2 papers applying neural networks to EEG data

[Scott Makeig]

       Announcing the availability of preprints of two articles 
          to be published in the NIPS conference proceedings:

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    INDEPENDENT COMPONENT ANALYSIS OF ELECTROENCEPHALOGRAPHIC DATA

  Scott Makeig                        Anthony J. Bell 
  Naval Health Research Center        Computational Neurobiology Lab  
  P.O. Box 85122                      The Salk Institute, P.O. Box 85800
  San Diego CA 92186-5122             San Diego, CA 92186-5800
  scott at cpl_mmag.nhrc.navy.mil        tony at salk.edu
                                    
  Tzyy-Ping Jung                      Terrence J. Sejnowski 
  Naval Health Research Center and    Howard Hughes Medical Institute and 
  Computational Neurobiology Lab      Computational Neurobiology Lab 
  jung at salk.edu                       terry at salk.edu

                            ABSTRACT

     Because of the distance between the skull and brain and their
different resistivities, electroencephalographic (EEG) data collected
from any point on the human scalp includes activity generated within
a large brain area. This spatial smearing of EEG data by volume
conduction does not involve significant time delays, however,
suggesting that the Independent Component Analysis (ICA) algorithm
of Bell and Sejnowski(1994) is suitable for performing blind source
separation on EEG data. The ICA algorithm separates the problem of
source identification from that of source localization. First
results of applying the ICA algorithm to EEG and event-related
potential (ERP) data collected during a sustained auditory detection
task show: (1) ICA training is insensitive to different random
seeds. (2) ICA analysis may be used to segregate obvious artifactual
EEG components (line and muscle noise, eye movements) from other
sources. (3) ICA analysis is capable of isolating overlapping alpha 
and theta wave bursts to separate ICA channels (4) Nonstationarities 
in EEG and behavioral state can be tracked using ICA analysis via 
changes in the amount of residual correlation between ICA-filtered 
output channels.

Sites:            http://128.49.52.9/~scott/bib.html
            ftp://ftp.cnl.salk.edu/pub/jung/nips95b.ps.Z

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            USING NEURAL NETWORKS TO MONITOR ALERTNESS
           FROM CHANGES IN EEG CORRELATION AND COHERENCE

        Scott Makeig                        Tzyy-Ping Jung
   Naval Health Research Center     Naval Health Research Center and
       P.O. Box 85122                Computational Neurobiology Lab
   San Diego, CA 92186-5122               The Salk Institute 
 scott at cpl_mmag.nhrc.navy.mil               jung at salk.edu

                         Terrence J. Sejnowski
                    Howard Hughes Medical Institute &
                     Computational Neurobiology Lab
                          The Salk Institute 
                            terry at salk.edu

                               ABSTRACT

We report here that changes in the normalized electroencephalographic
(EEG) cross-spectrum can be used in conjunction with feedforward
neural networks to monitor changes in alertness of operators
continuously and in near-real time. Previously, we have shown that
EEG spectral amplitudes covary with changes in alertness as indexed
by changes in behavioral error rate on an auditory detection task
(Makeig & Inlow, 1993). Here, we report for the first time that
increases in the frequency of detection errors in this task are
also accompanied by patterns of increased and decreased spectral
coherence in several frequency bands and EEG channel pairs.
Relationships between EEG coherence and performance vary between
subjects, but within subjects, their topographic and spectral
profiles appear stable from session to session. Changes in alertness
also covary with changes in correlations among EEG waveforms recorded
at different scalp sites, and neural networks can also estimate
alertness from correlation changes in spontaneous and
unobtrusively-recorded EEG signals.

Sites:         http://128.49.52.9/~scott/bib.html
          ftp://ftp.cnl.salk.edu/pub/jung/nips95a.ps.Z