering literature, and begin research in the development and application of electromagnetic neuroimaging. Students interested in the related topics of electrical impedance tomography (EIT) and transcranial magnetic stimulation (TMS) will gain all the necessary basics by studying EEG and MEG in this course.

The first half of the course addresses the forward imaging problem. The sources of EEG-MEG signals are described from the underlying neuroanatomy and neurophysiology. Starting from Maxwell's equations, the physics of electromagnetic fields in biological tissue are studied. Solutions for the fields generated by idealized monopole and dipole sources in a uniform conductor are derived. These solutions are extended to incorporate spherical or realistic head geometry. Lead field theory brings all head modeling techniques into a common framework, and develops intuition about the spatial sensitivity of EEG and MEG surface detectors.

The second half of the course focusses on data analysis and the inverse imaging problem. Principles of EEG-MEG data acquisition are described, and demonstrated in a UCSF laboratory. Linear methods for analyzing EEG-MEG time series are described, including especially the time-domain impulse response function, Fourier power spectrum and coherence. Techniques for solving the ill-posed EEG-MEG inverse problem are introduced, including single- and multiple-dipole solutions, linear inverse methods, and beamforming. The course concludes with statistics, hypothesis testing, and experimental design.

Prerequisites: The course assumes reasonable knowledge of differential and integral calculus, complex variables, vectors and matrices, and electromagnetism. More advanced mathematics, especially vector calculus, methods for solving partial differential equations, linear response theory, and matrix decomposition, will be covered as needed.

General Information

Offered through the UCSF/UCB Bioengineering Graduate Program

Instructor:
Thomas Ferree, PhD Email: tom.ferree@radiology.ucsf.edu

Course Schedules:
Spring 2002
Fall 2003

Lecture Notes

Some (but not all) of the lecture notes have been typeset in LaTeX.
These include chapters on:

  • Chapter 1: Introduction
  • Chapter 2: Neuronal Basis of EEG-MEG
  • Chapter 3: Bioelectromagnetism
  • Chapter 4: Dipole Source Modeling
  • Chapter 5: Head Modeling
  • Chapter 6: Lead Field Theory
  • Chapter 7: Principles of Data Acquisition
  • Chapter 8: Linear Time Series Analysis
  • Chapter 9: Topographic Analysis
  • Appendix A: Vector Calculus
  • Appendix B: Linear Response Theory
  • Appendix C: Fourier Transform

All chapters may be downloaded by clicking here.
Send an email to receive the login and password.

Homework Sets

Some (but not all) of the homework sets have been typeset in LaTeX.
Send an email to receive the login and password.

Online Resources