IUBio Biosequences .. Software .. Molbio soft .. Network News .. FTP

Frontiers in Electroencephalography Symposium

Richard M Dasheiff M.d. dasheiff+ at pitt.edu
Mon Sep 27 10:59:58 EST 1993

       A N N O U C E M E N T    O F     A      S Y M P O S I U M
      Frontier Science in EEG Symposium:SATURDAY, OCTOBER 9, 1993
For scientists doing signal analysis in both biomedical and physical sciences.
      C O N T I N U O U S     W A V E F O R M     A N A L Y S I S

John K. Chapin, Ph.D.,Dept. Physiology & Biophysics,Hahnemann Univ.
D. Kent Cullers, Ph.D.,NASA/SETI
Lester Ingber, Ph.D.,Lester Ingber Research
Ben Jansen, Ph.D.,Dept. of Electrical Engineering,Univ.of Houston
B. H. Juang, Ph.D.,Supervisor, Speech Research Dept.,AT&T Bell Labs
Periklis Ktonas, Ph.D.,Dept. of Electrical Engineering,Univ.of Houston
Fernando Lopes da Silva, M.D., Ph.D.,Institute Neurobiology,The Netherlands
Paul L. Nunez, Ph.D.,Brain Physics Group,Dept. of Biomed. Eng.,Tulane Univ.
Edward T. Olsen, Ph.D.,NASA/JPL
Robert D. Sidman, Ph.D.,Dept. of Mathematics,Univ.of Southwestern Louisiana
M. Victor Wickerhauser, Ph.D.,Dept. Mathematics,Washington Univ.

*                  To be held in conjunction with 
*          the annual meeting of the American EEG Society
*                        Oct. 11 - 13, 1993
*        call 203-243-3977 about regist. for the Am. EEG Soc. meeting
          [this will not register you for the symposium - see below]
                       NEW ORLEANS MARRIOTT
                      NEW ORLEANS, LOUISIANA 

Sponsored by:
University of Pittsburgh Medical Center 
Center for Continuing Education in the Health Sciences   

Supported by an Educational Grant from:
Parke-Davis, Div. of Warner-Lambert


Electroencephalography (EEG) is the study of the electrical activity of the 
brain.  The field of EEG includes the technology to record these electrical
signals, the science to analyze them and the expertise to apply them to 
patient care.  This symposium will explore the scientific frontiers related 
to EEG, presenting the latest research and thought with this year's topic 
being continuous waveform analysis.  As advances in science and technology 
often involve collaboration among scientists from different fields, we are 
bringing together a diverse group of investigators, many from areas not 
conventionally associated with EEG, to actively encourage multidisciplinary 
research in EEG and foster new ideas.


This conference is designed for anyone dealing with signal analysis,
especially neurologists, neurophysiologists, electroencephalographers, 
mathematicians, physicists, computer scientists, astronomers, and engineers.


Richard M. Dasheiff, M.D.
Univ. Pittsburgh Epilepsy Center
email rmd at med.pitt.edu

Diana Joan Major-Vincent, Ph.D.
University of Pittsburgh
email dvi at med.pitt.edu
      vincent at pittvms.bitnet


John K. Chapin, Ph.D.
Dept. Physiology & Biophysics
Hahnemann Univ.

D. Kent Cullers, Ph.D.

Lester Ingber, Ph.D.
Lester Ingber Research

Ben Jansen, Ph.D.
Department of Electrical Engineering
University of Houston

B. H. Juang, Ph.D.
Supervisor, Speech Research Department
AT&T Bell Labs

Periklis Ktonas, Ph.D.
Department of Electrical Engineering
University of Houston

Fernando Lopes da Silva, M.D., Ph.D.
Institute of Neurobiology
The Netherlands

Paul L. Nunez, Ph.D.
Brain Physics Group 
Department of Biomedical Engineering
Tulane University School of Engineering

Edward T. Olsen, Ph.D.

Robert D. Sidman, Ph.D.
Department of Mathematics
University of Southwestern Louisiana

M. Victor Wickerhauser, Ph.D.
Dept. Mathematics
Washington Univ.

[ Conflict of Interest Disclosure Statement                                   ]
[ In accordance with the policies on disclosure of the Accreditation Council  ]
[ for Continuing Medical Education and the Faculty Advisory Committee for     ]
[ Continuing Education in the Health Sciences, University of Pittsburgh,      ]
[ presenters for this program have identified no personal relationships which,]
[ in the context of their topics, could be perceived as real or apparent      ]
[ conflict of interest.                                                       ]


       7:00 - 7:30am - Registration with continental breakfast

       7:45am - Introduction: Richard Dasheiff,M.D. Univ. of  Pittsburgh

            Focus is on continuous, time-series, alternating current/signals. 
            Standing   potentials  (DC  EEG),  Evoked  Potentials,  
            Non-Stationary Processes, Epilepsy, Intensive care unit (ICU) 
            monitoring, etc.,  will be  topics  for  future  symposia.

