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data file and then requests users to specify one routine from each
of the three categories to be used
in processing that
file.
It also
requests users to specify which channels they wish to process, and
which data-points within channels. FILMAN then loads the desired
routines from the disk and begins
to process
the
input
file one
record-set
at
a
time.
If the chosen
record-set
selection
routine
determines
that
the
current
record-set
is
to be
processed,
then
FILMAN executes the
selected grouping-variable and data-point
operations for the selected channels and data points. 11 Depending
on which point-processing routine was selected, an output data file
may also be written;
if so,
it
is
itself a standard
file and hence
accessible for further processing, either by FILMAN
itself or by
other routines such as the
statistical analysis programs.
We remark here that the analysis of EEG data in particular is a
very complex and
still rather poorly understood subject. There are
really two aspects
to
the
analysis of task- or
state-related EEG
differences: first, feature-extraction, or reduction of the raw EEG to
some hopefully more compact and physiologically revealing repre-
sentation; and second, pattern-recognition, or use of the extracted
features to discriminate the relevant tasks or
states. Within our
system,
these aspects are handled largely
separately.
All of the
feature-extraction
or
data-reduction
takes
place
in FILMAN.
Holding no very
fixed views
at present concerning the
relative
merits of alternative feature-extraction methods, we have endowed
FILMAN
with
the
capacity
to
apply
most
major
techniques
presently known or thought to be useful. These include in particu-
lar spectral analysis (Adey,
1965,
1970; Bendat and Piersol,
1971;
Dumermuth, 1973), period analysis (Klein, 1976a; Saltzberg, 1973),
and analysis of raw EEG amplitude distributions (Adey, 1970; Elul,
1969).
The pattern recognition part of the problem
is
itself open to a
vast variety of possible approaches, and may ultimately prove to be
an
important
area
of
investigation
in
its own
right
(e.g.,
see
Nilsson,
1965). For the present, however (and no doubt as a good
first-order approximation
to
still more elaborate techniques that
may come later), our statistical treatment of physiological data will
be carried out within the framework of the
multivariate general
linear model (Morrison,
1967; Timm,
1975).
In
brief,
this model
absorbs all the familiar univariate statistical designs into one over-
11 FILMAN uses a specially modified version of the FORTRAN overlay system,
in which
the load and execute
phases
are
separated.
In
this way
the
selected
routines
are loaded
into
specific
regions
of core
at
initialization time and then
executed in place repeatedly, rather than having to be reloaded for each record-set
in turn.
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163
arching theory, extends
it
to cases
in which there
are
multiple
dependent variables, and provides a unified computational frame-
work for
all cases. The multivariate extension is important in our
work because we typically use batteries of physiological measures
to attempt to discriminate the kinds of differences that interest us.
For example, continuing with our hypothetical ESP experiment,
kinds of questions
to be answered by
statistical
analysis would
include whether vector-valued observations such as EEG power
spectra, or coherence and phase relations between pairs of chan-
nels
at multiple frequencies,
differ systematically for hitting and
missing ESP responses.
With this as background, we now describe our statistical analysis
programs, which have to run stand-alone because of their size. We
have two main programs, each originally developed and published
by other workers. Each has been subjected to what we will call the
“basic modification”—namely, modification to allow input of stan-
dard
files, with channel and point
selection. The uniform
avail-
ability of channel and point selection mechanisms throughout the
data analysis system,
it should be noted, effects great economies in
analysis because the analysis of K channels
is carried out in paral-
lel,
as
it were,
rather than requiring the K-fold repetition of an
essentially identical analysis sequence. In addition we eliminate the
costs
in
processing
time and
storage
space
that would
arise
it
specially
tailored
files
containing just
the
right
data
points and
channels had to be created for each new application of the statisti-
cal analysis programs.
The first and simpler program is a multiple discriminant analyzer
(MDA) adapted from Overall and Klett (1972).
It performs a one-
way multivariate analysis of variance (MANOVA),
like the ordi-
nary one-way univariate ANOVA, but extended to multiple depen-
dent variables or criteria. We have expanded the maximum number
of criteria to 40 (in order to accommodate large parts of the EEG
frequency spectrum simultaneously), added a variety of optional
data transform operations (to improve the distributional properties
of extracted EEG features), and added significance tests on indi-
vidual
criteria
(to help
identify
sources of significance
in
multi-
variate
results). The
final change
is
an
optional “random
rep-
licates”
feature.
This
feature
permits
arbitrary
numbers
of
reanalyses of a single data-file, each time randomly permuting the
assignments of record-sets to groups rather than using their true
group
identities.
This mechanism can be used
to provide what
amounts
to
a
brute-force
non-parametric
test circumventing
all
distributional assumptions (approximates a randomization test). We
will also use
it to study the distributional properties of statistics
based on features such as EEG power spectral estimates.
