Tutorial
May 28 , 2006, 15:40-17:40 , Room C

New Techniques for Functional Imaging of Electric Fields of Human Brain

Walter J. Freeman	dfreeman@berkeley.edu
Professor of the Graduate School	tel 1-510-642-4220
Dept Molecular & Cell Biology	fax 1-510-643-9290
University of California , Donner 101	http://sulcus.berkeley.edu
Berkeley CA 94720-3206

Abstract
    Recent advances in electroencephalography might enable researchers to make images of brain activity patterns in subjects who report the contents of their mental states. Making such images is a challenging task.  The objective of this tutorial is to describe these advanced methods of analysis of multichannel electroencephalographic (EEG) recordings.  These new algorithms can be applied in any clinical facility with standard equipment at no great expense.  They have applications to the study of attention, intention, expectancy, sensory processing, learning, habituation, and auras in epilepsy.
    In the first part of the tutorial I will review conventional as well as advanced methods of analysis of EEGs in the temporal and frequency domains. A distinctive feature of the EEG is the appearance of oscillations in different frequency bands, which reflect the synchronized activity of large groups of neurons. Brain oscillations have been correlated with different brain processes and its power is usually quantified by means of the Fourier Transform. The Fourier Transform is so far the most used tool for the analysis of EEGs, but it assumes stationarity of the signal and it gives no time information. It is therefore not appropriate when frequency patterns change over time. For these cases, ‘time-frequency’ representations such as that given by the Short Time Fourier Transform are more suitable. In particular, I will describe variants of time-frequency decomposition, namely, the Wavelet Transforms, and its advantages and limitations in the analysis of EEG data. Brain processes involving larger neuronal assemblies or interactions between distant sites are represented in correlations between EEG electrodes. In this respect, I will describe the Hilbert transform to derive measures of synchrony and compare them to Fourier approaches.
    In the second part I will review recent advances in EEG spatial pattern imaging, with emphasis on techniques for analysis of multichannel recordings from high-density electrode arrays placed on cortical surfaces intracranially in animals and on the scalp of normal human volunteers. I will begin by describing the advantages of spatial analysis with 1-D arrays preparatory to 2-D recording. I will show the advantages of displaying the power spectral density output of the 1-D FFT in log-log coordinates. This display is useful to distinguish among various noise spectra and the “1/f” scaling that distinguishes EEGs from muscle potentials [electromyograms, EMGs]. I will use the application of the 1-D FFT to EEGs from curvilinear scalp electrode arrays to discuss temporal and spatial sampling, aliasing, the Nyquist frequencies, and the spectral changes caused by the impedance barriers of the scalp and skull, and by the sulci and gyri of cortex.
    I will demonstrate the methods used for detection, measurement, display, and classification of spatial patterns of amplitude and phase, which are made possible by the Hilbert transform. I will describe the methods and criteria for the temporal and spatial filters that are necessary for effective use of the Hilbert transform. I will discuss in detail a form of nonstationarity in brain dynamics, in which cortical states occur as brief stable EEG amplitude patterns, like frames in a movie film. Each window is bracketed by sudden changes in EEG phase patterns. I will conclude by introducing novel measures of specific properties of the EEG, including the analytic signal, phase slip, intermittent synchronization, pattern stabilization, and indices of the amount of information in EEG patterns for correlation with psychophysical studies, and of the rate of energy dissipation needed to sustain them for correlation with brain images that rely on blood flow and oxygen utilization.

Biosketch 
    Walter J. Freeman studied electronics in the US Naval Reserve 1944-46, physics and mathematics at MIT, medicine at Yale (MD cum laude 1954), internal medicine at Johns Hopkins, and neuropsychiatry at UCLA. He has taught neuroscience in the University of California at Berkeley since 1959, now as Professor of the Graduate School. He has received a Guggenheim Fellowship, a MERIT Award from NIH in 1990, a Pioneer Award from the Neural Networks Council in 1992, was President of the International Neural Network Society in 1994, and is a Life Fellow of the IEEE. He has published over 400 articles and 5 books on brain dynamics.