       7:55am - Moderator: Diana Major-Vincent, Ph.D. Univ. of Pittsburgh

       8:00am - Biophysics of the EEG and Neural networks: Fernando Lopes  da
       Silva, M.D., Ph.D. Univ. of Amsterdam

            This talk will address the relationship between  dynamic  aspects
       of the biological generators of the EEG and the structural constraints
       imposed by the anatomy of the brain  (a  non-homogeneous,  irregularly
       shaped  structure)  and of the surrounding tissues. Topics include the
       effect these biological structures have  on  signal  degradation,  the
       differences  in  signals  from intracranial and extracranial recording
       (spatial  and  temporal  dispersion,  S/N,  voltage  loss,  extraneous
       noise), and how these affect recording and modeling of the EEG.

       8:40am - History  of  source  localization  and  bioelectric  imaging:
       Robert Sidman, Ph.D. Univ. of Southwestern Louisiana 

            What should a mathematical model of the EEG do? An  early  model,
       "nonlinear oscillations and the EEG", Dewan's 1964 paper may have been
       chaos 30 years before its  time.   Source  localization  in  the  time
       domain  (dipole  localization  methods)  offers  a  parsimonious model
       requiring  few  assumptions,  but   has   limitations.    Imaging   or
       topographical  mapping  methods  in the spatial domain such as CIT and
       the Spline-Laplacian are also  presented  with  a  discussion  of  the
       mathematical ideas that led to CIT.  Many of the methods for analyzing
       the EEG have their roots in a few seminal  research  papers.   I  will
       discuss these papers and their relationship to "modern" techniques.

       9:20am - Computer-based  EEG  pattern  recognition:  Periklis  Ktonas,
       Ph.D. Univ. of Houston

            Computer-based  recognition  of  specific   EEG   signals   (e.g.
       spindles,   spikes)   in  multichannnel  EEG  records  has  relied  on
       mathematical techniques (e.g. spectral analysis, "optimum"  filtering)
       and  on  heuristic "mimetic" approaches, including expert system-based
       methods.  Recently,  nonlinear  discriminant  analysis   methodologies  
       under the broad term "neural networks"  have been tried as well.  This
       presentation will focus on  the  pitfalls,  achievements  and  overall
       usefulness of the above techniques.

       10:00 - 10:20am Coffee Break

       10:30am - Spatial Analysis of EEG: Paul Nunez, Ph.D. Tulane Univ. 

            The field of scalp  recorded  EEG  spans  about  nine  orders  of
       magnitude of temporal scale (Ktonas, Dasheiff), but spatial resolution
       is limited to several cm's (Sidman).  Neocortical  dynamics  evidently
       involves  neural  interactions  at  multiple  spatial scales (Lopes da
       Silva), with effects that cross hierarchical levels  from  microscopic
       to  the  macroscopic  scale of scalp EEG (Ingber).  Modern measures of
       dynamic function based on a small number of EEG channels may result in
       a distorted view of this dynamics (Jansen, Ingber, Juang).

            The severe limitations of conventional EEG and new methods  which
       increase  spatial  resolution by about a factor of three are reviewed.
       The ways in which this vast amount of new information may be  used  in
       clinical  and  cognitive  studies  is considered.  The implications of
       recording strategy for  various  estimates  of  dynamic  function  are

       11:10am - Chaos and Quantitative EEG Analysis:  Ben  H.  Jansen,  Ph.D
       Univ. of Houston 

            Support for a  nonlinear  and/or  chaotic  nature  of  the  human
       electroencephalogram  (EEG)  is  reviewed. It is argued that dimension
       calculations and other classical  methods  to  assess  chaos  are  not
       reliable in the case of EEG data because it is impossible to ascertain
       that the system (i.e.,  the  brain)  is  time-invariant  and  that  it
       displays  asymptotic  behavior  during  the  interval  of observation.
       Despite the lack of hard scientific evidence, there are ample  reasons
       to conclude that the EEG is produced by a nonlinear, possibly chaotic,
       (deterministic)  system.  The  implications  of  this  change  from  a
       (stochastic)  to  a  deterministic  viewpoint of EEG generation on the
       practice of visual and quantitative EEG interpretation  is  discussed.
       It is argued that conventional EEG analysis techniques will have to be
       revised and that previously reported findings need to be reinterpreted
       in the light of the chaotic or nonlinear nature of the EEG.

       11:50am - 12:20pm Panel Discussion

       12:20 - 1:20pm Lunch (included in tuition)

       1:20pm  -  Wavelets,  Adapted   Waveforms,   and   Denoising:   Victor
       Wickerhauser, Ph.D.  Washington Univ. 

            The goal is to describe some new  libraries  of  waveforms  well-
       adapted  to  various  numerical  analysis and signal processing tasks.
       The main point of this presentation is that by expanding a signal in a
       library  of  waveforms  which  are  well-localized  in  both  time and
       frequency,  one  can  achieve  both  understanding  of  structure  and
       efficiency in computation.  The talk will briefly cover the properties
       of the new "wavelet" and  "localized  trigonometric"  libraries.   The
       main  focus  will be applications of such libraries to the analysis of
       complicated transient signals: a feature recognition  algorithm  based
       on  fast  approximate  principal factor analysis, a feature extraction
       and data compression algorithm for acoustic signals which  uses  best-
       adapted  time-frequency  decompositions, and a wavelet-based denoising
       algorithm for passive sonar and music.  These signals  share  many  of
       the  same  features  as  EEG  traces,  and the algorithms are directly
       useful for that application as well.


More information about the Comp-bio mailing list

Send comments to us at biosci-help [At] net.bio.net