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The heart of our data analysis system
is an adaptation of the
widely known MANOVA program
originally
written
by
Elliot
.
Cramer
in
1965, based principally on an algorithm published by
Bock (1963). MANOVA is an extremely general program for least-
squares analysis of linear models. Apart from size restrictions
it
can
analyze
any
analyzable
experimental
design,
univariate
or
-
multivariate, orthogonal or non-orthogonal, including
all the var-
ious
kinds
of unbalanced,
incomplete,
and
nested
designs.
In
analyzing factorial designs
it permits various kinds of special single
or multiple degree of freedom contrasts
in the main effects and
.
interactions.
It also permits optional transformations of criterion
variables,
as
in
our
version of MDA.
Finally,
it can perform
analysis of covariance, multiple regression, and canonical correla-
tion. In short,
it is a very powerful general-purpose instrument for
the analysis of experimental data.
It
is also reasonably well docu-
mented and for most applications quite easy to use.
The version we began with had already been adapted to run on
another small computer, the IBM 1130. In adapting
it to our pur-
poses we have made a number of major changes in program struc-
ture beyond those needed to make the “basic modification.” By
suitably restructuring the program to use storage more efficiently,
we have been able to implement a version which runs in approx-
imately 24K core and will handle up to 40-variable problems. As
indicated above, this expansion of the maximum number of criteria
is essential because
it allows us to analyze realistically large col-
lections of physiological measures simultaneously. In order to se-
cure
it we had to compromise on other characteristics of the im-
plementation, but our version will
still handle up to four factors
with a maximum of eight levels each, or a total of not more than 30
non-vacant cells. These specifications are quite sufficient to cover
our presently anticipated needs; in fact, in its most critical specifi-
cation (number
of
variables),
our
version
is
equivalent
to
that
implemented on our regional computer (IBM 370).
C.
Auxiliary File-Handling Routines
A
few
additional
utility
routines
for
manipulating
data
files
should be mentioned (see also Figure
2). SUMRY merely writes
file header information
to
the user terminal and
line
printer for
identification and checking. APPEND adds records from one stan-
dard file to those of another, conformable file (i.e., one sufficiently
similar
in
structure—having the same number of grouping
vari-
ables, same number of channels, same number and format of data-
points, etc.).
It can be used, for example, to add individual subject
or session
files
to a master
file.
Finally, FILGEN
is a program
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165
allowing creation of standard files from the user terminal. It can be
used to input
test data for new FILMAN programs, or to bring
small amounts of experimental data collected
in
other environ-
ments into the system for analysis. Larger amounts of non-physio-
logical data can also readily be brought into the system via special-
purpose programs (see, for example, Schouten and Kelly,
1978).
D. Programs for Calibration of the Physiological Measurement
System
Physiological signals such as the EEG are extremely small, on
the order of microvolts, and between our recording electrodes on
the
scalp
and
the
output
from
the MANOVA
program
there
stretches a long chain of instruments and processes, some of which
are individually very complex. The integrity of the basic measure-
ment system, from scalp electrodes to numbers inside the computer
is particularly
critical, and
it
is vulnerable to corruption at many
points along the way. Even with meticulous attention
to
detail,
technical problems
of various
kinds
occasionally
but
inevitably
occur. To reduce such problems to a minimum, we have invested
considerable
effort
in developing procedures
for
calibrating our
measurement system and studying
its properties. We
will
briefly
describe the main features of these procedures.
1. Daily calibration
routines. The measurement system should
establish
a fixed and known correspondence between
the mag-
nitude of physiological signals appearing at the inputs to the poly-
graph
preamplifiers
and
the
numbers
which
emerge from
the
A-to-D converter at the computer interface. This correspondence
defines
the
sensitivity
of
the
measurement
system. Many
psychophysiology laboratories simply use the manual and visual
calibration controls on the polygraph itself. Procedures of this sort
may be
sufficient
for
clinical purposes and
for
simple
kinds of
experimentation, but they certainly do not provide a
sufficiently
precise foundation
for work involving computer analysis of the
EEG (Clusin
et
al.,
1970; Walter,
1972).
In our system the daily calibration process
is pegged to a precise
external reference—namely, the DC calibration signals generated
by our H/P instrumentation recorder. After a preliminary manual
adjustment
of the polygraph
drivers,
this DC
signal
is
used
to
calibrate any one of the data acquisition channels. A program
is
then run which uses this channel to measure an adjustable 10 Hertz
signal source. This AC signal, now of precisely adjusted amplitude,
is
reduced
in
size by
a
passive
voltage
divider and
presented
simultaneously to the inputs of all polygraph channels. Each poly-
graph output
is attached in turn to the data acquisition channel
it
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