. s +” Approved For Release 2003/09/16 : CIA-RDP96-00788R002000260002-1 A Computer-Based Laboratory 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 t 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 if 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. Approved For Release 2003/09/16 : CIA-RDP96-00788R002000260002-1
t . Approved For Release 2003/09/16 : CIA-RDP96-00788R002000260002-1 . 164 Journal of the American S ociety for Psychical Research 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 _ Approved For Release 2003/09/16 : CIA-RDP96-00788R002000260002-1
ee TTT ccc Approved For Release 2003/09/16 : CIA-RDP96-00788R002000260002-1 * A Computer-Based Laboratory 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 Approved For Release 2003/09/16 : CIA-RDP96-00788R002000260002-1
Approved For Release 2003/09/16 : CIA-RDP96-00788R002000260002-1 166 Journal of the American Society for Psychical Research will use in the course of the experiment and this combination is adjusted under program control. Daily calibration is completed by running a program which measures all channels simultaneously, producing statistical averages for each channel’s offset and gain. The product of this calibration process is an updated version of a special disk file containing calibration data which are then read and displayed by whatever real-time experimental program is being executed. Offsets are invariably close to zero, and gains to unity. These values are used to correct received physiological! signals for the measured deviations from ideal calibration in the channels in which they appear. 2. System measurement routine. \deally, all channels of the measurement system should respond identically to identical input signals (assuming identical arrangement of the channel bandpass controls); that is, the frequency and phase response characteristics of all channels should be as nearly identical as possible. To the degree that this condition is not met, physiological effects may be obscured or confused with measurement system artifacts. Although the daily calibration procedure described above is greatly superior to simple visual methods, and effectively equalizes system response at the frequency of the calibration signal, it is far from guaranteeing homogeneity of response at other frequencies — within the EEG band. To cite an unfortunate example, Clusin et al. (1970) reported discovering massive and previously unsuspected interchannel differences when spectral analysis was performed on a single pre-recorded EEG signal passed simultaneously through all channels of their 16-channel electroencephalograph. Estimates of corresponding spectral magnitudes. differed by as “much as 44% across channels, and there were phase differences on the order of 45 degrees. Intending to avoid this kind of problem, we have set out to measure carefully our system response. Our technique, detailed in Lenz and Kelly (1980), uses trains of specially designed test signals consisting of computer-generated impulses of modified sin x/x form, with known spectra. These test signals are sent from com- puter to lab, passed through the relevant polygraph channels, and sent back up to the computer for recovery, averaging, and spectral analysis of the average recovered impulse responses. The fre- quency and phase response functions of all channels are thus pre- cisely measured, and can be compared with each other, with poly- graph technical specifications, and with theoretical expectation. Applications of this procedure to our six AC-coupled EEG chan- nels at several bandpass and sensitivity configurations have yielded excellent results showing that the system functions of these chan- nels are highly homogeneous. Consequently, equation of system Approved For Release 2003/09/16 : CIA-RDP96-00788R002000260002-1 ca ae uf